8/14/2019 Gis Sem 6

1/21

Technological Advances using Remote Sensing and GIS in Forestry Sector of India

In spite of an impressive history of scientific forest management in India, the high forest typediversity and vastness of forest resources have posed challenging gaps in forestry database.However, with the advent of Remote Sensing (RS) and Geographical Information System (GIS)hopes of bridging these gaps has arisen. The paper provides an overview on how enhanced

measurement of natural resources, facilitated by RS and GIS, may prove invaluable for forestmanagement, with emphasis on developments made during the last one decade. The paper alsohighlights the pivotal role of Forest Survey of India (FSI) in this respect. Forest resourcesdatabase needs to be set, run and updated at both divisional (basic unit of forest administration)and national levels in India. At the divisional level the forest officers are guided by working plan,the document that describes the divisions profile and prescribes action for future two decades. Itneeds periodic updating with all the relevant information of a division. At the national level anassessment of forest and tree cover is required for ascertaining the ecological, economic andeven social value the green cover carries. In 1980s, after National Remote Sensing Agency(NRSA) displayed the potential of forest cover mapping and assessment, FSI successfully builtcapacity for highly accurate RS based forest cover assessment. FSI was subsequently mandatedto make biennial forest cover assessment with remote sensing. With improvement in scale of interpretation and resolution of imagery, FSI has made significant progress in forest cover

mapping and assessment. In addition to forest cover mapping, remote sensing technology hasbeen extensively used of late to prepare classified Tree Outside Forest (TOF) maps depictingblock, linear and scattered patches of trees groups up to 0.1 ha. RS combined with GIS has alsobeen applied in a number of projects, particularly in the preparation of working plans (case studyof Mizoram), national forest type mapping, forest fire mapping, etc. Such spatial informationgenerated on maps is of immense value to the planner and policy makers at the district / stateand national level.The Global Positioning System (GPS) technology is now being usedextensively for field inventory. GPS in conjunction with RS and GIS has been used by FSI for implementation of suitable sampling design to carry out forest inventory in inaccessible areasalso.One of the key areas for dissemination of knowledge in the field of RS, GIS and GPS hasbeen training. FSI has been building the capacities of forestry personnel over the year in the fieldof remote sensing, GIS and GPS applications is forest resource management. FSI also helpsSFDs set up GIS cells, which in conjunction with FSI-trained personnel, further facilitatedissemination of these modern technologies in each state.In the final part, the paper seeks todelineate the areas of focus at national and state levels so that duplication of effort and resourcesis averted. The paper also suggests concrete steps that may be taken for further effective use inforestry of the technological advancements in geoinformatic techniques.

Application of Remote Sensing and GIS for forest fire susceptibility mapping usinglikelihood ratio model

ABSTRACTThis paper presents capability of remote sensing and Geographical Information Systems (GIS) toevaluate forest fire susceptibility. Forest Fire locations were identified in the study area fromhistorical hotspots data from year 2000 to 2005 using AVHRR NOAA 12 and NOAA 16 satelliteimages. Various other supported data such as soil map, topographic data, and agro climate was

collected and created using GIS. These data were constructed into a spatial database using GIS.The factors that influence to fire occurrence, such as fuel type and Normalized DifferentialVegetation Index (NDVI) were extracted from classified Landsat-7 ETM imagery. Slope andaspect of topography, were derived from topographic database. Soil type was extracted from soildatabase and dry month code from agroclimate data. Forest fire susceptibility was analyzed usingthe forest fire occurrence factors by likelihood ratio method. The results of the analysis wereverified using forest fire location data. The validation results show satisfactory agreement thesusceptibility map and the existing data on forest fire location. The GIS was used to analyze thevast amount efficiently, and statistical programs were used to maintain the specificity andaccuracy. The result can be used for early warning, fire suppression resources planning and

8/14/2019 Gis Sem 6

2/21

allocation.

1.0 IntroductionFire has been identified as one of the major threats causing the loss of forests in several states inMalaysia. According to Forestry Department of Peninsular Malaysia (JPSM) and Forest ResearchInstitute Malaysia (FRIM) statistics show that during the last 7 years (between 1992 and 1998),more than 1600 ha of peat swamp forests of Peninsular Malaysia have been destroyed by fire(Wan Ahmad, 2002). In the event of a prolonged spell without rain, and a lowering of the water table in the peat swamp forest, the organic layers becomes completely dry and is very prone tofire. Fires in these peat swamp forest create much more smoke per hectare than other types of forest fires and are difficult to extinguish. Therefore, the understanding of the areas at risk to fireneeds to be closer concentration in peat swamp forests. A precise evaluation of forest fireproblems and decision on solutions can only be satisfactory when a fire hazard zone mapping isavailable (Jaiswal et al, 2002).

Geospatial technology, including Remote Sensing and Geographic Information Systems (GIS),provides the information and the tools necessary to develop a forest fire susceptibility map inorder to identify, classify and map fire hazard area. Before, during and after disaster, the accuratesharing of information is important. Making the information available via the world-wide web,people can share information to assess the situation and make decisions. In this study we want todevelop and produce a forest fire hazard model and map for Sungai Karang and Raja MudaForest Reserve, Selangor (Peat Swamp Forest) using frequency ratio, which is a statisticalmodel.

2.0 Study AreaThe study area is located approximately between Upper Left (3 23 53.6E and 101 3 36.3N)and Lower Right (3 45 18.05E and 101 30 55.33N). The area located within the KualaSelangor District, northern part Selangor. The landuse at the study area is mainly peat swampforest, plantation forest, inland forest, scrub, grassland and ex-mining area. The landform of thearea ranges from very flat terrain, especially for the peat swamp forest, ex mining, grassland andscrub area, to quite hilly area for the natural forest ranging between 0- 420 meter above sea level.Based on Malaysian Meteorological Services Department, the temperature of northern part of Selangor is between 29 C to 32 C and mean relative humidity of 65% to 70%. The highest

temperature is between April to June while the relative humidity is lowest in June, July andSeptember. The rainfall about 58.6mm to 240mm per month was recorded in the study area(Tanjung Karang weather station provided by Malaysian Meteorological Services Department).

There is a high potential of danger of fire in the dry season especially in the peat swamp forestand plantation forest. Most of fires are caused by human activities, either due to carelessness or burning activities in crop plantations. On 1995 and 1999, fire was occurred in the peat swampforest area within the study area. Figure 1 shows Raja Muda Musa and Sg Karang ReserveForest, Selangor.

8/14/2019 Gis Sem 6

3/21

Figure 1: Sg Karang and Raja Muda Musa Forest Reserve, Selangor

3.0 Data using GIS and Remote Sensing

Accurate detection of the location of hotspots is very important for probabilistic forest firesusceptibility analysis. Recent advances in remote sensing, GIS and computer technologiesprovided an opportunity to assess and monitor the land cover changes in a near real time basis.NOAA AVHRR satellite data with a spatial resolution of 1.1 km at nadir was found to be extremelyuseful for national-scale assessment and monitoring of major land cover types (Giri & Shrestha,1996). Historical forest fire data were collected from satellite remote sensing NOAA AVHRR 12and NOAA 14 sensors for last 5 years. To assemble a database to assess the surface area andnumber of hotspots in the study area, a total of 112 hotspots were mapped in a mapped area of 616 km2. The imagery from Landsat-7 ETM of path 157 and row 058 acquired on 21 September 2001 was used in this study. The spatial resolution for Landsat-7 ETM was 30 meter x 30 meter.Fuel map were extracted from satellite imagery.

GIS data and ancillary data consist of biophysical and socio-economic variable is based on 1:

25,000 scale. Contour, administrative boundaries, water resources, settlement, transportationinfrastructure are based on the topographic map from Survey Department (JUPEM). Forest firereports have been collected from Forest Department Peninsular Malaysia (JPSM). Hotspotsprone areas, fire occurrence map, peat swamp map and soil maps have been acquired anddigitized. Socio-economic data such as population data and socio-economic data were obtainedfrom Statistical Department. Meteorological data such as temperature and relative humidity andFire Danger Rating System (FDRS) map were obtained from Malaysian Meteorological ServicesDepartment. Image processing was carried out using ERDAS Imagine 8.7 and PCI Geomatica9.0.

To apply the probabilistic method, a spatial database that considers forest fire-related factors wasdesigned and constructed. These data are available in Malaysia either as paper or as digitalmaps. The spatial database constructed is shown in Table 1.

There were six factors that were considered in calculating the probability, and the factors wereextracted from the constructed spatial database. The factors were transformed into a vector-typespatial database using the GIS, and forest fire-related factors were extracted using the database.A digital elevation model (DEM) was created first from the topographic database. Contour andsurvey base points that had elevation values from the 1:25,000-scale topographic maps wereextracted, and a DEM was constructed with a resolution of 20 m. Using this DEM, the slope angleand slope aspect were calculated. The soil map is obtained from a 1:100,000-scale soil map.Landsat-7 ETM, 30 meter x 30 meter resolution was used for extracting fuel map in the SgKarang and Raja Muda Forest Reserve, Selangor. Multi-parametric analyses or overlays was

8/14/2019 Gis Sem 6

4/21

carried out using GIS which severity zones and the prioritize was based on frequency ratioapproach. Land use and land cover data was classified using a Landsat-7 ETM image employingan unsupervised classification method and topographic map. The fuel type has been classifiedinto ten classes, such as peat swamp forest, mangrove, inland forest, rubber plantation,grassland, oil palm plantation, paddy, shrub, cleared land and unclassified (water bodies andurban) were extracted for fuel type mapping. Finally, the NDVI map was obtained from Landsat-7ETM satellite images. The NDVI value was calculated using the formula NDVI = (IR R) / (IR +R), where IR value is the infrared portion of the electromagnetic spectrum, and R-value is the redportion of the electromagnetic spectrum. The NDVI value denotes areas of vegetation in animage.

The factors were converted to a raster grid with 30 m 30 m cells for application of the frequencyratio model. The area grid was 2,418 rows by 1,490 columns (i.e., total number is 3033610) and112 cells had forest fire occurrences.

4.0 Methodology

4.1 Frequency ratio model and its applicationFrequency ratio approaches are based on the observed relationships between distribution of hotspot and each hotspot-related factor, to reveal the correlation between hotspot locations andthe factors in the study area. Using the frequency ratio model, the spatial relationships betweenhotspot-occurrence location and each factors contributing hotspot occurrence were derived. Thefrequency is calculated from analysis of the relation between hotspot and the attributing factors.Therefore, the frequency ratios of each factors type or range were calculated from their relationship with hotspot events as shown in Table 2. In the relation analysis, the ratio is that of the area where hotspots occurred to the total area, so that a value of 1 is an average value. If thevalue is greater than 1, it means a higher correlation, and value lower than 1 means lower correlation.

To calculate the Forest Fire Susceptibility Index (FFSI), each factors frequency ratio values weresummed to the training area as in equation (1). The hotspot susceptibility value represents therelative susceptible to forest fire occurrence. So the greater the value, the higher the susceptibleto forest fire occurrence and the lower the value, the lower the susceptible to forest fire

occurrence.

FFSI = Fr 1 + Fr 2 + + Fr n (1)(FFSI: Forest Fire Susceptibility Index; Fr: Rating of each factors type or range) The forest firesusceptibility map was made using the FFSI values and for interpretation is shown in Figure 2.

This study consists of development of Forest Fire Susceptible Map. Figure 2 shows flowchart of methodology.

8/14/2019 Gis Sem 6

5/21

Figure 2: Flowchart of methodology

5.0 Conclusion and DiscussionIn the present study, frequency analysis method was applied for the forest fire susceptibilitymapping for Sungai Karang and Raja Muda Forest reserve. The validations results show that thefrequency ratio model has predication accuracy of 3.52%. Here, the authors can conclude thatthe results of frequency ratio model had shown the best prediction accuracy in forest firesusceptibility mapping.

The frequency ratio model is simple, the process of input, calculation and output can be readilyunderstood. The large amount of data can be processed in the GIS environment quickly andeasily. Moreover, it is hard to process the large amount of data in the statistical package.Recently, forest fire susceptibility mapping has shown a great deal of importance for hazedetection and fire prevention in forest area. The results shown in this paper can help theconcerned authorities for forest fire management and mitigation. However, one must be carefulwhile using the models for specific mitigation. This is because of the scale of the analysis whereother forest fire related factors need to be considered. Therefore, the models used in the studyare valid for awareness so that necessary prevention measures can be taken during the time of forest fire. In this paper, Forest fire susceptibility map was developed to determine the level of severity of forest fire hazard zones in terms of mapping susceptibility to fire by assessing therelative importance between fire factors and the location of fire ignition

Application of Satellite Based Remote Sensing for Monitoring and Mappingof Indias Forest and Tree Cover

IntroductionForests are ecological as well as socio-economic resource. These have to be managed

judiciously not only for environmental protection and other services but also for various productsand industrial raw materials. Considering the crucial role forests play in the countrys ecologicalstability and economic development, the current National Forest Policy (1988) in India aims atmaintaining a minimum of 33 percent of countrys geographical area under forest and tree cover.

8/14/2019 Gis Sem 6

6/21

This requires periodic monitoring of the forest cover of the country for effective planning andsustainable development. Forest Survey of India (FSI), an organization under the Ministry of Environment & Forests (Government of India) has been mandated to monitor and map countrysforest cover on a biennial basis. Consequently, FSI has been carrying out assessment of forestcover in the country using satellite based remote sensing data and has been publishing itsfindings in the State of Forest Report (SFR) every two years. Its first assessment of forest cover made in 1987 was published as SFR 1987 and the latest i.e., the eighth one as SFR 2001. Withthe improvement in satellite data resolution and adoption of digital image processing by FSI, ithas been possible to assess forest cover patches as small as 1ha. However, there exists asignificant tree cover wealth outside conventional forest areas, most of which is less than 1 ha inextent. These include small patches of trees in plantations and woodlands, or scattered trees onfarms, homesteads and urban areas, or trees along linear features such as roads, canals, bundsetc. and constitute significant area. In a study done in Haryana State by FSI in 1997, it was foundthat growing stock of trees outside forest was approximately seven times than that from naturalforests. Trees outside forests (TOF) are therefore considered an alternative but significant sourceof fuel, fodder, timber and environmental services to the local people. In 2001 assessment, FSIassessed tree cover (less than 1ha in extent) in the country using a stratified sampling and fieldinventory, and estimated it to be 2.48% of countrys geographical area. Thereafter, FSI hasdeveloped a methodology based on high-resolution satellite data for mapping and stratification of TOF leading to an improved sampling design for field inventory.

In the present paper, methodologies of forest cover and tree cover assessments as used by theFSI are discussed.

Forest Cover Assessment:Till recently, FSI was using mostly visual interpretation of satellite data on 1:250,000 scale for assessment of forest cover. However, in its latest assessment i.e., 2001 assessment, it useddigital interpretation of satellite data on 1:50,000 scale for mapping and monitoring forest cover.The present methodology uses Digital Image Processing software and involves the followingsteps:

Acquisition of satellite data: The digital data of IRS-1C and 1D LISS III is acquired from NRSA inCD.. India is covered in about 340 scenes, of IRS 1C and 1D. One scene covers an area of about

20000 km2

, having an overlap of about 10% with adjoining scenes. While procuring the data, careis taken to ensure that it is cloud free (with not more than 10% cloud cover) and therefore datapertaining to the period from October-December is preferred.

Geometric Rectification of raw data: After downloading the data into computer, rectification iscarried out in each image to provide Latitude and Longitude information into raw satellite sceneusing raster based geometric corrections. Rectification carried out in geographic projection is re-projected in shape of polygonal projection and the scene is geo-coded with using SOI toposheets.

Mosaicing of rectified scenes: Different scenes, which are already rectified, may have to bemerged together to get one combined FCC (False Colour Composite). FCC of sheet is extractedfrom mosaiced scene in a chosen area of interest. Image is displayed in three bands 3, 2, 1.

Masking of non-forest areas is done separately to extract forest areas on the basis of groundknowledge, cover map of previous cycles and on the basis of information available through SOItoposheets in the area of interest.

Classification of forest cover using NDVI: Interactive method of display is used for assigningthreshold values for each class (open, dense and scrub) on the basis of the ground knowledge tohighlight forest/vegetated areas. Density class of forest cover and colour is accordingly allocated.Survey of India toposheets is used for delineating boundaries of each district and classified mapof forest cover is generated.

8/14/2019 Gis Sem 6

7/21

Flow chart of methodology of dynamic forest cover mapping using remote sensing is shown infigure-1

Figure1- Flow chart of Forest cover mapping using remote sensing

The output includes forest cover maps on 1:50,000 scale. These maps show forest cover in threeclasses- (i) Dense forest, having canopy density of more than 40%, (ii) Open Forests with canopydensity between 10-40% and (iii) Scrub which are forest areas having less than 10% canopydensity. These maps are also generated for district and States/Union Territories by overlaying therespective District/State/UT administrative boundary. Area under forest cover atDistrict/State/country level is then assessed. Change maps are also prepared to depict changestaking place under different land cover classes.

In its latest assessment of 2001, taking advantage of advancements in remote sensing andimprovement in digital interpretation qualities, FSI has provided a much more comprehensivestatus of forest cover in the country than in the previous assessments. Some of the new featuresincorporated in this assessment are:

For the first time FSI has interpreted the satellite data of the entire country digitally. Inearlier estimates, interpretation has been largely visual. Digital interpretation has theadvantage of overcoming subjectivity prevalent in visual method.Due to absorption of digital image processing technique, it has been possible for FSI tointerpret the data on 1:50,000 scale. This has resulted in providing more realisticinformation on forest cover as areas having forest cover down to 1 ha could bedelineated while in earlier assessments, forest cover down to 25 ha could only bedelineated. Similarly blanks down to 1 ha within forested areas can be separated. Theentire exercise has resulted in new base-line information on forest cover.As perennial woody vegetation (including bamboos, palms, coconut, apple, mango etc.)has been treated as tree and thus all lands with tree crops, such as agro-forestryplantations, fruit orchards, tea and coffee estates with trees etc., have been included inforest cover.Mangrove cover has been classified into dense and open mangrove cover. The area of mangrove cover so assessed has been merged in the respective area figures of denseand open forest cover.

8/14/2019 Gis Sem 6

8/21

A classification is not complete unless its accuracy is assessed. For the first time anindependent and systematic assessment of accuracy of satellite data interpretation wasmade. An error matrix was generated by comparing classified forest cover with the actualforest cover on the ground at 3,608 locations spread throughout the country. Highresolution PAN data was used as proxy for ground verification. The overall accuracy of forest cover classification was found to be 95.9%.

Though forest cover in areas as less as 1 ha in extent could be assessed using satellitedata, significant tree cover exists in patches of less than 1 ha and in linear shapes alongroads, canals, etc. and scattered trees that can not be assessed using remote sensing.An attempt is made for the first time to assess such tree cover using ground inventorymethod.

The abstract of forest cover assessment 2001 is given in Table 1.

Table 1: Forest Cover as per 2001 assessment

Class Area (km 2) Percent of Geographic Area

Forest Cover

a) Dense 416,809 12.68

b) Open 258,729 7.87

Total Forest Cover* 675,538 20.55

Non-forest

Scrub 47,318 1.44

Total Non-forest** 2,611,725 79.45

Total Geographic Area 3,287,263 100.00*includes 4,482 km 2 under mangroves (0.14 percent of countrys geographic area)

**includes scrubForest Cover Assessment 2001

Figure 2- Forest cover in India

Tree Cover Assessment:TOF is assessed in rural as well as urban areas, although the greater part exists in rural areas.

Initially, conventional field method was used for TOF assessment in rural areas. The state or agroup of districts is considered as the study area. Since this area is fairly large there is everypossibility of heterogeneity of the study variable i.e. growing stock. TOF being planted along withagricultural crops is likely to be influenced by the Agro-ecological variables. Therefore, study areais stratified according to agro-ecological zones (AEZ), which has already been demarcated byother agencies. Districts, in India, are the basic planning and administrative units, which influencethe TOF and therefore, is considered for further stratification of AEZs. Villages are treated assampling units. Optimum number of sample villages is selected randomly from different districtsproportionate to the TOF area of the same. Complete enumeration of all the trees with diameter of 10 cm and above at breast height in the randomly selected villages in each district is carried

8/14/2019 Gis Sem 6

9/21

out. Data is collected on pre-designed formats following prepared instructions for fieldwork andcollected data is processed following appropriate formula. The above-mentioned methodologywas providing accurate estimates but was very time consuming. It was not able to provide preciseinformation at district level, which is the basic unit for economic planning.

Methodology Using Remote Sensing DataTo do away with these constraints many alternatives were tried and finally a methodology basedon digital image processing and GIS analysis using multi spectral and panchromatic data for mapping of trees outside forests (TOF) was devised. The remote sensing data is used to providestratification of the TOF resources, which is utilized to increase the precision and is time effective.In addition, sometimes the objectives of TOF resource assessment may require spatialdistribution of resources on maps along with several other features. This objective can also beappropriately tackled by this methodology.

High-resolution satellite imageries provide information even up to identification of a single tree butthese are cost prohibitive. The IRS LISS III data, which is multi spectral, and has a resolution of 23.5 m 23.5 m, provide information on vegetation cover. There are techniques available throughwhich tree vegetated land can be segregated from agriculture land if the tree vegetated patch isabout one ha and more. However, LISS data cannot be used for smaller patches or scatteredtrees. The IRS PAN data, which is monochromatic, having resolution of 5.8 m 5.8 m can identifya tree vegetated land even less than 0.1 ha. Therefore, both LISS III and PAN imageries are usedfor stratification of TOF resources on the basis of geometrical formation of trees i.e. blockplantation (group of trees), linear plantation and scattered trees.

Raw images of IRS IC/D PAN and LISS III data for the period between Oct.-Dec. 2002 areacquired from National Remote Sensing Agency, Hyderabad. Thereafter, the PAN image isgeometrically rectified with the help of Survey of India toposheets on 1:50,000 Scale. The LISS IIIimage is then co registered with the rectified PAN images. PAN and LISS III images are fused

8/14/2019 Gis Sem 6

10/21

using appropriate algorithm. Since mapping of TOF areas is the objective, the boundary of forestarea is digitized and masked out. The remaining fused image are classified into settlement, water bodies, burnt areas, tree cover and agriculture area using appropriate classifier viz. Maximumlikelihood. This classification enables the interpreter to distinguish between tree cover and other classes on fused image. This classified image is visually analyzed with respect to fused imagesfor editing and refinement for inclusion and omissions. Since a cluster of trees having 0.1 ha areaor more is defined as block plantation, pixels are clumped and cluster of pixels having area lessthan 0.1 ha are eliminated. After editing of the classified image the final classified map isgenerated which is done by taking the PAN, LISS-III and the fused images. Incorporating thesecorrections final classified image is prepared having three classes in TOF areas, namely, Block,Linear and Scattered. From the classified TOF map information pertaining to area under Block,Linear, Scattered and water bodies can be calculated. In addition, such areas, which do notsupport tree vegetation, like rivers and water bodies, snow covered mountains, marshes,etc.which is termed as Culturable Non Forest Area (CNFA)can also be calculated. Such informationis very helpful for district level planning.

Flow chart of methodology of Tree Cover mapping using remote sensing is shown in Figure-3

Figure 3- Flow chart of methodology of Tree Cover mapping

Sampling MethodBesides generation of TOF maps, the information on block, linear and scattered patches can beused to estimate the number of trees and the corresponding volume (species wise) usingappropriate sampling design by laying out optimum number of plots randomly selected in every

stratum. Since the variability in each stratum is expected to be different demanding differentsample and plot sizes, pilot studies were conducted to ascertain these so that the variability of thestratum can be properly addressed. In this pilot study, 0.1 ha, 0.2 ha and 0.3 ha plots wereconsidered for Block Stratum. Similarly, strip of size 10 m 75 m, 10 m 100 m, 10 m 125 m,10 m 150 m, 10 m 175 m & 10 m 200 m were considered for Linear Stratum. For scatteredstratum plot of size 0.5 ha, 1.0 ha, 1.5 ha, 2.0 ha, 2.5 ha and 3.0 ha were considered for non-hillydistricts and 0.25 ha, 0.50 ha, 0.75 ha and 1.00 ha were considered for the hilly districts. Twentyconcentric plots in each stratum were randomly selected and data were recorded. After analysis itwas concluded that optimum plot size for Block, Linear and Scattered stratum are 0.1 ha, 10

8/14/2019 Gis Sem 6

11/21

125 m strip and 3.0 ha respectively for non-hilly districts and 0.1 ha, 10 125 m strip and 0.5 hafor hilly district. It was also concluded through pilot study that the sample sizes for Block, Linear and Scattered stratum are 35, 50 and 50 respectively for non-hilly districts and 35, 50 and 95 for hilly district.

Desired number of sample points was randomly generated in each stratum separately and thedata on pre decided variables were collected on designed formats, following Manual for Assessment for Trees Outside Forests (FSI, 2003). Thereafter, data processing is carried outfollowing appropriate formulae corresponding to sampling design. The following table indicatesthe results obtained with regard to stems/ha, total number of stems, volume/ha and total volumeof trees outside forests in rural areas of Gurdaspur district of Punjab, India. Likewise, similar results obtained from different districts spread across the country are aggregated to generatenational level figures (Table 2).

Table 2: District level estimates (Gurdaspur, Punjab, India)

Geographical Area 3,551 sq.km.

Urban Area 76.42 sq.km.

Forest Area 368 sq.km.

Water bodies 94.58 sq.km.

CNFA (Rural) 3,013 sq.km.

Stems / ha 18.5

Total Stems 5,563,798 (5.56 M)

Volume / ha 3.5 cu.m

Total Volume 1,054,577 cu.m(1.05 M cu.m)

Accuracy of ClassificationAny classification is not complete unless and until its accuracy is assessed. For the present study

the accuracy of classification was assessed by taking 53 points in block, 65 in linear and 65 inscattered stratum for a particular district. It is recommended that 50 or more points should belocated for ground verification in each class. The accuracy of this classification was high asevident from the following confusion matrix of Kapurthala district of Punjab state.

Table 3: Confusion Matrix

Block Linear Scattered RowTotalUsers Accuracy(%)

Block 41 0 0 41 100

Linear 0 63 0 63 100

Scattered 12 2 65 79 82

Column Total 53 65 65 183

Producers Accuracy(%) 77 97 100

Overall Accuracy = 92 %

ConclusionThe main objective of Forest survey of India in mapping and monitoring forest and tree cover of the country on a two-year cycle is to know the dynamic changes of forest resources in terms of

8/14/2019 Gis Sem 6

12/21

quantity and quality over a period of time so that appropriate planning and managementinterventions can be developed for their conservation and sustainable utilization. Remote Sensingbased forest cover mapping and monitoring adopted by FSI has proved to be cost and timeeffective over traditional forest resource monitoring. The methodology using digital imageprocessing and geographical information system, as explained above can be effectivelyemployed using multi spectral and high-resolution satellite imageries to stratify the TOF resourcesin such a way that the classification system of TOF resource remains valid. In addition, spatialdistribution of TOF resources on maps along with other features will provide information for planning and implementation and utilization of these resources in a sustainable manner. Since,this methodology enables resource-based stratification, it is expected to provide better estimatesof TOF resources than the one generated through field survey alone.

The Development of Forest Fire Forecasting System using Internet GIS andSatellite Remote Sensing

AbstractThe purpose of this study is to develop the most effective method for a forest fire forecasting insmall mountain through GIS and Remote Sensing. The study area was Young-chon areaincluding all of the Kyung-sang Province, Korea. GIS DB was constructed based on factors of

geographical and natural features those are necessary factors to forecast a forest fire. It wasclarified that satellite image and some spatial data is very effective for developing the GraphicUser Interface to forecast the forest fire using Internet GIS. In addition, the forest fire hazard areawas prevented and managed effectively.

IntroductionRecently the human lives, fortune and the ecosystem have been deadly threatened by the manycases of forest fire and it's huge size. Even though there is trip of extinguish equipment, the mainreason of this large sized forest fire is that there is limitation of traditional method and no morescientific way to predict these disasters.

In this paper, we present that the fatal damage by forest fire could be reduced if there are suitablepredictions and rapid provision against forest fire using Internet GIS. This Internet GIS modelingis the most perfect way for forest fire forecasting system because forest fire has a movement inboth in spatial and temporal.

CFFDRS(Canadian Forest Fire Danger Ration System) was developed for a prediction of forestfire in 1987 and GIMS(Geographical Information and Modeling System) was installed for amanagement of it by Kessell(1990). GIMS could assign a part by telling the shape of forest fire inreal time and help the managers of forest fire have best decision against these disasters. In 1993Gracia and Lee prepared a map for forest fire forecasting in Alberta after evaluating the maindanger factors of forest fire.

In Korea, Y.H Cheong(1989) studied about predicting the dangerous rate of oxidation and S.YLee(1990) found out the relationship between the factor of environment and temperature of forestfire . K.C Lee(1998) constructed the modeling of suitability analysis about forest fire extinguishwater tank using GIS .

The study purposes of this paper are as follows that the investigation into actual condition of forest fire in Young-chon area was first carried out and secondly constructed in to GIS DB.Danger index of forest fire was computed with the based on topographic and meteorologicalfactor in this area and evaluated the relationship between these factors and forest fire. Finally, thenetwork presentation system of that using Internet GIS, which is the main goal of this paper, wasinstalled.

Review on Physical Factors of Forest Fire in Study Area

8/14/2019 Gis Sem 6

13/21

In the view of season, the number of forest fire increase in the spring and the winter because it isvery dry and small amount of precipitation. 3,362 cases took places between 1990~1999 and thedamaged area was 13991.43ha. Among them 2,069 cases happened between March and Apriland 883 cases occured in the winter. The main reasons of forest fire in this area have beencomposed of accidental fire(44%), ditch burn up(22%), visit tomb accident fire(7%), playing withfire(5%), etc(23%). 49% cases of them took places around 2 p . m ~ 6 p . m, 38% took places 11a . m ~1 p . m, 9% took places 7 p . m ~ 4 a . m, 4% took places 5 a . m ~ 10 p . m in order.

In study area, 84.2% cases of forest fire happened from March. 21 to April 10.

The potential factors including aspect, elevation, slope, stream, vegetation, which can have aneffect on forest fire were extracted for probability analysis.

Aspect is related to the amount of sunshine. In general, the cases of forest fire occur in area of south more than in the area of north because a southern exposure has higher burning point.

Actually, more than 40% of forest fire happened in aspect of south area while it doesn't happen inthe other area. Therefore, the aspect is really related to forest fire. Comparing with previous forestfire, more than 90% cases of forest fire happened at 100m above the sea level. Most thesedisasters take places in lower area above the sea level. 65 % cases in entire forest fire occurredin between a slope of zero and a slope of twenty degrees. The rate of forest fire decreaseremarkably as slope increases.

Stream is regarded as an important role not only to extinguish forest fire but also to providemoisture toward plants. The area far from stream has higher dangerous factors. Especially, theroad can be immediate factor to forest fire because there are human beings.

Fuel, which is composed of the amount of precipitation, the humidity of air, the direction of thewind and temperature, is very related to season, and time.

In study area, the air is exceedingly dry in the winter and much precipitation is in the summer.Also, its north, west, east is consisted in a mountainous area, which is over 900 meters above thesea level. There are open field in its south area and cultivated fruit.

The Kum-ho river, which is joined with Nam-chun(Jaho-chun, Gokung-chun) and Buk-chun(Sinryung-chun, Gohyun-chon), is flowed in the middle of this study area. The size of this is919 and 69% in this area is forests and crop fields and 18% is cultivated area.

In 1999 the number of a broad-leaved tree was twice than the number of a needle-leaf tree whilethe number of both was same. By analyzing Landsat TM satellite image data, the classes of treesare consist of 55% of Coniferous, 30% of Deciduous, 15% of Mixture forest.

8/14/2019 Gis Sem 6

14/21

Figure 1. The location of study areaMaterials and MethodsIn this paper the classes of trees in this area were simply composed of Conifer, Deciduous, Mixed

forest, and Agriculture. And the dimensions of damage area, the classification of vegetation andland classification map were found out by Landsat images.

The spatial data including topographic map, geologic map and aerial photo was used to makeforest fire hazard index GIS DB. ERDAS IMAGINE 8.3 and Unix Arc/Info GIS tool for imageprocessing and spatial analysis are used and Map Object 2.0 and Visual Basic 6.0 for InternetNetwork are needed. Virtual GIS is applied to realize forest fire hazard index on 3D terrain.

Topographies of three areas, which are called Hawsan, Hawnam, Jungang in Young-chon city,are analyzed. More than 60% of forest fire happened in between a slope of zero and a slope of twenty degrees and in aspect of south and southern west. Places of those disasters occurredbetween 100m and 350m above the sea level and close to road, which is far from river.

Table 1. Forest fire Summary

Area Date Temperature Precipitation(mm) Damage(ha) Vegetation

A 1999/4/15 10.43 42.5 5 Conifer

C 1999/3/31 8.55 0 1.5 Conifer

G 1999/3/4 4.97 18.5 1.5 Deciduous

After analyzing above table1, the main factors which could affect forest fire, are needle-leaf treesthe aspect of southern west and humidity.

Forest fire hazard index could be extracted by using average data acquired from an observation

station based on three above factors and presented it in a contour line.

In general, predict modeling was used like density transfer, density regression, significanceregression, discriminate function analysis, logistic regression. In this research, logistic regressionwas considered most suitable analysis because it could compute difference of a variableenvironment between occurrence spot, in addition nonoccurrence spot and applied to undetectedarea yield probability.

Zi = 3.754 + 0.231(slope) + 0.324(elevation)+ 0.165(aspect) + 0.328(stream) +0.195(forest type) + -0.017(agricultural pattern) + -0.128(urban) + 0.030(road)+

8/14/2019 Gis Sem 6

15/21

0.872(rainfall)+ 0.652(sunshine)+0.713(moisture)

Pi = exp(Zi) / (1 + exp(Zi))

Figure 2. Hazard Map on Study Area

Forest fire hazard Index Forecast System using GIS

Development of Forecasting SystemThe purpose of this study is to develop the most effective method for a forest fire forecastingthrough GIS and Remote Sensing. In this study digital map was prepared and expressednumerically which includes factors of geographical and natural features, which are necessaryfactors to forecast a forest fire hazard index.

Fire potential requires collecting baseline vegetation information, daily to weekly monitoring of vegetation condition or vigor daily monitoring of weather conditions, and acquiring riskmanagement information.

A computer-based model is to predict wild fire behavior across time and space. The computer model uses fuel type, weather conditions, slope, aspect and elevation to predict the direction,speed, and burn intensity of a wild fire across various landscapes. The model uses GeographicInformation System (GIS) technology. The program is responsible for all the complexcomputations necessary for simulating fire behavior.

The model's user-interface is designed so advanced computer skills and GIS knowledge is notrequired to execute the model. Ease-of-use puts fire behavior prediction into the hands of firemanagers where it can be most effectively applied. With fire damage growing every year, firedepartments need better planning tools to minimize fire's impact.

The model is also a good analysis tool for resource managers. A Graphic User Interface (GUI)allows the user to easily specify and edit the data and parameters necessary to execute each

simulation. Forest fire Danger Index Presentation System would be useful to managers, policymakers and scientists interested in mitigating and evaluating the effects of forest fire. Real timeforest fire hazard information is offer to public welfare and administration business management.

8/14/2019 Gis Sem 6

16/21

Figure 3. Procedure of Study Frame ConclusionIn conclusion, the information of forest in the specific area can be easily searched, analyzed andmanaged through Internet GIS and Remote Sensing. It makes possible for this forest fireforecasting system to predict and to prevent forest fire in effective and scientific because it canassume exact forest fire hazard index in real time and present in proper time. Especially forestfire hazard index was presented in real time integrated with meteorological data through internetweb base to forest fire task officer and local citizens without time lagging. It also allows to analyzewith spatial modeling and monitoring in the predicted area. Therefore it was clarified that theforecasting system using Web based GIS is prominent for management and prevention of forestfire

Forest Burned Area Mapping by using SPOT Images

Gwo-Jern Hwant 1 Wen-Fu Chen 2

Keywords: remote sensing, forest fire, pseudo color images, aerial photograph, working circle,map overlay, geographic information system.

Abstract The objective of this research is to apply remote sensing technique to investigate themethodology of mapping forest fire burned area. SPOT HRV were used to compare with thepanoramic aerial photo mapped forest fire burned areas in this research. The areas included Yu-Shan working circle and Hsiu-Ku-Luan working circle in Taiwan. Each was independently

8/14/2019 Gis Sem 6

17/21

interpreted and the results were compared and analyzed by map overlay utilized geographicinformation system software Arc/Info. The resulted precision rate of calculated burned interpretedfrom aerial photograph was 98.1%. It is worthwhile.

1. Technician of, Bureau of Forestry of Taiwan Province, ROC2. Prof. of National Chung-Hsing Univ. Tai-Chung, Taiwan, ROC

IntroductionForest fire affects ecology seriously, it will break even erode the soil. The most important thing wemust to do during the forest is being fired are fire fighting and the firing area control. In order toprevent forest fire, Taiwan Forestry Bureau has used various method of fire prevention broadcastand taken fire-prevention workshops to remind and warm the fire fighter of hot-spot location tocheck the safety during dry season. On the other hand, in order to enhance the forester to learnvarious fires fighting skill, the Forest Bureau evaluate the work at the end of year.

The Forest Bureau was called by the Police Team of Yu-San National Park that a severe forestfire occurred at the connecting zone of the 51st compartment of Yu-San working circle in Yu-SanNational Park and the 15th compartment of Hsiu-Ku-Luan working circle at noon of 4.1.1998. Theflight team was then commanded to take picture above the burning sites. Finally, was judged the

fire reached the 51st and 53rd compartment of Yu-San working circle as well as the 15th and16th compartment of Hsiu-Ku-Luan working circle. The burned area was 287.78 ha. Most of theforest fire burned area was grass land and habitat alpine sassos site and there were only fewweed under Pinus Taiwanensis.

Science the Remote Sensing Development Planning Committee of Administration of Economicwas erected in 1976.8. the research and application of remote sensing technique has started inTaiwan. There are four kinds of resolution in remote sensing multi-spectral bands: spatial,spectral-temporal and radiation etc., and those afford advantageous selection in application, sothe remote sensing technique always be used on forest resource surveying and forest firedistribution surveying of large scale.

The purpose of this research is to utilize the SPOT images, which were taken on the forest fireburned sites at Ah-Sue-Ku Mountain area in 4. 4. 1998. The results was compared and checkedwith the aerial photographs of the same sites, which were taken in 4. 3. 1998. According to theaccurate areas, which were estimated from the satellite remote sensing, we can help to afford theauthority a more correct and quick method on estimating the area of the burned sites.

Material and Method

1. Material

1. Study SitesHsiu-Ku Luan circle is located in Hua-Lien Forest District Office with the area of 70,429.23 ha. The space is very vast and the elevation distributes in a big range. On thehigh elevation distributes in a big range. On the high elevation site, there grow pure cropof Pine Tree with some Hemlock, Formosan Douglas Fir and grows grass near the ridgeof Central Mountain. On the moderate elevation site, the mixed crop of ChamaecyparisFormosensis and Hemlock grow very well. On the low elevation site, there growshardwood forest only and most of the expensive Cinnamomum Randaiense growspiecemeal at those points that the traffic is not convenient.

Yu-San working circle is located in Gia-Yi Forest District Office with the area of 49,647.77ha. The lowest elevation of this working circle is 250 m at Dah-Pu working circle and thehigh elevation is 3,952m at the main peak of Yu-San Mountains. It forms a vertical zone

8/14/2019 Gis Sem 6

18/21

distribution from Tropical Zone, Warm Zone, Temperate Zone and then Frigid Zone. Theconstitution of forest species and the forest types are very complex.

2. SPOT Images DataThe SPOT HRV multispectral and panchromatic level-10 images with resolution of 12.5mx 12.5m and 6.25m x 6.25m.

3. Land Cover Map:Photographic Base Map (1/5,000 and 1,10,000 scale),Compartment Base Map (1/5,000 scale).

2. Method

1. Delineating with Aerial Photographic Base Map.The Forestry Bureau assigned the burned sites and then we collected the relative mapdata. Then taken aerial b/w and color photographs were then checked with thestereoscopes and interprets the burned area manually with photographs and finally todelineate the area boundary. After we finished interpretation with aerial photographs weselected the control points. Finally, after mapped the interpreted boundary on the circlephotographic base map (1/5,000 scale) we plotted the burned area and calculated thearea after digitized it. To those susceptible points we visited the field sited to check them

and then remapped the burned area.2. SPOT Satellite Image ApplianceA. A High-Resolution Pseudo Synthetic Images Making.

Firstly, We used the SPOT MSS images to resample as RGB with resolution of 6.25m and then transform the RGB to be ISH. Secondly, we combined thepanchromatic images to be three bands images and finally retransform ISH to beRGB and we got the high resolution pseudo color synthetic images

B. The Burned Boundary Mapping with Image EnhancementWe used GPS to assign the burned area of TM2 coordinate and then referred thecircle photographic base map to locate the burned area on the satellite images.Secondarily, we enhanced the pseudo color images with linear enhancementmethod and then smoothed it by operating with 3*3 matrix and removed thenoises. Finally, we used IMAGINE software to sharpen the edge with 5*5 matrixto figure out the boundary of the burned area obviously.

Results and Discussion

1. Results from Aerial Photographic InterpretationThe area of the burned area from the aerial photograph at 4.3.1998 was 287.78 ha. Thephotograph was shown in Figure 1.

8/14/2019 Gis Sem 6

19/21

Fig 1. BW Photograph Film of Forest Burned Area

The area of the 53rd compartment of Yu-Shan working circle and the 15th & 16thcompartment of Hsiu-Ku-Luan working circle were calculated and was given in table 1.

Table 1. The Burned Area of Yu-Shan Working Circle and Hsiu-Ku-Luan WorkingCircle (unit hectare)

Working Circle Comp. of Yu-Shan Comp. of Hsiu-Ku-LuanTotal

Situation 53rd comp. 15th comp. 16th comp.

Serious 7.64 0.03 ---- 7.67

Moderate 2.85 50.60 28.34 81.79

Light 0.02 47.49 150.81 198.32

Total 10.51 98.12 179.15 287.78

Comp.: compartment

The disaster zone of the 53rd compartment of Yu-Shan working circle and the 15th &16th compartment of Hsiu-Ku-Luan working circle was shown in figure 2.

8/14/2019 Gis Sem 6

20/21

Figure 2. The Map of Equal-class of Forest Fire Disaster

2. The Interpreted Results of the Satellite Images Figure 3 showed the SPOT image of theforest fire in 4.4.1998. The area, which was in black color, was the burned area and itwas equal to 164.98a. and the white color area was the smoke of forest fire

Figure 3. The interpreted forest fire map f rom satellite image.

Conclusion

The technique of aerial phtogrammetry has being broadly used on surveying of forestryand agriculture disaster for it can afford the necessary information to the relative instituteand spend less labor and cost. Although the information an be got from both aerial

8/14/2019 Gis Sem 6

21/21

phtotogrammetry and satellite images classification and they can help maturely, but incase of emergent disaster the satellite images can afford information more fast.

The resulted area was 287.78 ha from the forest fire photograph that was taken in4.3.1998. The accuracy would be up to the proficient training, experience, the quality andkind of photograph.

The calculated burned area 164.9ha was go from SPOT image. The precision rate was

98.1% by comparing to aerial photograph interpretation. The result was worthwhile. It will be a tendency for Taiwan forestry Bureau to integrate the technique of satelliteremote sensing, GPS and GIS on forest fire fighting and fire danger assessment.