ATBD_RAlgorithmTheoreticalBaseDocument&Results

COSTALZONE

EXECUTIVESUMMARY

TableofContents

List of Figures ........................................................................................................................ 3

List of Tables ......................................................................................................................... 4 1.1 Brazilian Costal Zone and its Legal Definition .......................................................... 5 1.2 Key Science and Applications ................................................................................... 6 2.1 Brazilian Costal Zone, Context and Key Information ................................................ 7

2.2 Existent Maps and Mapping Initiatives ...................................................................... 8 2.2.1 World Atlas Of Mangroves ................................................................................. 8 2.2.2 NOAA’s Digital Coast ......................................................................................... 8 2.2.3 MangroveWatch ................................................................................................. 9 2.2.4 EMODnet Coastal Mapping ............................................................................... 9

3 Algorithm Descriptions, Assumptions, and Approaches .................................................. 9 3.1 Algorithm Descriptions, Assumptions and Approaches ............................................ 9 3.2 Collection 1 ............................................................................................................. 13

3.2.1 Algorithm .......................................................................................................... 13

3.2.1.1 Cloud Free Composites. .............................................................................. 14 3.2.1.2 Parameters Extraction .................................................................................. 16 3.2.1.3 Classification ................................................................................................ 17 3.2.1.4 Temporal Filter ............................................................................................. 19

3.3 Collection 2 ............................................................................................................. 21

3.3.1 Algorithms ........................................................................................................ 21 3.3.1.1 Cloud-free composites. ................................................................................ 21 3.3.1.2 Parameters Extraction. ................................................................................. 23 3.3.1.3 Classification ................................................................................................ 24 3.3.1.4 Temporal Filter ............................................................................................. 25

4 Validation Strategy ......................................................................................................... 30 5 Concluding Remarks ...................................................................................................... 31 6 References ..................................................................................................................... 32

ListofFigures

Figure 1 - In green, the legal delimitation of the Brazilian Coastal Zone terrestrial stripe. ...... 6 Figure 2 – A esquerda, os sete setores delimitados em cores diferentes. A direita, os mesmos sete setores, sobre um mosaico Landsat 8. ............................................................. 9 Figure 3 – Extreme north of Amapa, regardless of the year, this area is persistently covered by extreme cloud condition. In solid black, cloud residue identified for the year 2000. ......... 11 Figure 4 – General processing flow chart .............................................................................. 12 Figure 5 – Mainframe of the MapBioma Workaspace interface. ........................................... 13 Figure 6 - Zona Costeira é enquadrada em 91 cartas ao longo do território costeiro do Brasil...................................................................................................................................... 14 Figure 7 – Workspace Cloud Free Mosaic main interface .................................................... 15 Figure 8 – Collection 1 Input data, as described in GEE Java Script. .................................. 15 Figure 9 - Spectral curve of the Forest class as in Landsat 5. .............................................. 17 Figure 10 –Java Script to for endmembers collection and generation of fraction images. .... 17 Figure 11 - Empirical decision tree, having NDFI, GV and GVs as parameters to map the Forest and Non-Forest classes. Water and Cloud masks are applied to compose the final map. The variables (V1, V2, V3, V4) can be individually adjusted for each chart. ................ 18 Figure 12 – Classification based on empirical tree definition. ............................................... 19 Figure 13 - Diagram of components of the Earth Engine Code Editor atcode.earthengine.google.com ............................................................................................ 21 Figure 14 – Script para remoção de nuvens e sombras aplicado sobre a Coleção 2 da Zona Costeira. ................................................................................................................................ 22 Figure 15 – Cloud Free Mosaic examples, from the left to the right, 2000, 2005, 2010, 2015............................................................................................................................................... 22 Figure 16 – rescaled calculation of NDVI and MNDWI. Note the term “+ 100” and multiplicative “ * 100”, at the beginning and end of the expression. ...................................... 23 Figure 17 – In the top left, NDVI, followed by MNDWI. In the bottom left, greyscale WVI and finally the color ramped WVI. Green represents Mangrove Forests, magenta for Not Vegetation, yellow for Non-Mangrove Vegetation and blues as Water. ................................ 23 Figure 18 – Classes and its ID’s ............................................................................................ 24 Figure 19 – Maranhao State Coast. Above classification without temporal filter application. Below, post filter result. ......................................................................................................... 30 Figure 20 – Web Collect interface. In the top left, the view and statistical panel, in the top right, Classification Panel and in the bottom, the mosaic temporal variation panel. ............. 31

ListofTables

Table 1 – Collection 1 Temporal Filter. This tables show all the invalid trajectories and how they are filtered out. ............................................................................................................... 20 Table 2 – The table shows how the temporal filter operates, as demonstrate by the before and after columns. The acronyms represent; F = Forest, MG = Mangrove Forest, NFV = Not Forest, PD = Beaches and Dunes, NV = No Vegetation, AG = Water, NO = Not Observed. 25

1 INTRODUCTION

1.1 BRAZILIAN COSTAL ZONE AND ITS LEGAL DEFINITION According to Decree No. 5,300/2004, the Brazilian Coastal Zone (BCZ) corresponds to the geographical interaction space of air, sea and land, including its renewable or non-renewable resources, covering a sea and a terrestrial strips, with the following limits: - Sea strip; space extending twelve nautical miles, measured from its baseline 1 , thus comprising the entire territorial sea. - Terrestrial strip; land portion directly influenced by coastal zone phenomena; this specifically refers to the municipalities along the Atlantic Ocean, and other municipalities where high relevance coastal ecosystems exist and environmental and/or infrastructure impacts, due to coastal zone activities, have been registered. To what regards MapBiomas Project, the BCZ mapping region directly encompasses the "Terrestrial Strip", as shown in Figure 1:

1 Baseline – According to UNCLOS (United Nations Convention on the Law of the Sea), a baseline is defined as, the line along the coast from which the seaward limits of a state's territorial sea and certain other maritime zones of jurisdiction are measured, such as a state's exclusive economic zone. Normally, a sea baseline follows the low-water line of a coastal state.

1.2 KEY SCIENCE AND APPLICATIONS Coastal zones are exposed to a wide range of

coastal hazards including sea-level rise and it associated effects, while at the same time, it is more densely populated than the hinterland and exhibit higher rates of population growth and urbanization (Neumann et al., 2015). Brazil’s coastal zone extends for approximately 9,200 km and presents a very diverse suite of coastal environments that evolved during the Quaternary, in response to changes in climate and sea level, interacting with varying sediment supply and a geologic heritage that dates back from the South America to Africa break up, during the Mesozoic (Domingues et al., 2009).

The mangroves systems are among this diverse suite of coastal environments. Globally the mangrove forests are distributed in the tropics and subtropics intertidal region, between approximately 30 ° N and 30 ° S (Giri, 2016). According to (Giri et al., 2011), in the year 2000, the mangrove forests of the planet represented a total area of 137,760 km², s 118 countries, around 1% of the total tropical forests in the world. In Brazil, the mangrove area is approximately 107000 km2 (Magris and Barreto, 2010).

Mangroves systems play an important role in human sustainability, providing a wide range of ecosystem services, including nutrient cycling, soil formation, wood production, fish spawning grounds, ecotourism and carbon (C) storage (Murdiyarso et al., 2015; Saenger et al., 1983; Alongi, 2002), being one of the most productive and biologically complex ecosystems on earth (Donato et al, 2011). Current studies suggest that mangroves and coastal wetlands annually sequester carbon at a rate two to four times greater than mature tropical forests and store three to five times more carbon per equivalent area than tropical

Figure 1 - In green, the legal delimitation of the Brazilian Coastal Zone terrestrial stripe.

forests (Murdiyarso et al., 2015). In addition to these functions, mangrove forests protect the coastal zone from the action of waves, tides, pororocas and tsunamis (Kathiresan and Rajendran, 2005, Giri et al., 2008).

Due to the great importance of this ecosystem, since the 80s, mapping and change detection in mangrove areas at global scale are carried out (Saenger et al., 1983; Spalding et al., 2010; Giri et al., 2011). However, there are no studies in the current literature, allowing systematic and continuous identification of wetlands, and its associated changes, whether in global or regional scale, case of the Brazilian coastal zone (ZCB).

Part of this, is due to scarcity of mathematical mechanisms designed specifically for wetlands mapping (vegetation indices, for example) and its distinguishment from surrounding forest formation. Traditionally mangrove identification uses, among others, classical vegetation indexes such as NDVI, EVI and NDWI, visual interpretation, supervised classification and the use of microwave imagery (Tong et al., 2004; Magris e Barreto, 2010; Fei et al., 2011; Giri et al., 2011; Alsaaideh et al., 2013; Nascimento Jr et al., 2013; Nardin et al., 2016).

In addition, systematic and continuous identification of patterns, whether forest or not, requires large storage capacity and large data processing capacity. These two variables have only been recently circumvented, with the advent of cloud computing platforms, such as Google Earth Engine (GEE) and Amazon Web Service (AWS), which combine several petabytes of orbital and geospatial data with statistical analysis resources in planetary scale.

The Mapbiomas project aims, among many other goals, to identify and analyze variations of occurring in the in the Brazilian coastal zone, recognizing; beaches and dunes, mangrove forests, water bodies, in systematic and automated manner, from the year 2000 to 2016, integrating orbital, geospatial and statistical data through the Google Earth Engine platform (GEE).

2 OVERVIEW AND BACKGROUND INFORMATION

2.1 BRAZILIAN COSTAL ZONE, CONTEXT AND KEY INFORMATION

Coastal areas comprise 20% of the Earth’s land area (Burke et al., 2001) and host almost half the planet’s population (Small and Nicholls, 2003). Coastal zones have always attracted humans due to its resource abundance, particularly the supply of subsistence resources; for logistical reasons, as it offer access points to marine trade and transport or simply because of its special sense of place at the interface between land and sea.

It is widely known that a significant part of the Earth's shores, undergoes geological processes of coastal erosion and/or accretion, naturally reshaping the coastal zone. Although, more recently the anthropogenic contribution has unbalanced this scenario, artificially reshaping shorelines, modifying sedimentary balance, changing river courses and, more commonly, converting natural forest cover into a more profitable land use. The development and utilization of coastal zones has greatly increased during the recent decades and coasts are undergoing tremendous socio-economic and environmental changes (Neumann et al., 2015).

In Brazil, this dynamic landscape of fast physical and socio-economic changes is home to approximately 18% of the country’s population, inasmuch as 16 out of 28 metropolitan regions lie along the coast (Nicolodi and Petermann, 2011). Climate change disturbances may also lead to a maximum global loss of 10 to15% of mangrove forest, but this climatic variable is to be considered of secondary importance when compared with current average annual rates of 1–2% deforestation (Along, 2008).

Despite their importance for most coastal tropical countries in Latin America, mangrove ecosystems have experienced accelerated rate of resources depletion, precise estimates of deforestation in mangrove areas of Latin America are scarce. Although conversion of mangrove forests into rice fields, salt, mariculture and carciniculture ponds are well reported along the continent coastal zone (ADD REFs with conversion examples).

Apart from deforestation, degradation of large mangrove areas are taking place in many Latin American countries due to misuse of coastal resources, and in Brazil it is not different. Diversion of fresh-water and land reclamation are, among others, the major actions leading to mangrove degradation. The mangroves of Guanabra Bay, in Rio de Janeiro, which measured 50km2 in the beginning of the century, is heavily impacted with less than 15 km2 of pristine forest remaining (Salmons et al. 1999).

Coastal environments scenarios, highly dynamic, where climatic, geological and primordially anthropic factors, act together, imposing constant land use and land cover changes, urges for continuous and systematic monitoring platforms. Not only to make precise estimation of deforestation, or any other conversion, but more importantly, such platforms, as MapBiomas, would pave the path to the appropriate design of public policies, supported by up-to-date information, in such way to allow sustainable use the coastal zone and a more organized population growth.

2.2 EXISTENT MAPS AND MAPPING INITIATIVES

2.2.1 World Atlas Of Mangroves From Mark Spalding, Lorna Collins e Mami Kainuma, 2010. An atlas that provides the global assessment of the state of the world's mangroves. It includes detailed estimates of changes in mangrove forest cover worldwide and at regional and national levels, an assessment of these changes and a country-by-country examination of biodiversity protection.

2.2.2 NOAA’s Digital Coast https://coast.noaa.gov/digitalcoast/tools/index.html

“This NOAA-sponsored website is focused on helping communities address coastal issues and has become one of the most-used resources in the coastal management community. The dynamic Digital Coast Partnership, whose members represent the website’s primary user groups, keeps the effort focused on customer needs.”

2.2.3 MangroveWatch http://www.mangrovewatch.org.au/

‘MangroveWatch’ is a program that has been established to address the urgent need to preserve and protect threatened tidal wetland ecosystems as well as addressing both scientific and environmental management needs.

2.2.4 EMODnet Coastal Mapping http://www.emodnet.eu/coastal-mapping

The main objectives of the EMODnet Coastal Mapping project are to assess the current availability of digital coastal maps in the EU, to disseminate this information by EMODnet, to share experience of coastal mapping in the EU, to develop standards for best practices and to propose how a future Joint European Coastal Mapping Programme (JECMAP) could operate.

3 ALGORITHM DESCRIPTIONS, ASSUMPTIONS, AND APPROACHES

3.1 ALGORITHM DESCRIPTIONS, ASSUMPTIONS AND APPROACHES

All the mapping produced by MapBiomas occurs supported by Google Earth Engine platform. Google Earth Engine (GEE) is an online processing platform that gathers satellite images of the Earth's surface and makes them available to scientists and independent researchers to map and quantify changes on the surface of the globe. The platform has a collection over 40 years of imaging and is available online.

Since the Brazilian coastal is an extensive region, approximately 8,500 kilometers from Oiapoque to Chuí (not counting reentrances), it is affect by different atmospheric systems of lesser or greater influence of nebulosities, has different geological ( Domingues et al., 2009), structural and environmental characteristics (Schaeffer-Noveli et al., 1990). Due to this heterogeneity, the ZCB is divided into 7 different sectors, figure 2.

Figure 2 – A esquerda, os sete setores delimitados em cores diferentes. A direita, os mesmos sete setores, sobre um mosaico Landsat 8.

Sector 1 - Amapa (AP), coastal region of the state of Amapa. Sector 2 - Marajo Island (MAR), coastal region of Marajo Island. Sector 3 - Para / Maranhao (PAMA), coastal sector of the states of Para and Maranhao. Piaui / Bahia (PIBA), coastal sector of the states of Piaui to Bahia. Sector 5 - Espirito Santo / Sao Paulo (ESSP), region that includes the states of Espirito and São Paulo. Sector 6 – Parana/Laguna (PRLA), a coastal region that goes from the state of Parana to the municipality of Laguna in Santa Catarina and finally Sector 7 (LARS), a region that ranges from Laguna to the state of Rio Grande do Sul.

Prior to coastal zone classification, it is necessary to produce cloud-free mosaics covering the entire Brazilian Coastal Zone (ZCB) and the entire temporal range, 2000 until 2016. Landsat mosaics are produced based on the "Best Pixel Availability" (BPA). Obeying a variety of pre-set parameters, BPA ranks every pixel in the Landsat collection according to its cloud-shadow condition and selects the pixel with the best condition (less clouds and shadows as possible). The selection interval begins on January 1 of and ends on December 31 of each year.

The mosaics, as far as possible, are designed to maximize the observable area of the Brazilian coastal zone. However, due to its conditional nature, this selection technique is directly affected by the scarcity of cloud-free orbital data. Thus, for a given region "R" (Coastal Zone), in a certain period of time T (January to December of each year), there may not be a pixel that satisfies the cloud-free condition, thus this position in the mosaic is represented by absence of pixel, resulting from the non-fulfillment of the selection assumptions.

Cloud residues and non-observed pixels are counted, in percent and absolute terms, for each year of the mosaic, figure 3. It is worth remembering that, even if cloud residues are identified, there may be sections of the mosaic contaminated by Haze, type of cloudiness that presents on the form of fog, being of difficult identification / counting.

Figure 3 – Extreme north of Amapa, regardless of the year, this area is persistently covered by extreme cloud condition. In solid black, cloud residue identified for the year 2000.

The choice of a wide time interval, 1 year, comes from two main characteristics; 1 - High incidence of clouds in coastal regions and 2 - High resilience of mangrove forests, one of the main themes to be mapped in the ZCB.

The ZC is a narrow, contiguous portion of land that runs from north to the south of the country, always side by side with the ocean, one of the largest cloud producers due to its high evapotranspiration rate. In addition, the north and northeast region of the country, severe atmospheric systems are constantly present, such as ITCZ (Intertropical convergence zone). The ITCZ is the main cause of precipitation in the tropics and responsible for bad weather over a wide area, leaving sections such as Amapa , Marajo, northeast of Para and part of the Brazilian northeast systematically covered clouds.

Moreover, mangrove forests are not as sparse shrub vegetation, pastures in formation, cerrado, catinga, among others vegetation types, where any local rainfall fluctuation, increases the soil moisture, transforming what once was sparse vegetation, or almost without any vegetation, into a vast green carpet. The mangroves systems are extreme environments, of very high salinity, frequently inundated by tides and stablished in anoxic substratum condition. These highly specific conditions makes the mangrove forests a robust example of resilient communities, which does not favor sudden phenological changes.

After completing the ZC sectorization and mosaic composition, the tasks of classification, validation and data dissemination are on the queue, figure 4.

All data produced by Mapbiomas is categorized as "Collections", in such way that the subsequent collection is always an improvement of the previous one. A total of 3 Collections are to be produced, where the third Collection is the most robust and complete of all. The preparation of Collections 1 and 2 will be minutely explained in the following chapters.

Figure 4 – General processing flow chart

3.2 COLLECTION 1

3.2.1 Algorithm

In the collection 1, the first collection of the project, the coastal zone was classified with the objective of discriminating forests and non-forest vegetation. For this purpose, NDFI (Normalized Differential Fraction Index) and fraction images obtained through SMA (Spectral Mixture Analysis) were the main source of information.

The SMA simulates the physical process of measuring the radiation mixture for each element contained in the image resolution cell as a linear mixing model (Adams et al., 1993). The surface reflectance images are decomposed into 4 fractions: shade, soil, green vegetation and non-photosynthetically active vegetation. These fractions represent the linear combination of a finite number of spectral signatures of the original components of the Earth's surface (Adams et al., 1993).

The fractions obtained through the reflectance image are bases for the elaboration of NDFI (Normalized Difference Vegetation Index) as proposed by Souza Jr. et al. (2005). NDFI helps differentiate native forest from forest degradation in a single synthetic band with values ranging from -1 to 1 where intact forests values are close to 1 and degraded areas close to -1.

In Collection 1, the parameters that define a class, whether forest or non-forest, are customized through a platform called "MapBiomas Workspace", Figure 5. It is through this tool that the MapBiomas team accesses the Google tools Earth Engine, without the need to enter any programming code line.

To visit the Workspace platform visit http://workspace.mapbiomas.org

Figure 5 – Mainframe of the MapBioma Workaspace interface.

Within Worksapce the parameters customization, whether for classification or for the construction of cloud-free mosaics, adopts the cartographic concept of division to the millionth. Thus, every biome or transversal theme used in the Workspace must be individually adjust its thresholds based on each chart that makes up the biome or transversal theme. In such division, the Coastal Zone is framed as 91 charts along Brazilian coastal territory, figure 6.

Figure 6 - Zona Costeira é enquadrada em 91 cartas ao longo do território costeiro do Brasil.

3.2.1.1 Cloud Free Composites. In Worksapce the creation of a mosaic free of clouds and shadows, occurs by defining

a set of parameters guided by a graphical interface. The year, YYYY, initial period (t0) and final period (t1, in DDMMYYY format), the maximum cloud coverage and the sensor, figure 7 are the mosaic composition parameters to be set. This set of parameters is used In Landsat data processing with the objective of generating a temporal mosaic with the lowest possible cloud cover, within a period of higher spectral contrast of the mapped classes.

Computationally, this step is described by the code below;

Subsequent to this stage, probable cloud and shadow residues are removed. For this, a cloud detection and removal algorithm based on Spectral Mixture Analysys (SMA), aided by the Landsat thermal bands (Band 6 for L5 and L7, and Band 10 for L8) is applied, all converted to Celsius temperature scale).

All pixels with a cloud fraction greater than or equal to 10% and a temperature less than or equal to 22 degrees Celsius are initially detected as a cloud. A 10-pixel buffer is generated to allow the detection clouds and its associated shadows. In these areas the percentage of cloud fraction in the pixel decreases, and for this reason, all pixels in the cloud surroundings with values greater than or equal to 7% of cloud fraction are added to cloud shadow mask.

Figure 7 – Workspace Cloud Free Mosaic main interface

Figure 8 – Collection 1 Input data, as described in GEE Java Script.

3.2.1.2 Parameters Extraction

The Collection 1, in addition to the Landsat surface reflectance values, relies on spectral indexes extracted from fraction images. To this end, SMA (Spectral Mixture Analysis) assumes that the reflectance of each pixel results from the linear combination of the percent product of each pure component (i.e., pixel ratio) as the basis of the equation:

𝑹𝒃 = 𝑹𝒊,𝒃 + 𝜺𝒃 (1)

𝚺𝑭𝒊 = 𝟏 (2)

Where, Rb is the reflectance in band b, Ri,b is the reflectance of the pure component i, in band b, Fi is the fraction (or proportion) of the pure component i, whose sum is 1 (or 100%), and εb Is the residual error of each band. The SMA (Spectral Mixture Model) is solved to estimate the Fi values (ie, the fraction or proportion of the pure component), thus obtaining the fractions for Vegetation, Non-Photosynthetically Active Vegetation, Soil and Cloud, which are used to calculate the NDFI (Souza Jr et al., 2005). The NDFI is defined by the equation below:

𝑁𝐷𝐹𝐼 = 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛8 − (NonPhotosyntheticallyActiveVegetation + 𝑆𝑜𝑖𝑙)𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛8 + NonPhotosyntheticallyActiveVegetation + 𝑆𝑜𝑙𝑜

Where,

𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛8 = 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛100 − 𝑆ℎ𝑎𝑑𝑒

and,

𝑆ℎ𝑎𝑑𝑒 = 1 − 𝐹𝑖 In MapBiomas, the applied SMA uses a spectral library defined by five pure

components: Vegetation, Non-Photosynthetically Active Vegetation, Soil, and Cloud. In Figure 9, below, we can observe the spectral curve for the Forest class.

Figure 9 - Spectral curve of the Forest class as in Landsat 5.

Computationalally, this step is described by the code below, figure 10;

Figure 10 –Java Script to for endmembers collection and generation of fraction images.

3.2.1.3 Classification

The SMA fraction images are used Collection 1 land cover classification, including the following classes; Forest, Non-Forest, Water and Cloud. The classification is driven from an empirical decision tree, figure 11.

Figure 11 - Empirical decision tree, having NDFI, GV and GVs as parameters to map the Forest and Non-Forest classes. Water and Cloud masks are applied to compose the final map. The variables (V1, V2, V3, V4) can be individually adjusted for each chart.

Initially, the cloud mask is previously described, extracting cloud residues that may have not been removed during the temporal mosaic generation. Subsequently, water mask is applied based on the following criteria:

- Pixels with shade fraction greater than 75%,

- Vegetation lower than 10%

- Soil less than 5%, provided it is not in relief shading areas (detected via Hill Shadow model and available lighting available in Landsat metadata).

Finally, the empirical decision tree is applied, Figure 9, to map the Forest and Non-forest areas. The Cloud and Water masks are combined with the resulting classification to generate the final map.

Computationaly this task is described as follows;

Figure 12 – Classification based on empirical tree definition.

3.2.1.4 Temporal Filter

The previously generated land cover classes shall pass through spatial and temporal filters. The spatial filter segments and indexes Forest, Non-Forest, and Water Classes in contiguous regions that are subsequently identified and reclassified based on the following criteria: Areas less than or equal to a half hectare (approximately 5 pixels) are reclassified based on the majority of its neighboring classes. Thus, for example, a segment of the Non-Forest class, up to 5 pixels, is first identified regardless of its neighboring pixels. Then the temporal filter may reclassify this group of Forest pixels into the predominant class value of its neighboring pixels. This process is applied to the entire classification (i.e. Forest, Non-Forest and Water).

The next step is to use temporal information to identify not-allowed class transitions between consecutive years. For example, if a pixel is consecutively classified as NF, F, F, F, the time filter will reclassify Year 1, shifting it from Non-Forest (NF) to Forest (F), as such

transition, non-forest to forest in a single year is vertically impossible. Table 1, shows all the temporal rules available.

Table 1 – Collection 1 Temporal Filter. This tables show all the invalid trajectories and how they are filtered out.

Rules Input(Year) Output Gerais ti ti+1 ti+2 ti+5 ti ti+1 ti+2 ti+3RG-1 NF F F F F F F FRG-2 NF F NF NF NF NF NF NFRG-3 NF SI * * NF NF * *RG-4 NF A NF NF NF NF NF NFRG-5 F SI * * F F * *RG-6 F A F F F F F FRG-7 F NF F F F F F FRG-8 A F F F F F F FRG-9 A NF NF NF NF NF NF NFRG-10 A * A A A A A ARG-11 A SI * * A A * *RG-12 * NF SI * * NF NF *RG-13 * F SI * * F F *RG-14 * A SI * * A A *RG-15 * * NF SI * * NF NFRG-16 * * F SI * * F FRG-17 * * A SI * * A AEspecíficas 2012 2013 2014 2015 2012 2013 2014 2015RE-1 NF NF F NF NF NF NF NFRE-2 NF NF A NF NF NF NF NFRE-3 F F NF F F F F FRE-4 F F A F F F F F

3.3 COLLECTION 2

3.3.1 Algorithms In comparison to Collection 1, this collection underwent profound transformations.

Collection 2, is natively built on GEE. This means that all of its work chain, except for validation (which occurs on an independent platform, WebCollect), is now built directly into the GEE itself. The stages of mosaic construction, parameterization, classification, spatial and temporal filtering received its own algorithms, natively executed through GEE console, dropping the use of the Workspace Platform, which reduced the number of user dependent tasks and also accelerated some of the computational processes by customizing the algorithms to benefit from the coastal zone peculiar characteristics.

In addition, Collection 2, counts with an spectral index with the specific purpose of highlighting dense wet vegetation’s, such as mangrove forest, the WVI - Wet Vegetation Index (in the publication phase), is now consolidated. Constructed by the integration of the Normalized Vegetation Index (NDVI) and the MNDWI (Modified Normalized Difference Water Index), the WVI showed tremendous potential in discriminating Mangrove Forests from other surrounding vegetation.

All algorithms are built in Java Script language, directly on the GEE Code Editor, figure 13, For more information visit https://developers.google.com/earth-engine/.

Figure 13 - Diagram of components of the Earth Engine Code Editor atcode.earthengine.google.com

3.3.1.1 Cloud-free composites.

The mosaics, as far as possible, are designed to maximize the observable area of the Brazilian coastal zone. However, due to its conditional nature, this selection technique is directly affected by the scarcity of cloud-free orbital data. Thus, for a given region "R" (Coastal Zone), in a certain period of time T (January to December of each year), there may not be a

pixel that satisfies the cloud-free condition, thus this position in the mosaic is represented by absence of pixel, resulting from the non-fulfillment of the selection assumptions.. Computationally the creation of the mosaic, figure 14, is given by:

Figure 14 – Script para remoção de nuvens e sombras aplicado sobre a Coleção 2 da Zona Costeira.

Differently from Collection 1, in here a hybrid approach where taken, joining the "simple cloud score", for cloud filtering, and the "fmask" data, for shade filtering. Such approach allows greater flexibility to the final mosaic, removing clouds and their shadows, without the removal of beach pixels or Dunes, one of the Coastal Zone mapping targets. Figure 15 shows some of the mosaics produced for the 2000 to 2016 range.

Figure 15 – Cloud Free Mosaic examples, from the left to the right, 2000, 2005, 2010, 2015

3.3.1.2 Parameters Extraction.

Any spectral index, or combinations thereof, can be calculated directly inside the 'Code Editor'. All indices in this Collection are calculated as shown in Figure 16. Due to the WVI “under publication” nature its mathematical formulation where left off this chapter. Although it will be dully presented on the Collection 3 documentation.

Figure 16 – rescaled calculation of NDVI and MNDWI. Note the term “+ 100” and multiplicative “ * 100”, at the beginning and end of the expression.

All indexes used during attribute extraction, NDVI, MNDWI and WVI, had their original mathematical range, from -1.0 to 1.0, rescaled for the integer and positive range, ranging from 0 to 200. The rescaled matrix is obtained from the equation p_res = (p * 100) + 100, where p is the pixel value in float 32 bits and p_res is the rescalonetd pixel value converted to unsigned 8-bit format. The rescaling converts the arrays to a data type that requires four times less memory space and disk storage. In addition, it maintains the level of detail necessary this mapping methodology, since the information of the values within two decimal places are maintained, allowing to perform the inverse rescale operation without large loss of information.

Figure 9 shows a

sample of each index, in Guanabara Bay, Rio de Janeiro.

Figure 17 – In the top left, NDVI, followed by MNDWI. In the bottom left, greyscale WVI and finally the color ramped WVI. Green represents Mangrove Forests, magenta for Not Vegetation, yellow for Non-Mangrove Vegetation and blues as Water.

3.3.1.3 Classification

In Collection 2, the CZ is classified with the objective of identifying the classes; Mangrove, Natural Non-Forest Formations, Natural Forest Formations, Not Vegetation, Not Observed, Water, Beaches and Dunes. Each class is identified with a integer-positive numeric ID, as follows Mangrove, 5, Water Bodies, 26, Dunes and Beaches, 23, Not Vegetation, 25, Non Forest Formations, 13, Natural Forest Formations, 2, Not Observed, 27, Figure 18.

Figure 18 – Classes and its ID’s

The applied classification is supervised, based on a binary decision tree structure. The decision is supported by spectral indexes extracted from Landsat images, with TOA correction - Top of Atmosphere, cases of: NDVI (Normalized Difference Vegetation Index), MNDWI (Modified Normalized Difference Water Index), and WVI (Wet Vegetation Index), the latter being an index, not yet published, created with the specific purpose of facilitating the identification of mangrove forests..

Moreover, TOA data is transformed from RGB to the HSV system, where the variables Hue (H) is used to map the water bodies, in a similar approach to Pekel JF et al., 2016, and the variable (V) to better discriminated not vegetation targets from beaches and dunes formations.

At this point, it is important to remember that the Brazilian coastal region runs through various atmospheric systems, with less or greater influence of cloudiness, presents different geological characteristics (Domingues et al., 2009), structural and environmental (Schaeffer-Noveli et al., 1990). Thus, due to this heterogeneity, the CZ is divided into 7 distinct sectors, figure 2. Each sector is classified by a decision tree of the same structure, but with the values of its nodes being slightly different. The fluctuation of the values of each node is a reflection of the heterogeneity coastal zone itself, as well as of the non-homogeneity of the TOA data over large time interval, in here 365 days (interval used in the construction of the cloud free mosaic). Figure 11 shows the decision tree applied to Sector 3 (PAMA).

3.3.1.4 Temporal Filter Applied over the classified products, the temporal filter sets off a group of transition

rules, which identifies and reclassifies not allowed temporal transitions of any given class. In this way, it is possible to remove cloud residues, reduce the effects of stripes for the operation of the SLC sensor, as much as correcting the impossible transitions.

Table 2 shows, individually, all the set of rules corrected by the temporal filter.

Table 2 – The table shows how the temporal filter operates, as demonstrate by the before and after columns. The acronyms represent; F = Forest, MG = Mangrove Forest, NFV = Not Forest, PD = Beaches and Dunes, NV = No Vegetation, AG = Water, NO = Not Observed.

Rule ClassPreFilter ClassPosFilterR01 MG-NFv-MG MG-MG-MGR02 MG-PD-MG MG-MG-MGR03 MG-NV-MG MG-MG-MGR04 MG-AG-MG MG-MG-MGR05 MG-NO-MG MG-MG-MGR06 MG-F-MG MG-MG-MGR07 NFv-MG-NFv NFv-NFv-NFvR08 PD-MG-PD PD-PD-PDR09 AG-MG-AG AG-AG-AGR10 NV-MG-NV NV-NV-NVR11 NO-MG-NO NO-NO-NOR12 NO-AG-NO NO-NO-NOR13 NO-NV-NO NO-NO-NOR14 NO-F-NO NO-NO-NOR15 NO-PD-NO NO-NO-NO

R16 NO-NFv-NO NO-NO-NOR17 F-MG-F F-F-FR18 F-NFv-F F-F-FR19 F-PD-F F-F-FR20 F-AG-F F-F-FR21 F-NV-F F-F-FR22 F-NO-F F-F-FR23 NFv-PD-NFv NFv-NFv-NFvR24 NFv-AG-NFv NFv-NFv-NFvR25 NFv-NV-NFv NFv-NFv-NFvR26 NFv-NO-NFv NFv-NFv-NFvR27 NFv-F-NFv NFv-NFv-NFvR28 PD-NFv-PD PD-PD-PDR29 PD-AG-PD PD-PD-PDR30 PD-NV-PD PD-PD-PDR31 PD-NO-PD PD-PD-PDR32 PD-F-PD PD-PD-PDR33 AG-PD-AG AG-AG-AGR34 AG-NFv-AG AG-AG-AGR35 AG-NV-AG AG-AG-AGR36 AG-NO-AG AG-AG-AGR37 AG-F-AG AG-AG-AGR38 NV-NFv-NV NV-NV-NVR39 NV-AG-NV NV-NV-NVR40 NV-PD-NV NV-NV-NVR41 NV-NO-NV NV-NV-NVR42 NV-F-NV NV-NV-NVR43 MG-NO-NFv MG-MG-NFvR44 MG-NO-PD MG-MG-PDR45 MG-NO-AG MG-MG-AGR46 MG-NO-NV MG-MG-NVR47 MG-NO-NO MG-MG-NOR48 MG-NO-F MG-MG-FR49 NFv-NO-PD NFv-NFv-PDR50 NFv-NO-AG NFv-NFv-AGR51 NFv-NO-NV NFv-NFv-NVR52 NFv-NO-NO NFv-NFv-NOR53 NFv-NO-MG NFv-NFv-MGR54 NFv-NO-F NFv-NFv-FR55 PD-NO-MG PD-PD-MGR56 PD-NO-NFv PD-PD-NFvR57 PD-NO-AG PD-PD-AGR58 PD-NO-NV PD-PD-NVR59 PD-NO-NO PD-PD-NO

R60 PD-NO-F PD-PD-FR61 AG-NO-MG AG-AG-MGR62 AG-NO-NFv AG-AG-NFvR63 AG-NO-PD AG-AG-PDR64 AG-NO-NV AG-AG-NVR65 AG-NO-NO AG-AG-NOR66 AG-NO-F AG-AG-FR67 NV-NO-MG NV-NV-MGR68 NV-NO-NFv NV-NV-NFvR69 NV-NO-PD NV-NV-PDR70 NV-NO-AG NV-NV-AGR71 NV-NO-NO NV-NV-NOR72 NV-NO-F NV-NV-FR73 F-NO-MG F-F-MGR74 F-NO-NFv F-F-NFvR75 F-NO-PD F-F-PDR76 F-NO-AG F-F-AGR77 F-NO-NO F-F-NOR78 F-NO-NV F-F-NVR79 AG-MG-MG MG-MG-MGR80 NV-MG-MG MG-MG-MGR81 NO-MG-MG MG-MG-MGR82 NFv-MG-MG MG-MG-MGR83 PD-MG-MG MG-MG-MGR84 F-MG-MG MG-MG-MGR85 NO-NFv-NFv NFv-NFv-NFvR86 NO-PD-PD PD-PD-PDR87 NO-AG-AG AG-AG-AGR88 NO-NV-NV NV-NV-NVR89 NO-F-F F-F-FR90 MG-NFv-NFv NFv-NFv-NFvR91 PD-NFv-NFv NFv-NFv-NFvR92 NV-NFv-NFv NFv-NFv-NFvR93 F-NFv-NFv NFv-NFv-NFvR94 AG-NFv-NFv NFv-NFv-NFvR95 AG-PD-PD PD-PD-PDR96 MG-PD-PD PD-PD-PDR97 NV-PD-PD PD-PD-PDR98 NFv-PD-PD PD-PD-PDR99 F-PD-PD PD-PD-PDR100 MG-AG-AG AG-AG-AGR101 NV-AG-AG AG-AG-AGR102 NFv-AG-AG AG-AG-AGR103 PD-AG-AG AG-AG-AG

R104 F-AG-AG AG-AG-AGR105 AG-NV-NV NV-NV-NVR106 MG-NV-NV NV-NV-NVR107 NFv-NV-NV NV-NV-NVR108 F-NV-NV NV-NV-NVR109 PD-NV-NV NV-NV-NVR110 AG-F-F F-F-FR111 MG-F-F F-F-FR112 PD-F-F F-F-FR113 NV-F-F F-F-FR114 NFv-F-F F-F-FR115 MG-NO-NO NG-MG-NOR116 AG-NO-NO AG-AG-NOR117 PD-NO-NO PD-PD-NOR118 NV-NO-NO NV-NV-NOR119 NFv-NO-NO NVf-NVf-NOR120 F-NO-NO F-F-NOR121 AG-AG-MG AG-AG-AGR122 NV-NV-MG NV-NV-NVR123 F-F-MG F-F-FR124 F-F-NO F-F-FR125 NFv-NFv-NO NFv-NFv-NFvR126 PD-PD-NO PD-PD-PDR127 AG-AG-NO AG-AG-AGR128 NV-NV-NO NV-NV-NVR129 MG-MG-NO MG-MG-MGR130 NFv-NFv-MG NFv-NFv-NFvR131 NFv-NFv-PD NFv-NFv-NFvR132 NFv-NFv-NV NFv-NFv-NFvR133 NFv-NFv-F NFv-NFv-NFvR134 NFv-NFv-AG NFv-NFv-NFvR135 PD-PD-AG PD-PD-PDR136 PD-PD-MG PD-PD-PDR137 PD-PD-NV PD-PD-PDR138 PD-PD-NFv PD-PD-PDR139 PD-PD-F PD-PD-PDR140 AG-AG-NV AG-AG-AGR141 AG-AG-NFv AG-AG-AGR142 AG-AG-PD AG-AG-AGR143 AG-AG-F AG-AG-AGR144 NV-NV-AG NV-NV-NVR145 NV-NV-NFv NV-NV-NVR146 NV-NV-F NV-NV-NVR147 NV-NV-PD NV-NV-NV

R148 F-F-AG F-F-FR149 F-F-PD F-F-FR150 F-F-NV F-F-FR151 F-F-NFv F-F-FR152 NO-NO-MG NO-MG-MGR153 NO-NO-AG NO-AG-AGR154 NO-NO-PD NO-PD-PDR155 NO-NO-NV NO-PD-PDR156 NO-NO-NFv NO-PD-PDR157 NO-NO-F NO-PD-PDR158 NO-F-NFv F-F-NFvR159 NO-AG-NV AG-AG-NVR160 NO-NV-AG NV-NV-AGR161 NO-NV-F NV-NV-FR164 AG-NV-NO AG-NV-NVR165 NV-AG-NO NV-AG-AGR166 F-AG-NO F-AG-AGR167 F-NV-NO F-NV-NVR162 AG-NV-MG AG-NV-NVR163 NV-AG-MG NV-AG-AG

Figure 19 compares the classification of the coastal region of the State of Maranhao,

Sector 3, year 2010, before and after the application of the temporal filter. In this region, it is remarkable the effect of the temporal filter in smoothing out the effect of Landsat 7 stripes. This effect, marked by the appearance of noisy lines transverse to the direction of passage of Landsat 7, is a result of the malfunctioning of the Scan Line Corrector (SLC) system, a problem that permanently affected all ETM + sensor images.

Antes

Depois

Figure 19 – Maranhao State Coast. Above classification without temporal filter application. Below, post filter result.

4 VALIDATION STRATEGY The validation is the unique process held out of the GEE environment. For the purpose

of validation, the Web collect tool was developed. The Web collect is a tool implemented to evaluate sample points based on visual interpretation, having as reference the same Landsat mosaic used in the classification process. Three (3) different interpreters, with long experience in Landsat image interpretation and Coastal Zone mapping, evaluate each randomly distributed point.

For the Coastal Zone, a total of 800 points, per year, where randomly distributed inside its boundary. The user has to analyze and these 800 through the time-series, from 2000 until 2016, which makes a total of 12.800 points for the entire collection.

Figure 20 – Web Collect interface. In the top left, the view and statistical panel, in the top right, Classification Panel and in the bottom, the mosaic temporal variation panel.

5 CONCLUDING REMARKS Collection 2 presents great improvements in comparison to its predecessor collection. It

marks also a tremendous shift in methodological terms, once it leaves the “Workspace Platform” behind to operate almost natively inside the GEE environment.

Supported by the development of a variety of new algorithms, created to fit into Coastal Zone specific characteristics, the Collection 2 presents a more complex and diverse classification, closely matching the existent scientific literature reports.

At last but not least, new updates are yet to come. For Collection 3, a new set of algorithms are being developed and/or customized, such as; a new cloud/shadow filter (joining Simple Cloud Score and TDOM approach), random forest classification, endmember collector to feed Random Forest needs, improvement of temporal filter and others.

6 REFERENCES Endnote License Expired. Downloading Mendely to convert my Library