USE OF SUBPIXEL CLASSIFIER FOR WETLAND MAPPING

A case study of the Cuitzeo Lake, Mexico

Rodrigo Sagardia February, 2005

USE OF SUBPIXEL CLASSIFIER FOR WETLAND

MAPPING

A case study of the Cuitzeo Lake, Mexico

by

Rodrigo Sagardia February 2005

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfillment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation (Planning and coordination in Natural Resources Management)

Degree Assessment Board Chairman Prof. Dr. W.H. van den Toorn External Examiner Dr. G. Epema Internal Examiner Dr. Ir. W. Bijker First Supervisor Dr. C.G. Atzberger Second Supervisor Dr. D. van der Zee

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

I certify that although I may have conferred with others in preparing for this assignment, and drawn upon a range of sources cited in this work, the content of this thesis report is my original work. Signed ……………………. Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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ACKNOWLEDGEMENTS

I would like to thank the people from the National Autonomous University of Mexico, UNAM, for all the support they gave our group before and during fieldwork, providing us with knowledge, equipment and facilities. Especially I would like to mention Dr. Alejandro Velázquez, Manuel Mendoza, Elvira Durán, the secretaries Liliana and “Lupita”, and all the people who made that time an enjoyable experience. I also would like to thank the University of Michoacán for lending me equipment to take my measurements and to the French ISIS programme for Incentive for the Scientific use of Images from SPOT system, and CNES, for providing me with the necessary SPOT image to perform this work. My gratitude goes to Gerard Reinink and Boudewijn van Leeuwen, for their help and always good disposition in solving remote sensing and image processing issues. With affection I want to mention Ulfrano, our Mexican fieldwork guide, and his family. Without his help and knowledge of the lake none of this work would have been possible. Special thoughts are dedicated to my supervisors for their comments and orientation and to my advisor Dr. Zoltan Vekerdy, with whom I went to fieldwork, and offered me not only guidance and advice but hospitality. Finally I want to express my gratitude to the ones that were my classmates at ITC and now I can call friends. The time shared with them means much to me.

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TABLE OF CONTENTS

1. INTRODUCTION .................................................................... 1

1.1 WETLANDS, THEIR IMPALACE AND NEED.............................................1 1.2 RATIONALE .............................................................................3 1.3 SUBPIXEL CLASSIFICATION AS PROMISING TOOL FOR WETLAND STUDIES......4

2. RESEARCH PROBLEM............................................................. 7

3. RESEARCH OBJECTIVES......................................................... 7

4. RESEARCH DESIGN................................................................ 8

4.1 STUDY AREA ...........................................................................8 4.2.2 Cuitzeo Lake. Location and Size .........................................8 4.2.3 Wetland Type..................................................................9 4.2.4 Climate and Topography...................................................9 4.2.5 Land Cover .....................................................................9 4.2.6 Environmental Problems .................................................10 4.2.7 Biological Diversity.........................................................10 4.2.8 Vegetation....................................................................11 4.2.9 Functions and Values .....................................................12

4.2 METHODS AND MATERIALS .........................................................14 4.2.1 Data Availability and Data Collection ................................15 4.2.2 Image Pre-Processing.....................................................17 4.2.2.1 Georeferencing ...........................................................19 4.2.2.2 Atmospheric Correction................................................19 4.2.2.3 Masking.....................................................................20 4.2.2.4 Image Scaling ............................................................20 4.2.3 Image Processing ..........................................................20 4.2.3.1 Supervised Classification..............................................21 4.2.3.2 Unsupervised Classification ..........................................22 4.2.3.3 Soft Classification .......................................................23

5. RESULTS.............................................................................. 25

5.1 CLASS SPECTRAL CHARACTERISTICS ..............................................25 5.2 HARD CLASSIFICATION .............................................................27

5.2.1 Unsupervised Classification .............................................27 5.2.2 Supervised Classification ................................................29

5.3 LINEAR SPECTRAL UNMIXING.......................................................31 5.3.1 SPOT ...........................................................................31 5.3.1.1 Preliminary Visual Analysis ...........................................31 5.3.1.2 Presence-Absence Analysis and Accuracy Assessment ......33 5.3.1.3 Material Pixel Fraction Accuracy Assessment ...................34 5.3.2 MODIS .........................................................................36 5.3.2.1 Preliminary Visual Analysis ...........................................36

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5.3.2.2 Presence-Absence Analysis and Accuracy Assessment ......38 5.3.2.3 Material Pixel Fraction Accuracy Assessment ...................39

6. DISCUSSION ....................................................................... 41

6.1 ON CLASSIFICATION ACCURACY ...................................................41 6.2 LINEAR SPECTRAL UNMIXING.......................................................41 6.2.1 ENDMEMBER DETECTION .........................................................41 6.2.2 MPF ESTIMATION.................................................................42 6.2.3 HARD VS SOFT CLASSIFICATION ................................................43 6.2.4 PENDING TASKS ..................................................................44

7. CONCLUSIONS AND RECOMMENDATIONS ........................... 45

BIBLIOGRAPHIC REFERENCES..................................................... 46

APPENDICES.................................................................................. 51

APPENDIX 1. MODIS 09 ATMOSPHERIC CORRECTION PROCESSING THREAD FLOW CHART [57]..................................................................................52 APPENDIX 2. CONFUSION MATRICES FOR HARD CLASSIFICATIONS ....................53 APPENDIX 3. MATERIAL PIXEL FRACTION OUTPUT MAPS. MODIS BANDS 2 1 4 6 .54 APPENDIX 4. ENDMEMBER PRESENCE-ABSENCE MAPS .................................55 APPENDIX 5. MATERIAL PIXEL FRACTIONS DIFFERENCE MAPS .........................59 REFERENCE DATA VS PREDICTED DATA. ..................................................59 APPENDIX 6. MODIS FALSE COLOUR COMPOSITES.....................................62 APPENDIX 7. ORIGINAL VS RECONSTRUCTED MODIS DIFFERENCE HISTOGRAMS ..63 APPENDIX 8. EFFECT OF PIXEL SIZE ON CLASSIFICATION ..............................65

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LIST OF FIGURES

Figure 1. Four Cases of Mixed Pixels (adapted from Fisher [10]) .............5 Figure 2. Study Area Location Map .....................................................8 Figure 3. Livestock feeding on Aquatic Vegetation...............................12 Figure 4. Research Approach ...........................................................14 Figure 5. Image Processing Workflow................................................18 Figure 6. Class Mean Spectra ...........................................................22 Figure 7. Class Mean spectra. SPOT ..................................................25 Figure 8. Endmember Mean spectra. MODIS ......................................27 Figure 9. Unsupervised Classification Output Map ...............................28 Figure 10. Supervised Classification Output Map.................................30 Figure 11. Material Pixel Fractions. SPOT ..........................................31 Figure 12. Overall RMSE. SPOT ........................................................32 Figure 13. Overall RMSE. MODIS Bands 2146.....................................33 Figure 14. Material Pixel Fraction Difference Maps. SPOT .....................35 Figure 15. Material Pixel Fractions. MODIS.........................................37 Figure 16. Overall RMSE. MODIS ......................................................38 Figure 17. Material Pixel Fraction Difference Maps. MODIS ...................40

LIST OF TABLES

Available Secondary Information ......................................................15 Jeffries-Matusita Distance for Class Pairs ...........................................21 Class Spectral Characteristics. SPOT .................................................26 Endmembers Spectral Characteristics. MODIS ....................................26 Unsupervised Classification Accuracies ..............................................28 Supervised Classification Accuracies .................................................29 Classified Areas per Class................................................................30 Accuracy Assesment. SPOT..............................................................33 Class Difference. Reference Data vs Predicted Data. SPOT ...................34 Class Difference. Reference Data vs Predicted Data. MODIS Bands 2146 36 Accuracy Assessment. MODIS..........................................................38 Class Difference. Reference Data vs Predicted Data. MODIS .................39

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LIST OF ABBREVIATIONS AND ACRONYMS

GCPs – Ground Control Points GLCF – Global Land Cover Facility INEGI – National Institute of Statistics, Geography and Computing. Mexico ISIS – Incentive for the Scientific use of Images from SPOT System Programme ISODATA – Iterative Self Organizing Data Analysis Technique LSU – Linear Spectral Unmixing LUT – Look up Table ML – Maximum Likelihood MOI – Material of Interest MPF – Material Pixel Fraction ppm – parts per million UMICH – University of Michoacán. Mexico UNAM – National Autonomous University of Mexico

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1. INTRODUCTION 1.1 Wetlands, their impalace and need Wetlands constitute diverse ecosystems, holding a high variety and richness of species, usually covering extensive areas. They are briefly defined as “areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six metres”, that “may incorporate riparian and coastal zones adjacent to the wetlands, and islands or bodies of marine water deeper than six metres at low tide lying within the wetlands”. [43] The importance of these areas has been recognized by the scientific community and organizations for their provision of key functions and services, such as biodiversity support [31], flood control [30, 65], water purification and storage [35, 33], and sediment stabilization [8, 33]. However, their real value remains underestimated by society at large [43], though efforts have been made to solve this by providing more quantitative approaches to their valuation [30, 62]. Commonly, services provided by wetlands are insufficiently acknowledged and therefore can not compete with other land uses, especially when there is a high population pressure for human development [30, 23]. This has led to degradation of their functions, sometimes with complete loss of the areas [65]. Major efforts on wetland restoration have been done in the Midwestern portion of the United States [58], and Europe. On tropical wetlands, Australia is one of the leading parties [23]. However, initiatives of protection or restoration are scarce in less developed regions. In 1971 the “Convention on Wetlands of International Importance especially as Waterfowl Habitat”, also know as the Ramsar Convention, was adopted. From then, the convention has broadened its scope of action, extending its efforts of conservation and sustainable use to almost every role and function supported by these areas. At present, the Ramsar Convention has 138 contracting parties, with more than 1370 registered wetlands covering 120 million hectares worldwide [43]. Currently the intention of the Convention on Wetlands is to promote the rational use of the wetland resources through communication, education and public awareness [40], as well as actively promoting the participation of the local communities in the management of those resources [39]. In this sense, mapping wetlands for inventorying and monitoring is seen as an important elements to achieve, as Ramsar says, the “wise use” of these regions [43, 41]. The data derived from inventories serve to the creation of knowledge on these areas, helps to understand their dynamic, which in turn can be used to direct initiatives on research, policy development, management, and in general can be incorporated in decision making processes.

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The role of remote sensing in wetland mapping has been profusely documented, and though it has been employed successfully in the past, there are still fields that need improvement; one of these being the spatial detail in the representation of the features that compose these ecosystems [34] and another being its periodical update. In that regard Harvey [19], citing other authors, says that, “wetlands are often spatially characterized by steep ecological gradients with vegetation units narrower than the pixel size of current sensors”, and where these vegetation types are often spectrally similar between them. The difficulty of finding high resolution sources that are both, accessible and economic, limits their use to particular projects and not for monitoring large areas on a regular basis, while affordable sources of lower spatial resolution do not live up to the task. One way to overcome in part the lack of more detailed spatial information is the use of subpixel classifiers, that do not add more spatial detail to the representations, but that are able to improve the knowledge over an area by telling us what is in a pixel [34]. The study area selected for this research, Cuitzeo Lake, is a wetland located in the central portion of Mexico. The characteristics and problems of the area provide a good opportunity to investigate the use of subpixel classification techniques. Mexico has 51 sites designated as Wetlands of International Importance, covering more than 5 million hectares.” [38]. Cuitzeo Lake has been identified as one of the potential places that could still be incorporated in the Ramsar Convention [36]. The lake, a very shallow and fragile ecosystem has suffered drastic changes in past years; from eutrophication of its waters to periodical droughts [52]. Its incorporation into the Ramsar Convention, as well as the development of some of its services, like ecotourism, could be some ways to improve its conservation status. Subpixel classification of the area could be useful to define suitability of nesting places for birds [31], delimit their feeding grounds and determine carrying capacity. It could also help in detecting trends in vegetation distribution that could evidence disturbance from various sources [49]. Such information could be used in addition to the existing one to preserve the natural dynamic of the lake, aimed as input to planning processes according to guidelines proposed by the Ramsar Convention.

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1.2 Rationale

Roggeri [48] synthesises many of the principles necessary to achieve a sustainable management of wetlands. He mentions the need to maintain their essential values and functions preserve their multi-functionality while taking into account the interrelation between them and other ecosystems. Furthermore, he mentions the need to involve rural wetland-dependent communities in these initiatives while integrating conservation and development at the same time. These guiding principles agree with what the Ramsar convention calls wise use of wetlands and defines as “their sustainable utilization for the benefit of mankind in a way compatible with the maintenance of the natural properties of the ecosystem” [43]. In addition in its strategic plan the Convention considers the need to maintain a list of wetlands of international importance and the necessity of international cooperation for enforcing the consolidation of functional wetland networks. However to reach these objectives it is first necessary to quantify and give value to resources that, are difficult to rate because the assignation of value implies a deep understanding of the resources, attributes and functions of a wetland and the processes that take place within it [62, 4]; especially because these are sometimes of a fast changing nature. The role of remote sensing in the context of wetland management is related to its ability to inventory and monitor large areas [34], fundamental elements to understand wetland changes, either natural, introduced by external factors, or by management strategies at local level. It does so, while keeping costs low and allowing a more or less frequent update of the information base. This in turn is part of the first step in developing an action plan for an area: the building of the information base, including inventory and preliminary evaluation of benefits. After this, a preliminary situation and problem analysis can take place, identifying action priorities, which are followed by the development of the action plan itself, that finally has to be implemented and evaluated [48]. In helping to build such a knowledge base, new sensors such as MODIS TERRA and AQUA appear to be of particular interest for monitoring, given the low cost of their images, their global coverage and high revisit time, which could allow for less frequent inventories derived from other high resolution sources and make fast response to unusual events more likely as it has proven in the case of fire detection [17, 24]. Nevertheless, given the spatial limitations inherent to these sensors, it is desirable to evaluate the possibility of extracting more information from available data. This is where techniques like subpixel classification come in.

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Summarizing, the justification to carry out this study is:

- the need of more, practical and affordable techniques and methods for wetland mapping that can complement existing ones to improve the application of remote sensors in the distinction of vegetation types; as well as the urge to take advantage of new, widely available, low cost remote sensing sources of information, to strengthen the capability to inventory and monitor wetlands, as input for the decision making process and their wise management

- the request to contribute to the status assessment of Mexican wetlands

not yet designated as Ramsar sites and in particular the request to add to the existing information of the Cuitzeo basin, to make a knowledge base suitable to be used in planning and development of the region

1.3 Subpixel Classification as Promising Tool for Wetland Studies Until now the common remote sensing techniques for wetland identification and mapping have included the rather extended use of radar imagery [3, 6, 53, 44] and multispectral imagery [3, 32]. A good review of techniques was made by Ozesmi [34]. The reason behind the profuse use of radar techniques is due to the fact that it overcomes some of the limitations in detecting understory water bodies, thus delimiting to a better extent the wetland presence under canopies [6]. It also overcomes the problem of clouds that is usually present in optical imagery [53, 9], especially in tropical regions [25, 9]. It is however usual to combine the use of different types of imagery and classification techniques, as they prove to be the more effective in wetland mapping [3, 34] , and to use approaches that includes some sort of rule based classification using ancillary data [34, 21]. Nevertheless the main limitation that affects most remote sensors, is their spatial resolution [54]. This is a matter influenced by two elements, the current status of sensor technology and the limitations imposed by physics. When recording remotely sensed data, the size of the instantaneous field of view has important implications: A small field of view allows the recording of fine details at the expense of less sensitive radiance measurements, as is the case of sensor like SPOT or IKONOS. The opposite being true when using a large field of view, as more energy is focused on the detectors, achieving higher signal levels, as can be seen in sensors like MODIS or NOAA AVHRR [28]. From this it becomes evident that there is always a trade-off to make when dealing with remotely sensed imagery, being a practical impossibility to count with all the advantages. That is why new techniques emerged to extract more information from an image. One approach being the merging of high spatial

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resolution sources with other images of lower spatial but higher spectral resolution [28]; a second method being the extraction of information at subpixel level, this last one particularly important as hyperspectral sensors begin to appear. Subpixel classifiers also know as soft classifiers, deal with the mixed pixel problem. Mixed pixels are normally found in boundaries between two or more mapping units, along gradients, such as ecotones, or when the occurrence of any linear or small subpixel object takes place [10](Figure 1). The most promising methods of wetland classification involve the use of different subpixel techniques (fuzzy classification, spectral mixture/unmixing analysis and mixtures estimation ) to allow detailed mapping [34].

Figure 1. Four Cases of Mixed Pixels (adapted from Fisher [10]) The subpixel classification relies on the multispectral characteristics of the sensor. Harvey and Hill [19] while mapping wetlands in Australia performed a comparison between aerial photographs, Landsat TM and SPOT XS. Though the results showed that aerial photographs were clearly superior to satellite images it was noted that LANDSAT offered a good performance in spite of its spatial resolution. They attributed this situation to the number of spectral bands that tended to compensate adverse effects of the lower spatial resolution.

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This complementary effect, seen when counting with many spectral bands, is one of the reasons why it is possible to extract more information from an image at the subpixel level [1], as also was found by Schmidt and Skidmore [51] who used a hyperspectral scanner at plot level to separate the spectral signature of wetland vegetation species in the Dutch Wadden Sea with a great degree of success. The subpixel classifier in the case of this study uses the linear unmixing technique, allowing to identify a “material of interest” and determine its “material part fraction” or cover percentage, within a pixel. That makes it suited for identification of specific materials within complex scenes. However, it is not designed to classify spectrally heterogeneous entities, something that hard classifiers commonly do [1]. The theory behind it is the contribution of a series of endmembers present within a pixel to its spectral signature. So the spectral signature of a pixel would be derived from the sum of the products of the single spectrum of the endmembers it contains, each weighted by a fraction plus a residue [7, 63, 47]. This can be expressed for three endmembers as follows:

Eλ = A * Eaλ + B * Ebλ + C * Ecλ Where

Eλ - spectral signature of the pixel Eaλ - spectral signature of endmember a Ebλ - spectral signature of endmember b Ecλ - spectral signature of endmember c A - subpixel fraction of endmember a (%) B - subpixel fraction of endmember b (%) C - subpixel fraction of endmember c (%)

One drawback of the method is that it considers a linear relationship between the elements that compose the spectral signature of a pixel. This shortcoming can be overcome, by using, for example, artificial neural networks, but that approach has important issues to consider, namely the requirement of an extensive training sample and the proper understanding of its internal functioning [12]. Therefore it was found to be not suitable for this study. Other limiting factor of the linear spectral unmixing approach are that, for the classification to work, the number of bands present in an image must be at least equal to the number of classes minus one to give a proper estimation of error and all present endmembers have to be defined.

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2. RESEARCH PROBLEM Current methods of wetland classification that use remotely sensed imagery pose two main problems; either they lack enough spatial resolution for adequate wetland mapping or, if they meet these requirements, result in high expenses, usually disproportionate to the monetary value currently given to the areas. Thus, there is a need for affordable and practical ways to extract more information from existing low cost imagery, one of these being image subpixel classification. 3. RESEARCH OBJECTIVES

The main objective of this study is to determine the utility of subpixel classifiers in extracting information on vegetation cover from low spatial resolution images, as a contribution to the improvement of inventorying and monitoring schemes of wetlands.

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• Is the subpixel classifer able to tell apart patches of dominant species present in the wetland with sufficient degree of accuracy?

• How accurately is it possible to distinguish the proportion of dominant species present within a pixel?

• Which additional ancillary data would be necessary to obtain more accurate results?

• What would be the requirements needed to export signatures obtained from subpixel classification to other images registered in the same phenological stage of the vegetation, but off different years?

OObbjjeeccttiivvee 22 : evaluate the differences in accuracy obtained from using a subpixel classifier against traditional hard classifiers

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OObbjjeeccttiivvee 33 : represent and evaluate the impact of subpixel classification on artificial spatially degraded images

• What is the difference in performance of subpixel classifications when using an artificially degraded image?

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4. RESEARCH DESIGN 4.1 Study Area

4.2.2 Cuitzeo Lake. Location and Size Lake Cuitzeo is located in the state of Michoacan, to the centre of Mexico (Figure 2). Its endorheic basin covers 3.675 km2, between 19°20’ to 20°15’ northern latitude and 100°35’ to 101°30’ western longitude.

Figure 2. Study Area Location Map

Its natural boundaries are given to the north by the basin of Yuriria Lagoon, to the south by the hydrologic region 18 Balsas, to the east by the Rio Lerma 3 and Rio Lerma 4 basins, and to the west by the basins of Patzcuaro lake and Angulo river. The main water inlet in the area is the Rio Grande river of Morelia, formed by the union of the rivers Tiripetio and Tirio. The Rio Grande flows in northeastern direction to its discharge point in Cuitzeo lake [37]. The Cuitzeo lake basin itself occupies the central part of the greater Rio Lerma basin [52], one of the most important basins in Mexico. The lake, second largest in Mexico [29] is positioned at an elevation of 1820 m.a.s.l, covering a maximum surface of 420 km2. Very shallow in nature, it

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suffers major fluctuations in surface and volume due to climatic variations and the alteration of the water regime by human activities [52], mainly water diversion and storage for agricultural use [52, 37]. The western and middle portions of the lake were selected as study area, since these are the regions that hold most of the vegetation present. The eastern part was discarded, since the presence of higrophilous vegetation in it was negligible.

4.2.3 Wetland Type According to the classification system for wetlands proposed by the Ramsar Convention [42], Cuitzeo Lake is an inland wetland of type O, namely, a permanent freshwater lake whose extent surpasses 8 hectares. This classification agrees with the general situation found in the area for describing the main wetland type. At a localized level the situations to be found are usually much richer in terms of environmental variability, though. In addition to the characteristics already mentioned it is usual to find swampy areas, with shallow, stagnant water, or other areas that lack of water cover for long periods of time, in part due to anthropic intervention.

4.2.4 Climate and Topography The climate in Lake Cuitzeo is of subtropical type. It is characterized by a wet season between June and September and a dry season from October through May [52]. Annual precipitation is less than 1.000 mm, with a mean temperature of 15°C [29]. Temperatures are mild with extremes that may go from 35°–40°C to 0°–10°C [52]. The basin is characterized by hills, small ridges and plains. Its soils of volcanic origin are mostly fine textured [29].

4.2.5 Land Cover The main land cover types found in the basin correspond to bushes, forests and agricultural crops, like sorghum and maize [29]. From 1975 to 2000 the area of temporal crops in less productive soils has been reduced and replaced by bushes. However, in the same period, the number of small dams present in the region has increased from 1.000 to 2.500, with the purpose of providing water to livestock and irrigation of agricultural fields. Another important increment took place in urban settlement areas, which increased their extent from 3.000 ha on 1975 to 11.000 ha in 2000. This change

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has not been proportional with population growth, which in fact dropped its density in the same period [29].

4.2.6 Environmental Problems One of the major problems that affect the basin is the fact that the untreated waste of Morelia, the largest city around, drains into the agricultural fields of the southern plane of the lake, ultimately reaching it with an additional load of fertilizers. Other agricultural areas in the region contribute to this last effect as well [29, 52]. This has led to an increase in aquatic vegetation that went from 5% of the lake surface in 1975 to 15% in 2000 [29], posing problems to fishing activities and expedite transport in the area, forcing in turn the regular maintenance of waterways by the authorities and local population. Another important issue in the region is the reduction of the lake surface, not only during periodical droughts, but apparently due to less water inlet from its tributaries [49, 52, 29], as a result of water being used for urban consumption and irrigation of agricultural crops [49, 29]. This constitutes one of the limiting factors for the growth of aquatic vegetation in the western and center portion of the lake [49]. The combination of these factors, is presumably resulting in hyper-eutrophication, that may already have caused the disappearance of five fish species from the lake, two of them endemic [52]. In addition to the problems already described, the construction of a new federal road crossing the eastern portion of the lake is threatening to aggravate the situation in the area, as it could further obstruct the free flow of water, besides constituting a major alteration of the landscape.

4.2.7 Biological Diversity The biological importance of Cuitzeo Lake is given by its unique characteristics. Not only does it house 19 native fish species, some of them endemic or of restricted distribution [52], but it also constitutes an important stop for migratory birds [36]. Long time have wetlands been recognized for their role in providing food, shelter and protection from human interference to migratory birds [48], being one of the original reasons for the adoption of the Ramsar Convention. In this aspect, the importance of Mexican wetlands in the life cycle of migratory birds, particularly waterfowl, has already been mentioned by Wilson and Ryan [59].

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In the particular case of Cuitzeo, it supports an important population of American White Pelican (Pelecanus erythrorhynchus), Mexican Duck (Anas diazi) and Canvasback, with an average waterfowl count of over 77.000 birds per season, also having one endemic bird species, the Blackpolled Yellowthroat Warbler (Geothlypis speciosa), which is only to be found in three other places. This makes Cuitzeo, along with other eleven wetlands, one of the most important undesignated sites in Mexico that qualifies under the Ramsar Convention as wetland of international importance [36]. However, the biological richness of the place is not limited or circumscribed to the fauna present in the area, as its flora is abundant, counting with 92 vegetal species from forty different families [49].

4.2.8 Vegetation The hygrophilous vegetation on Cuitzeo Lake is represented by a few vegetal communities, known locally as Tulares and Carrizales, as well as rooted submerged plants and free floating plants. Generally speaking, the Tulares are communities of herbaceous plants with long narrow leaves 1 to 3 meters in height, rooted in the bottom of shallow water sites. They can be found on temperate to warm climates on the shores of lakes, lagoons, or swampy areas. The most common species are from the genus Typha spp., Scirpus spp. and Cyperus spp. [50, 22]. The Carrizales share the common characteristics of the previously described community, but their representative species are Phragmites communis and Arundo donax [22]. Both community types are cosmopolite in nature and can be found on places of sweet or salty water [50]. According to Rojas [49], the hygrophilous dominant species present in Cuitzeo Lake are represented by species of the genus Typha, Scirpus, Eleocharis and Phragmites, in the case of rooted emergents, and Potamogeton pectinatus, in the case of rooted submerged plants, the last species covering more than half of the eastern portion of the lake. It was noted that, since Rojas publication of 1995, the free floating species Eichornia crassipes, commonly known as Water Hyacinth, changed its status in the last ten year period to become one of the dominant species. The distribution of this free floating plant changed as well, being nowadays widespread through most of the western part of the lake. As written in the book “Vegetación de México” by Rzedowski [50], Eichornia crassipes is an aggressive species, able to survive in ample climatic regimes and capable of covering vast extensions rapidly. This is particularly true when anthropic influence has altered the environment [50, 48].

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4.2.9 Functions and Values When addressing wetlands and their value, it is possible to differentiate between resources attributes and functions [48], which can have direct or indirect use values or non use value when we talk about existence value [4]. Many goods and services are produced in Cuitzeo. The most important is perhaps the water supply [29] for population and crops. Other resources, such as natural products derived from reeds [50, 49] and fisheries, have lesser economic importance, but their attribute of being part of the cultural heritage, even from pre-columbian times([5, 18] in [52]), must not be underestimated. The use of wetland vegetation as forage resource is also present, but not of wide use [49](Figure 3).

Figure 3. Livestock feeding on Aquatic Vegetation As mentioned earlier, the biological diversity of the area as well as it uniqueness within Mexico are important attributes. These attributes only have value for their existence, so it is not a trivial task to measure their importance [4].

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Important functions provided by Cuitzeo are its role in the life cycle of migratory birds as mentioned by Perez-Arteaga [36], nutrient retention and water treatment. It is common that these resources, attributes and functions along with their values fluctuate through time, frequently changing one at the expense of the other, to the extreme of being mutually exclusive in case of intensive use [4]. A good example of this in Cuitzeo could be the explosive expansion of Eichornia crassipes, that is know to be one of the most efficient plants for water treatment, as it contributes to particle sedimentation, reduces the number of coliform bacteria and removes high quantities of nutrients and heavy metals [48]. The value of the service provided by this plant could have increased in the previous years; however it is affecting the transport and fishing on the lake.

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4.2 Methods and Materials

This section and its following sub-sections introduce the methodological approach that was used during the study as well as the global research process which is shown in Figure 1.

Figure 4. Research Approach

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4.2.1 Data Availability and Data Collection

Prior to fieldwork a literature review took place, accumulating pertinent information that served to construct the conceptual framework used throughout the research. Other sources of secondary information like maps, images and tabulated data were collected, extracting their relevant portions (Table 1).

Table 1. Available Secondary Information

IINNFFOORRMMAATTIIOONN SSOOUURRCCEE//DDAATTEE CCHHAARRAACCTTEERRIISSTTIICCSS 1. Spatial data • CUITZEO BASIN 1 • LANDSAT ETM 1 • LANDSAT ETM 2 • MODIS TERRA MOD09 1 • MODIS TERRA MOD09 2 • SPOT 5 • TOPOGRAPHIC MAP CUITZEO • TOPOGRAPHIC MAP MOROLEON • TOPOGRAPHIC MAP ZINAPECUARO

UNAM

GLCF

GLCF

EOS

EOS

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1:37.000 Panchromatic photographs 30 m. Path 27, Row 46. 26 Nov 2001. 30 m. Path 27, Row 46. 08 May 2003. 500 m. Surface reflectance. 15 Oct 2004. Centre Point: N25.15° W105.07° 500 m. Surface reflectance. 15 Oct 2004. Centre Point: N15.06° W098.56° 10 m multispectral. 18 Oct 2004 Centre point: N20°0'58" W100°54'54" 1:50.000. Ellipsoid GRS80. Datum ITRF92. UTM Projection 1:50.000. Ellipsoid GRS80. Datum ITRF92. UTM Projection 1:50.000. Ellipsoid GRS80. Datum ITRF92. UTM Projection

By using old LANDSAT ETM+ images from 2003, 2000 and expert knowledge on the area, a preliminary sampling layout was set, which was complemented by an unsupervised classification of the regions of interest, as suggested by Harvey and Hill [19] for mapping wetland environments. The purpose was to identify major elements and their distribution as to serve as guide to improve the sampling scheme that consisted of a stratified simple random sample approach [27]. The intention was to fine-tune the sampling scheme and its stratification on the field, after a quick survey of the area. However this preliminary survey revealed that the selected sampling strategy was impossible to implement due to accessibility restrictions imposed by an unusual rise in the lake’s water level at the time of fieldwork. It was however noted from the survey, that there was good visual correspondence between features identified on the 2003 and 2000 satellite images and the vegetation present. This information was used to select the dominant species to be sampled in concordance with the information published by Rojas [49] on the area, the only exception being the inclusion of Eichornia Crassipes to the sampled species. So the study focused on four types of dominant plants: Typha dominguensis, Scirpus spp., Potamogeton pectinatus and Eichornia crassipes. Other types of vegetation were discarded as their coverage was too small.

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The aforementioned situation of accessibility forced to sample areas that were mainly accessible by boat, i.e. border areas between vegetation and water in the case of Typha dominguensis and Eichornia crassipes, and a few shoreline areas possible to cover by foot. The recorded data on each sample point comprised information on location, species composition, phenological status, height, health and coverage, this last was visually estimated. For many points the location was recorded using a common 12 channel tracking GPS handheld receiver, taking care to measure areas with big enough safe margins, considering the expected horizontal positioning accuracy of the device, which lies between 10 and 15 m [15]. For points not directly measured, vector information readings from a compass were also included. In addition, other measurements were made using a Trimble Pathfinder Pro XL receiver with a TDC1 data logger with Asset Surveyor Software, capable of decimetre accuracy. This equipment was used mainly to record linear and area features. Mostly limits between vegetation and water or between two types of vegetation were recorded in the first case, and isolated vegetation patches in the second case. This GPS receiver, capable to achieve sub meter accuracy with only one measure, was operated in carrier phase mode in order to make a differential correction of the points afterwards. For that purpose a logging interval of 15 seconds was set, synchronized with the base station of “Aguas Calientes”, part of INEGI’s stationary geodetic network covering most of the country. Other configurations of the receiver were set according to the manufacturer recommendation, with a minimum carrier time of 10 minutes, recording from a minimum of 4 simultaneous satellites, an elevation mask of 15 degrees and a signal to noise ratio equal or over 6 [56]. A PDOP mask of 4 was used, instead of the recommended 6, to ensure a more than adequate geometry of the satellites. Averaging of measurements was not utilized as it was deemed to be unnecessary and the difficulty of staying stationary on the lake made it unpractical. The expected error of the GPS under the conditions used was expected to be 70 cm RMS error for null distance from the base station. To this an additional error, due to deterioration in accuracy according to the distance to the base station, had to be added. This error is in the order of 5 ppm when using carrier phase, meaning that, for every 10 kilometres of distance between the rover and the base station 5 cm of error had to be added [55]. Since the base station was around 200 km away, the expected horizontal positional error was 110 cm. The actual estimated average error of the recorded data after differential correction for a 95 % confidence interval was 1.29 m, with a standard deviation of 0.19 m.

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4.2.2 Image Pre-Processing Two different kinds of image were used for all the processes leading to image classification: a pair of MODIS TERRA MOD09 surface reflectance products and a multispectral SPOT5, as the high spatial resolution reference counterpart. The choice for SPOT imagery was based on availability, as images of the area for the same period of time were not obtainable from other high resolution sensors. The requirement of close dates for both datasets was meant to eliminate differences in vegetation development, phenological status and displacement, in the case of floating plants. The image processing workflow covering all the general phases performed throughout the study is depicted on Figure 5.

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Figure 5. Image Processing Workflow

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4.2.2.1 Georeferencing As a first step in image processing, proper georeferencing of the SPOT5 image was made by using ground control points properly placed around the study area with the help of old satellite images and topographic maps and measured with the Pathfinder Pro XL GPS. These control points complied with an attribute set [7] to ensure their quality. From 33 points considered, 19 were used. The remaining points were discarded for various reasons. A few were outside of the coverage of the satellite image, others were obscured by clouds or the image features were too coarse to allow for a proper positioning on the image. Finally, after revision and correction of the remaining ones, a few were discarded as their contribution to the overall error was deemed excessive. The overall RMS error achieved in the georeference of the SPOT image was 0.52 m, with an error of 0.34 m in the X axis and 0.39 m in the Y axis. In the case of the MODIS images, georeferencing was not necessary and the metadata accompanying the original image was used instead. The original expected geolocation accuracy of the satellite was of 150 m, but nowadays, after several updates which involved the use of parametric and non parametric (GCPs, DEM) methods to eliminate bias and other sources of error, the accuracy of higher level products is of 50 m [60] and according to MODIS web sources it could even fall between around 40 m [61]. Additional processes done with the MODIS images were the confection of a mosaic of them, as the whole lake was not covered fully in one image, and the subset of the resulting image and its projection into UTM.

4.2.2.2 Atmospheric Correction The available SPOT5 image was corrected atmospherically to approximate at ground reflectance values. For this purpose the ATCOR2 software was used, which is based on the MODTRAN4 radiative transfer code. ATCOR2 does the atmospheric correction by inverting the results obtained from MODTRAN, which are stored in a Look up Table. The overall accuracy obtained using proper inputs is expected to be 88 to 93 percent for at sensor radiance [16]. The adjustment of the parameters on ATCOTR was done in an interactive, iterative process, where the spectra and trends from a sample of known pixels were compared to spectral libraries, until considered satisfactory. The idea behind the atmospheric correction is to be able to extract spectral signatures of the different MOI’s present in a scene, discarding atmospheric and illumination effects, and opening the possibility to use these signatures on other scenes by comparing them with previously compiled spectra libraries.

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However, for in scene classification there is no gain when converting radiance to reflectance, as the classification is not affected by the atmosphere or the solar curve [20]. For MODIS atmospheric correction was not necessary since the images already have the necessary corrections applied to them. MODIS uses different other MODIS products and parameters as input as well as look up tables to perform these corrections[57]. Please refer to Appendix 1 for details on the correction process.

4.2.2.3 Masking To leave only lake areas to be processed during the classification phase, a mask was composed. The composition was made based on visual interpretation at a 1:10000 scale. This way, the whole lake area was delineated, leaving outside the mask existent islands, clouds and other man made features. As some areas were flooded in an exceptional way, topographic maps along with older LANDSAT images were used to define the limits of the mask. It was not practical to use a more automatized method to generate the mask as the high density of the vegetation led to errors on many places. The availability of a thermal IR band, besides the NIR band in the satellite image, could have helped in such a process [28].

4.2.2.4 Image Scaling The SPOT5 image was scaled to 480 m, degrading the image spatially to analyze the response of the subpixel classification algorithm under ideal conditions. This was done using an average of the pixels for the given resolution. Given the magnitude of the scaling involved, this simple approach seemed sufficient to fulfill its purpose.

4.2.3 Image Processing The image processing involved two separate steps. First, a hard classification of the high resolution SPOT image was made to generate a map of the different classes, which was to serve as ground truth data for the second step: the soft classification of artificially spatially degraded SPOT data, and the classification of the MODIS image. For this purpose two different algorithms were considered for hard classification; one supervised and one unsupervised, with the aim of choosing the one that resulted in the best output as reference to relate to the subsequent soft classification.

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4.2.3.1 Supervised Classification The selected algorithm used for the supervised classification was Maximum Likelihood. This algorithm generates images where each pixel is assigned and belongs to a unique class, a characteristic of all hard classifiers. The ML algorithm assumes a normal distribution of the classes along the different bands of an image and calculates the probability of each pixel to belong to a certain class, assigning the class to the highest probability value [46]. For the classification half of the samples were used to train the classifier, and the other half was used for testing the obtained accuracy. The selection of the samples for each purpose was performed arbitrarily using a random number generator. The accuracy assessment was measured using a confusion matrix, taking into account partial and overall classification accuracies, and using kappa statistics as well to have an idea of the magnitude of chance agreements in the classification. The accuracy values arbitrarily set for accepting both hard classifications were a minimum of 80% overall accuracy, and 75% for each individual class, as to serve as good ground truth data for testing the LSU soft classification approach. From preliminary results it was noted that Scirpus spp. was confused with water and Typha dominguensis. A class separability analysis using the Jeffries-Matusita distance confirmed this result (Table 2). The Jeffries-Matusita measure ranges from 0 to 2, indicating how statistically apart a pair of classes or datasets is. Values over 1.9 indicate good separability, while values lower than one usually indicate the belonging to a common class [46, 64]

Table 2. Jeffries-Matusita Distance for Class Pairs CLASS PAIR

Jeffries-Matusita

Separability Value

Eichornia - Potamogeton 2.00 Eichornia - Scirpus 2.00 Eichornia - Typha 2.00 Eichornia - Water 2.00 Potamogeton - Scirpus 1.98 Potamogeton - Typha 1.96 Potamogeton - Water 1.99 Typha - Scirpus 1.87 Typha - Water 1.99 Water - Scirpus 1.62

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As this issue could not be solved after several depurations of the classification and since the class represented only a small fraction of the total area, it was decided to continue the classification without this class. On Figure 6 a graph displaying the mean class spectra for all classes backs this fact up.

Figure 6. Class Mean Spectra

4.2.3.2 Unsupervised Classification The algorithm used for unsupervised classification of the image was ISODATA. As is the case of the supervised classification, this unsupervised approach also gives as a result an image where each single pixel is assigned to only one class according to its spectral characteristics and similarity. In contrast with the supervised classification, this method does not need a prior selection of training pixels to work, but the final output has to be interpreted to assign it to known classes. The algorithm is processor intensive, calculating in each iteration the class means and reclassifying pixels according to these. The creation of new classes, their merging or deletion is based on a user given threshold. The process ends once the pixels in each class change less than this threshold or the number of iterations specified by the user are met [45]. For the classification of the classes observed in Cuitzeo, it was seen that using the number identified in the field was not enough to obtain a satisfactory result, as the variability, especially on the Water and Typha classes, was too great to accommodate for that number. Thus, more were used. After some trials it was

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observed that the best outcome resulted from the definition of 12 to 16 classes that were grouped afterwards into the defined classes, according to the knowledge of the area. The accuracy was measured in the same manner as the supervised classification, and using the same testing pixels to preserve a comparable basis between them. As was to be expected, the problem of the classification algorithm confusing the Scirpus class with water and Typha classes, appeared with this method as well.

4.2.3.3 Soft Classification The algorithm used for soft classification in the scaled SPOT and the MODIS image was Linear Spectral Unmixing. The LSU generates a series of maps, one for each class considered, instead of a unique one depicting all classes present in an image. Here, every pixel is not assigned exclusively to one class, but can be a part of many, modeled by a linear model. This is particularly important when working with coarse spatial resolutions where the problem of mixed pixels is more likely to appear [13, 2]. As the LSU algorithm requires the number of MOI’s to be equal to the number of spectral bands minus one to give a reliable measure of error, the definition of four classes for SPOT posed a problem. To overcome this situation, it was decided to make several runs of the linear mixture model using combinations of only three classes. Afterwards, the values for each class corresponding to the lowest root mean square errors were to be taken as the true ones for the generation of the final MOI maps. The selection of endmembers to train the algorithm was done by direct selection of assumed pure pixels from the images in the case of SPOT and MODIS. For MODIS the selection of pixels to derive the spectral signatures of the different MOI’s was slightly more complex, though. For it, the results of the SPOT hard classification were taken as reference in conjunction with visual analysis of the SPOT and MODIS images to identify homogeneous areas for all the endmembers. The accuracy assessment in all cases was performed by comparing the results of the linear mixture model against the hard classification of the high resolution SPOT5 image. This implied two different measures:

• The determination of the different endmembers present within the pixels, comparing hard and soft classifier, thus allowing the assessment of correct detection of endmembers.

• A difference measure of the endmember fraction values at pixel level,

between the original supervised classification and the soft classifier; allowing the assessment of how well the fractions were estimated.

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For the first item, dealing with presence-absence of different endmembers, a threshold was set at 20% MPF participation for the LSU algorithm [1]. The assessments for both measures were made using visual, qualitative measures and calculated, quantitative ones.

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5. RESULTS 5.1 Class Spectral Characteristics One of the requirements for classifiers to work, either at pixel or subpixel level is to count with spectrally distinctive classes. If this condition is not met, there is risk of confusion between them, leading to poor classification accuracies. This issue is particularly important when counting with few spectral bands as the ability to identify a specific class or material diminishes as less bands, and subsequently less information, is available [46]. The following table and graph (Table 1, Figure 7) show the estimated at ground reflectance for the four classes defined in this study across the different bands of the SPOT5 sensor. It can be seen that there is considerable overlap between the classes, especially in the green and red portion of the spectra. Also to be noted is that green and red bands show very similar patterns in their distribution. The shortwave infrared and the near infrared bands are the ones offering more separation, with the last one presenting the bigger differences and thus better distinction among the classes. As was determined by the Jeffries-Matusita distance measure shown in Table 2, the selected classes appear to be distinct.

Figure 7. Class Mean spectra. SPOT

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Table 3. Class Spectral Characteristics. SPOT

Reflectance SD Reflectance SD Reflectance SD Reflectance SD Reflectance SD(%) (%) (%) (%) (%)

G 7.43 0.67 6.62 0.54 9.02 2.55 5.28 0.80 9.94 2.38

R 4.10 0.50 3.81 0.44 7.84 2.86 3.23 0.63 6.36 2.16

NIR 29.42 4.44 12.11 2.53 15.52 4.36 4.48 1.14 3.83 0.76

SWIR 13.96 2.04 3.50 0.63 16.39 6.02 4.92 1.38 2.74 1.06

Eichornia

BAND

WaterScirpusTyphaPotamogeton

In Table 4 and Figure 8, the class spectral characteristics corresponding to the estimated at ground reflectance for the MODIS TERRA sensor suggest at first view a better performance when compared to SPOT. Analogous bands show improved separation, and the presence of more spectral bands indicates an additional potential for class discrimination. As was the case of SPOT, in MODIS the NIR band exhibits the best separation between classes. However, it is clear from the graph that it outperforms its high resolution counterpart in discrimination ability. Please note the addition of a second water class to the data to improve the classification accuracy of the MODIS linear spectral unmixing. Table 4. Endmembers Spectral Characteristics. MODIS

Bandwidth Reflectance SD Reflectance SD Reflectance SD Reflectance SD Reflectance SD(nm) (%) (%) (%) (%) (%)

1 620 - 670 2.00 0.82 1.33 0.46 7.20 2.46 3.02 0.21 7.57 0.77

2 841 - 876 40.80 4.39 16.06 2.52 24.24 3.94 1.98 0.25 1.80 0.23

3 459 - 479 0.98 0.38 0.64 0.50 2.90 2.43 1.14 0.09 3.91 0.61

4 545 - 565 4.46 1.09 2.92 0.64 5.85 2.44 5.83 0.27 8.83 0.74

5 1230 - 1250 26.37 6.30 4.13 2.00 26.84 5.82 0.78 0.49 0.68 0.38

6 1628 - 1652 14.09 2.84 1.97 0.97 18.78 4.59 0.96 0.10 0.79 0.06

7 2105 - 2155 4.53 1.29 0.99 0.59 10.23 2.83 0.85 0.45 0.35 0.14

Water 2

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Eichornia Potamogeton Typha Water 1

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Figure 8. Endmember Mean spectra. MODIS

5.2 Hard Classification

5.2.1 Unsupervised Classification The classification results for the unsupervised classification, shown in Table 5 and Figure 9 indicate that the mapping of vegetation and water classes can be done accurately with the selected method, giving an overall good representation of what was observed in the field. The overall accuracy as well as the accuracy measures of the individual classes surpass the range defined for satisfactory use of this classification as reference for the linear spectral unmixing of MODIS and scaled SPOT images. It has to be noted though, that for both, unsupervised and supervised classification, the actual accuracies are slightly lower, since Scirpus spp. and other minor vegetation areas are not represented on the classifications. In general terms the unsupervised classification holds very high accuracies except for the user’s accuracy of the water class, which is extraordinarily low when compared to the others classes. The reason behind this, is the confusion of the class water with Potamogeton pectinatus, resulting in the false detection of this water on areas that in fact have vegetation.

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Table 5. Unsupervised Classification Accuracies Class name Producers Users Kappa

Accuracy AccuracyUnclassified -- -- 0.000Eichornia 99.57% 99.29% 0.993Potamogeton 87.83% 99.18% 0.985Typha 97.06% 100.00% 0.986Water 99.23% 79.11% 0.685

Overall Classification Accuracy = 93.45%Overall Kappa statistics = 0.883

Figure 9. Unsupervised Classification Output Map

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5.2.2 Supervised Classification As can be seen from Figure 10 and Table 6, the ML algorithm used in the supervised classification shows similar trends when compared with the results attained by the unsupervised classification. The same trends applying to the lower user accuracy attained in the classification of the class water. The visual comparison between unsupervised and supervised approach show small variations and from the overall and individual results by class, this classification again lies well above the acceptable thresholds set to perform as ground truth data. Even with the slightly less accurate results obtained by this classification it was decided to use it as reference for the soft classification. The basis for this decision was founded on the empirical knowledge gained on the area, considering that the supervised classification better represents the actual field conditions. Since the difference in accuracies between both classifications was deemed negligible, this decision seemed justified. The areas with major divergences between both classifications are encircled in red on Figure 10.

Table 6. Supervised Classification Accuracies Class name Producers Users Kappa

Accuracy AccuracyUnclassified -- -- 0.000Eichornia 99.57% 97.22% 0.970Potamogeton 85.47% 99.95% 0.999Typha 97.10% 99.42% 0.943Water 99.57% 76.35% 0.674

Overall Classification Accuracy = 92.46%Overall Kappa statistics = 0.870

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Figure 10. Supervised Classification Output Map

The choice of using the supervised classification output as reference for the linear spectral unmixing of scaled SPOT and MODIS images is confirmed further when looking at Table 7, which shows the areas of both classifications to be in high concordance. Confusion matrices for the supervised and unsupervised classifications can be found on Appendix 2 for more details.

Class N Area Percentage Class N Area Percentage (km2) from Total (km2) from Total

Eichornia 137092 19.74 8% Eichornia 121217 17.46 7%Potamogeton 207786 29.92 12% Potamogeton 246838 35.54 14%Typha 351087 50.56 20% Typha 332285 47.85 19%Water 1035459 149.11 60% Water 1031084 148.48 60%

Total 1731424 249.33 100% Total 1731424 249.33 100%

Supervised Classification Unupervised Classification

Table 7. Classified Areas per Class

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5.3 Linear Spectral Unmixing

5.3.1 SPOT

5.3.1.1 Preliminary Visual Analysis The visual inspection of the MPF maps of the scaled SPOT image, shown in Figure 11, indicates that the LSU algorithm was generally capable to detect the patterns present in the supervised classification, but at the same time incurred in great errors. This is evident when looking at the fraction maps for Eichornia crassipes and Potamogeton pectinatus. The first exhibits an over detection along a wide range of the central portion of the area and in the case of Potamogeton, there is a clear case of false detection of the endmember in the western portion of the lake, particularly to the south. Typha and water classes show a better behaviour, with distribution and relative values more closely resembling the outputs of the supervised classification, though still some errors exist.

Eichornia Potamogeton

Typha Water

Figure 11. Material Pixel Fractions. SPOT

It has to be noted that output values of the different MPF’s for SPOT many times fell beyond acceptable limits, showing highly positive and negative values, usually corresponding to areas of high RMSE (Figure 12). In those cases, values

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that exceeded 1 were considered to be 1 and values under 0 were assimilated to this cipher. All further analysis took this assumption, except for the generation of difference maps between reference and predicted data.

Figure 12. Overall RMSE. SPOT

Performing an LSU classification on MODIS, using bands analogous to SPOT, it was seen that the results in between both, SPOT and MODIS, have very similar patterns of spatial distribution, incurring in the classification errors. Nevertheless in the case of MODIS, the use of these bands resulted in more extreme fraction values and a clearly higher RMSE, as Figure 13 reveals.

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Figure 13. Overall RMSE. MODIS Bands 2146

MPF maps depicting the distribution of the material pixel fractions for MODIS using the linear spectral unmixing algorithm with bands 2, 1, 4 and 6 can be found on Appendix 3 for comparison.

5.3.1.2 Presence-Absence Analysis and Accuracy Assessment To determine how accurately the LSU was able to identify the presence of particular fractions within pixels, all the pixels from the reference image whose fraction summed up to one, that is all “whole” pixels, were selected and compared to it. This analysis served to determine the accuracy in the detection of the different fractions but not to assess if those fractions were correctly estimated. The results are exposed on Table 8.

Table 8. Accuracy Assesment. SPOT

Class name Reference Classified Number Producers UsersTotals Totals Correct Accuracy Accuracy

Eichornia 113 166 67 59.29% 40.36%Potamogeton 162 301 137 84.57% 45.51%Typha 271 223 186 68.63% 83.41%Water 648 655 620 95.68% 94.66%

Totals 1194 1345 1010

Overall Classification Accuracy = 84.59%

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From the table can be extracted that the scaled SPOT image does not do a very good job in properly detecting the presence of endmembers. Particularly Eichornia exhibits bad results, especially in wrongly detecting it in places where it does not occur. Presence-Absence maps displayed in Appendix 4, put in evidence that this endmember is being confused with Typha. Potamogeton also shows mediocre results, producing many false detections as the user’s accuracy indicates, particularly in the southwestern portion of the area. Typha and water on the other hand exhibit good results. This assessment is congruent with the preliminary analysis of the MPF maps, giving more and precise insights on the magnitude, location and type of classification problems.

5.3.1.3 Material Pixel Fraction Accuracy Assessment The assessment presented here corresponds to the second key element to determine the success rate of the subpixel classifier. It intends to answer how well does the subpixel classifier perform in detecting the amounts of the different endmember fractions within the pixels. The overall results are shown on Table 9, and correspond to the difference between the MPF’s determined from the reference data minus the estimated MPF’s obtained from the subpixel classification.

Table 9. Class Difference. Reference Data vs Predicted Data. SPOT

Valid N Mean Minimum Maximum RMSE

Eichornia 841 -0.07 -1.00 0.28 0.16

Potamogeton 841 -0.10 -1.00 0.57 0.24

Typha 841 0.00 -0.66 0.70 0.15

Water 841 0.04 -1.00 0.63 0.23

From this table, which also takes pixels entirely situated within the lake boundaries, it can be drawn that the LSU algorithm in the case of the scaled SPOT image slightly overestimates the proportions of Eichornia and Potamogeton, this last displaying the highest root mean square error and thus having a more unreliable value of the mean. Typha and water present mean values that are close to the reference MPF’s, accompanied by moderate and high RMSE values respectively. The difference maps from Figure 14 help to further assess the quality of the statistics shown in Table 9. They illustrate the difference between reference data and estimated data from the LSU on a pixel by pixel basis. High overestimations

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of the MPF are represented by dark blue color, while high underestimations of it are displayed in red. An enlarged version of the maps can be found on Appendix 5. Relating the difference maps to the presence-absence maps, it can be seen that for Eichornia the measure of the mean for the MPF’s differences is close to zero in areas where the species is present. Moderate values nearing zero, represented by light orange and light green values showing across the vast majority of the map indicate an ideal functioning of the linear model. Nonetheless, a closer look reveals an overestimation of the MPF happening precisely in areas that are wrongly detected in the presence-absence analysis.

Eichornia Potamogeton

Typha Water

Figure 14. Material Pixel Fraction Difference Maps. SPOT For the Potamogeton endmember, its difference map shows only a slight overestimation in places covered by this vegetation, assessing the value of the MPF properly for these areas. Again high difference values occur in places where the endmember is wrongly detected as present. The difference map for Typha exhibits a very good performance, with only slight differences throughout the map, telling us that the fraction of the endmember is very well estimated by the LSU model. In the case of water, there is a good overall detection of the fractions, with two exceptions. The presence of a slight sub estimation in the fractions around the coordinates 297000 E and 220 6000 N, and a moderate to high sub estimation

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at the coordinates 286000 E and 220 9000, this last one situated in front of the peninsula and the village of San Agustin del Pulque, where two arms of the lake meet, producing the convergence of water with spectrally different signatures. From all the endmembers in the case SPOT, the one that is best modeled by the LSU is Typha, with excellent estimation of its fraction and a moderate to good detection of its presence. When a similar difference analysis was performed on the MODIS image using bands 2, 1, 4 and 6, similar patterns of conduct on the overall means and RMSE’s as the scaled SPOT image were revealed, but with much more extreme values in both statistics as Table 10 demonstrates.

Table 10. Class Difference. Reference Data vs Predicted Data. MODIS Bands 2146

Valid N Mean Minimum Maximum RMSE

Eichornia 841 -0.26 -0.87 0.54 0.37

Potamogeton 841 -0.12 -1.00 0.95 0.45

Typha 841 0.01 -0.93 0.89 0.25

Water 841 0.15 -0.59 0.85 0.29

5.3.2 MODIS

5.3.2.1 Preliminary Visual Analysis Visual inspection of the MODIS MPF maps (Figure 15) show good correspondence between the visual patterns and relative values of the fractions when compared with the supervised classification. The images also clearly suggest a better behaviour in the distribution of the different endmembers when compared with the SPOT LSU results. Errors can be seen in the wrong detection of Eichornia, but still following proper distribution patterns, and in a sub estimation of the water fractions in the central and eastern portions of the area. No other major errors can be detected from the visual inspection of these images.

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Eichornia Potamogeton

Typha Water

Figure 15. Material Pixel Fractions. MODIS The output values for the different MPF’s in the case of MODIS display a better conduct in relation to the SPOT soft classification, with most values lying within the 0 to 1 range. The overall RMSE is also much lower as is exposed by Figure 16. Still there are areas that with high errors that show distinctive patterns which indicate a bad fit of the linear model, and will be addressed in the next sections.

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Figure 16. Overall RMSE. MODIS

5.3.2.2 Presence-Absence Analysis and Accuracy Assessment The accuracy assessment of the MODIS image exposed on Table 11 shows high to very high producer’s accuracies and moderate to high user’s accuracies, with the exclusion of Eichornia. This last value indicates the assignation of the endmember to areas that do not present that vegetation. The presence-absence map, present in Appendix 4, confirms this and adds to the information by showing that errors in detection happen mostly in neighboring areas where the endmember is present and along Lake Boundary areas.

Table 11. Accuracy Assessment. MODIS

Class name Reference Classified Number Producers UsersTotals Totals Correct Accuracy Accuracy

Eichornia 113 300 109 96.46% 36.33%Potamogeton 162 221 144 88.89% 65.16%Typha 271 279 195 71.96% 69.89%Water 648 732 598 92.28% 81.69%

Totals 1194 1532 1046

Overall Classification Accuracy = 87.60% In the case of Potamogeton, even if the values are within good limits, it can be seen from the Presence-Absence maps that the endmember appears as wrongly detected as Typha on one region to the southeast of its distribution. The other

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sources of misdetection are fringes that limit with lake boundaries, even if this phenomenon is seen to a much lesser extent when compared with Eichornia. Typha also exhibits this peculiarity with the highest degree between the four classes, being the major source in user’s accuracy reduction. As for the water class, wrong detections appear in places where Eichornia is to be found and a case of no detection occurs in front of the peninsula.

5.3.2.3 Material Pixel Fraction Accuracy Assessment The overall results for the difference between reference and predicted MPF’s for MODIS are presented on Table 12. This table shows a good estimation in the present fractions, as their values approach to zero and their root mean square error are moderate. The exceptions to this are the water endmembers, where high underestimation takes place, accompanied by a high RMSE. This RMSE value has to be interpreted using the difference maps to determine if there is any visual pattern to be detected and where it appears, or to see if there is in turn a lack of any coherent pattern. Both have different implications on the model.

Table 12. Class Difference. Reference Data vs Predicted Data. MODIS

Valid N Mean Minimum Maximum RMSE

Eichornia 841 -0.08 -1.00 0.34 0.17

Potamogeton 841 0.02 -1.00 0.82 0.16

Typha 841 0.06 -0.50 0.82 0.18

Water 841 0.16 -0.32 0.92 0.32

From the mean difference, a very negative minimum and rather low maximum, there seems to be some overestimation in Eichornia, which is confirmed by analysis of the difference maps (Figure 17). Also overestimation is detected in areas where the endmembers is falsely detected, as was expected from the presence-absence maps. The difference map for Potamogeton shows good behavior in the prediction of the MPF, with most values nearing zero, in agreement with the results exposed on Table 12. Typha also shows high concordance with the table, showing low differences across most of its difference map and indicating slight underestimation in of MPF values in zones where the vegetation is present. Some effects from the detection of false positives from the presence-absence maps can be noted here along the borders of the lake.

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Finally, the map for the water endmembers shows the biggest discrepancies as was expected from the RMSE. Here there are many areas whose fractions are properly predicted, mainly in the western portion of the study area, however there is clear underestimation in the region surrounding the tip of the peninsula, the same happening with milder expression on the western portion of the study area, around the coordinates 297000 E and 2207000 N.

Eichornia Potamogeton

Typha Water

Figure 17. Material Pixel Fraction Difference Maps. MODIS

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6. DISCUSSION 6.1 On Classification Accuracy When assessing the accuracies of the image classifications presented in this work, several things have to be kept in mind. The most important one, the way of data collection that served as input for the classification of the reference set. The gathering of border information and assumption of presence of particular classes based on annotations and knowledge of the area casts doubts about the validity of the classification. Every possible measure was taken to minimize this effect, relating density measures and vegetation presence information with the visual interpretation of the SPOT image. Nevertheless, even when counting with a high resolution image, which certainly helps to ameliorate the effects of the poor sampling, care has to be taken in the interpretation of the results, as the presence of bias is likely to happen. To summarize, it is not certain that the reference data in this case actually is representative of the entire classification. Another source of bias that influences the conclusions that can be drawn from the classification is the quality in the co registration of the images [11]. This is still a matter of consideration when working with MODIS data, as the quality of its georeferencing could mean a maximum of 10 to 15 % of error in the area of a pixel. For subpixel classification the quality in the selection of the training set can have an important effect. This is essential to properly identify different endmembers and for MODIS it proved to be highly relevant, as it is clear from the results that a high adjacency effect exists between contrasting features. 6.2 Linear Spectral Unmixing 6.2.1 Endmember Detection The linear spectral unmixing approach proves to be useful in detecting the presence of different endmembers in the case of using SPOT and MODIS. Nevertheless, there is a clear improvement in the results exhibited by MODIS, with producer’s accuracies that are 4 % to 36 % superior (Table 8, Table 11). This behaviour can be attributed to the availability of more spectral bands in the case of MODIS, compensating in part its lack of spatial resolution. Such phenomenon is known and has been reported in other studies [19, 26]. When comparing scaled SPOT and MODIS, another important fact that can be partially attributed to the greater availability of bands, is its superior result in the detection of errors in terms of their spatial distribution (Appendix 4). MODIS is more consistent, showing detection errors in areas close to properly detected

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zones. The reason for the low user’s accuracies it attains is explained to great extent by the presence of another singularity: adjacency effects. The results suggest that this could be a limitation to the use of MODIS data in areas of high contrasting nature, as is the case of wetlands, and raises concerns about its application especially on heavily fragmented or small isolated areas. 6.2.2 MPF Estimation When unmixing an image to determine the MPF’s of their pixels two types of error in the estimation appear: avoidable and unavoidable. Spectral limitations of remote sensors introduce the second kind, as a low number of spectral bands difficult detailed modeling of the endmember spectra. This in turn creates differences between the values of endmember and pixel spectra. Also unavoidable errors can be derived from the presence of endmembers which don’t mix in a linear fashion. Errors in sensor operation and sensor noise can also add to the ones already mentioned. Avoidable errors take place when the selection of endmembers is wrong, for example when these do not correspond to pure fractions. Other errors that may be hindered, assuming the sensor has sufficient bands, come from the partial definition of the number of endmembers present in areas to classify. The determination of endmember fractions for both MODIS and SPOT in Lake Cuitzeo exhibit good behaviour in places where the endmembers are present, generally showing the capacity of estimating values within 20 % to 25 % of the actual ciphers (Table 9, Table 12). The unmixing of SPOT, however introduces important unavoidable errors as the sensor lacks enough bands to accommodate all the endmembers present. At the same time its spectral bands have much overlap between the different endmembers in the green and red bands, situation which contributes to even poorer outputs, as is reflected by the difference maps from Appendix 5. This results in high RMS errors and the false detection of some endmembers in vast areas. The LSU of MODIS on the other hand presents more errors which are avoidable. The overall values on the difference maps show only slight divergence, producing good estimates across the whole maps, except for the water endmember. Even when the differences are small, patterns can be detected on the maps. This is more evident in the case of Typha, and it suggest that the selection for this endmember could be improved, which is reasonable as this endmember shows high spectral variability. When no more recognizable patterns are found and the RMSE is overall small, it can be deduced that a near perfect model has been reached. As said, the only high contribution to the error in the case of MODIS is the water endmember. One of the reasons for it is that water is not an endmember in the proper sense, as it does not have a unique spectral signature. This makes

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classification difficult in environments like wetlands. From the patterns present in the difference map and analysis of the original image is noticeable that the errors happen in areas are covered by water, producing a high sub estimation of the MPF values. This indicates without doubt that there is need to create a new endmember to accommodate the spectral variability that this class has, and puts in evidence the iterative nature in the LSU process; as more refinements take place until the lowest RMSE and no recognizable patterns are reached in the difference maps. Even having room for improvement it is clear that MODIS, with its higher band numbers performs better in this field too when compared to SPOT. When the sources of error were mentioned it was said that the spectra of the different endmembers was modelled by linear equations. This introduces another form of quality assurance, as these equations can be inverted taking the endmember spectra to reconstruct the spectra of each pixel from the original image. Divergences between the original and reconstructed image also give idea of the goodness of fit of the model, and give insights to which bands add more to the errors. Such image pair is shown in Appendix 6, indicating here once more an overall good fit of the LSU model when using MODIS images to classify the wetland. The main errors in the reconstructed image come from the SWIR bands as is exposed by histograms and difference table found in Appendix 7. 6.2.3 Hard vs Soft Classification Hard classification of an image of the same pixel size as MODIS cannot be directly compared with results of a soft classification. In the case of the subpixel classification there is not only an issue of multiple assignation of more than one class to a pixel, but also an issue of properly determining the participation of that class within the pixel, making any correlation difficult. If accuracies just in terms of presence-absence are similar, we are already dealing with a performance three to four times superior when compared with the hard classification in places where mixed pixels appear. This is product of the ability of the subpixel classifier to determine the material pixel fractions that, as mentioned in chapter 6.2.3 for the case of MODIS subpixel classification of the Cuitze Lake, lie in the range of +- 25 %. As additional argument it can be said that as pixel size increases, the chances of high accuracies in hard classifications being product of random assignation of values magnifies. This is depicted in Appendix 8, where results of the hard classification for one image artificially degraded to various resolutions is shown. So, though no accurate value can be given, it becomes evident that MODIS subpixel classification performance is definitively better, when mixed pixels are present.

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6.2.4 Pending Tasks Because of time constraints and lack of data, there are research questions that could not be answered. First, the requirements and success in porting spectral signatures from the MODIS classification, and their use on images from the same sensor and area, acquired on close dates, could not be determined. Secondly, the effect of using ancillary data as means to improve the classification was also not explored. Both issues open areas for further investigation.

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7. CONCLUSIONS AND RECOMMENDATIONS

• Results obtained from this study reveal that it is possible to successfully map communities of plants in Lake Cuitzeo using MODIS images and linear mixture modeling. They also show that the fractions present at subpixel level can be estimated with a good degree of accuracy, lying within 25% of the actual values.

• Differences obtained in the subpixel classification of MODIS and scaled

SPOT images allow concluding that there is a marked increment in classification accuracy as more spectral bands are incorporated into the classification.

• The performance of the sot classifier is at least three times higher than a

hard classification of an artificial image of the same pixel size in areas that show mixed pixels.

• The presence of adjacency effects between features present in the lake

could affect the applicability of MODIS subpixel classification in small wetlands and those where the spatial arrangement of the different features favor the presence of vast borders between wetland and non- wetland areas.

• The definition of more endmembers to represent the water class is

necessary to increase classification accuracy, as well as the addition of another endmember or depuration of the already existing in the case of the species Thypa dominguensis.

It is advised to survey bigger wetland areas of similar type as Cuitzeo to clarify the influence of adjacency effects detected in the use of the MODIS TERRA sensor, and investigate its impact on the classification of the different endmembers. It is also recommended to initiate a pilot programme with aims to assess the applicability of wetland monitoring in the Cuitzeo Basin. MODIS global coverage and frequent update favours such initiative, which could prove of much value in the detection of trends in vegetation development and in the evaluation of management measures applied to the area.

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BIBLIOGRAPHIC REFERENCES

1. Applied Analysis Inc., ERDAS IMAGINE Subpixel Classifier. User’s Guide. Version 8.6, ed. L.G.G.m. division. 2003, Atlanta, Georgia: Leica Geosystems GIS & mapping division. 188.

2. Atkinson, P.M., M.E.J. Cutler, and H. Lewis, Mapping sub-pixel proportional land cover with AVHRR imagery. International Journal of Remote Sensing, 1997. 18(4): p. 917-935.

3. Augusteijn, M.F. and C.E. Warrender, Wetland classification using optical and radar data and neural network classification. International Journal of Remote Sens