Analisis Perubahan Penutupan Lahan dan Potensi Karbon di Taman Hutan Raya Pocut Meurah Intan, Aceh Indonesia

Perubahan penutupan lahan merupakan sektor penyumbang emisi gas rumah kaca terbesar, termasuk di dalamnya adalah pemanfaatan lahan. Analisis tutupan lahan menjadi bagian penting dalam menentukan jumlah potensi karbon yang tersedia. Penelitian bertujuan untuk menganalisis perubahan tutupan lahan dari tahun 2003 hingga 2018 dan menghitung potensi karbon di Taman Hutan Raya Pocut Meurah Intan dengan luas objek penelitian 6.215 ha. Penelitian dilaksanakan selama 5 (lima) bulan. Penelitian ini menggunakan metode stock difference, yaitu metode perhitungan luas tutupan lahan dan stok karbon pada dua titik waktu. Hasil penelitian menunjukkan bahwa perubahan luas tertinggi tahun 2018 seluas 263 ha dan terendah tahun 2009 seluas 108 ha. Lahan terbuka meningkat seluas 100 ha, pemukiman 81 ha, semak belukar 65 ha, pertanian lahan kering campur semak 32 ha. Sementara hutan lahan kering sekunder menurun 79 ha, hutan tanaman 76 ha, savanna 21 ha dan pertanian lahan kering 103 ha. Selama kurun waktu 15 tahun berdasarkan kelas penutupan lahan, cadangan karbon tertinggi pada tahun 2003 sebesar 656.053 ton, terendah tahun 2012 sebesar 620.992 ton. Laju serapan karbon tertinggi pada periode tahun 2015-2018 sebesar 94.615 ton CO2 dan terendah pada periode tahun 2009-2012 sebesar 1.981 ton CO2. Laju emisi tertinggi pada periode tahun 2003-2006 sebesar 79.559 ton CO2 dan terendah periode tahun 2006-2009 sebesar 9.069 ton CO2. Peningkatan serapan karbon diakibatkan oleh meningkatnya luas tutupan lahan pada hutan lahan kering sekunder dan adanya pemanfaatan lahan untuk pertanian lahan kering campur semak.ABSTRACTChanges in land cover are the largest contributor to greenhouse gas emissions, including land use. Land cover analysis is an important part in determining the potential amount of carbon available. The study aims to analyze changes in land cover from 2003 to 2018 and calculating the carbon potential in the Pocut Meurah Intan Forest Park with a research object area of 6,215 ha. The research was conducted for 5 (five) months. This research uses the stock difference method, namely the method of calculating land cover area dan stok karbon pada dua titik waktu. The results showed that the highest area change in 2018 was 263 ha and the lowest was in 2009 at 108 ha. Open land increased by 100 ha, settlement 81 ha, scrub 65 ha, dry land agriculture mixed with shrubs 32 ha. Meanwhile, secondary dry land forest decreased by 79 ha, plantation forest 76 ha, savanna 21 ha and dry land agriculture 103 ha. Over a 15 year period based on land cover class, the highest carbon stock in 2003 was 656,053 tons, the lowest was in 2012 at 620,992 tons. The highest carbon absorption rate in the 2015-2018 period was 94,615 tons of CO2 and the lowest was in the 2009-2012 period of 1,981 tons of CO2. The highest emission rate in the 2003-2006 period was 79,559 tonnes of CO2 and the lowest for the 2006-2009 period was 9,069 tonnes of CO2. The increase in carbon sequestration is caused by the increase in land cover in secondary dryland forest and the use of land for mixed dry land agriculture.

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Geospatial analysis of wetlands based on land use/land cover dynamics using remote sensing and GIS in Sindh, Pakistan

Science Progress ◽

10.1177/00368504211026143 ◽

2021 ◽

Vol 104 (2) ◽

pp. 003685042110261

Author(s):

Hamza Islam ◽

Habibuulah Abbasi ◽

Ahmed Karam ◽

Ali Hassan Chughtai ◽

Mansoor Ahmed Jiskani

Keyword(s):

Land Use ◽

Land Cover ◽

Vegetation Cover ◽

Vegetation Index ◽

Kappa Statistics ◽

Natural Ecosystem ◽

Land Use Land Cover ◽

Dry Land ◽

Lulc Changes ◽

Manchar Lake

In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.

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Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

SN Applied Sciences ◽

10.1007/s42452-021-04498-4 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Dereje Gebrie Habte ◽

Satishkumar Belliethathan ◽

Tenalem Ayenew

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Change Detection ◽

Catchment Area ◽

Environmental Conservation ◽

Land Use Land Cover ◽

Cultivated Land ◽

Plantation Forest ◽

Bare Land

AbstractEvaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

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Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

Jurnal Inotera ◽

10.31572/inotera.vol6.iss1.2021.id139 ◽

2021 ◽

Vol 6 (1) ◽

pp. 59-65

Author(s):

Safridatul Audah ◽

Muharratul Mina Rizky ◽

Lindawati

Keyword(s):

Land Use ◽

Land Cover ◽

Image Classification ◽

Satellite Image ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Sensing Technology ◽

Open Land ◽

Cover Class ◽

Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

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Perubahan Penutupan Lahan Kota Bogor

Talenta Conference Series Agricultural and Natural Resources (ANR) ◽

10.32734/anr.v2i1.572 ◽

2019 ◽

Vol 2 (1) ◽

pp. 44-51

Author(s):

Ina Lidiawati ◽

Ratna Sari Hasibuan ◽

Retno Wijayanti

Keyword(s):

Land Cover ◽

Rice Field ◽

Research Result ◽

Land Cover Changes ◽

Plantation Forest ◽

Geospatial Information ◽

Land Cover Map ◽

Cover Class ◽

The City

Pembangunan yang terjadi sangat pesat sehingga tutupan lahan di Kota Bogor berubah. Penelitian ini bertujuan untuk mengetahui tutupan lahan Kota Bogor yang berubah yaitu tahun 1996, 2006, 2016 dan faktor-faktor yang mempengaruhi tutupan lahan Kota Bogor yang berubah tersebut. Perubahan tutupan lahan Kota Bogor dianalisis menggunakan perangkat lunak Arc.GIS 10.2. Data yang digunakan sebagai bahan analisis adalah peta tutupan lahan Kota Bogor 1996, 2006 dan 2016 dari Kementerian Lingkungan Hidup dan Kehutanan (KLHK) dan peta Rupa Bumi Indonesia (RBI). Hasil dari penelitian ini adalah klasifikasi kelas tutupan lahan hutan tanaman kota Bogor, area terbuka, pelabuhan/bandara, pemukiman/lahan, pertanian kering, pertanian kering, semak, sawah, perkebunan, dan badan air. Pada tahun 1996 tutupan lahan didominasi oleh vegetasi, semak, dan semak-semak. Perubahan tutupan lahan yang paling masif terjadi pada kelas permukiman / tanah dengan luas 6.683 hektar pada tahun 2006 dan 7.532 ha pada tahun 2016. Diperkirakan bahwa luas lahan yang akan dibangun akan terus bertambah seiring dengan pertambahan populasi. Peningkatan populasi menyebabkan lebih banyak ruang untuk perumahan dan berbagai kegiatan, selain kondisi sosial ekonomi dan arah kebijakan pemerintah yang mempengaruhi tutupan lahan kota Bogor menjadi berubah. Development that occurred in the city of Bogor very rapidly causing land cover changes. This research purpose was to know the change of land cover of Bogor City in 1996, 2006, and 2016 and to know what factors influence the change of land cover. Changes in land cover in Bogor City were analyzed using Arc.GIS software 10.2. The data used as an analysis material were the land cover map of Bogor City 1996, 2006 and 2016 issued by the Ministry of Environment and Forestry and the map of Rupa Bumi Indonesia issued by the Geospatial Information Agency. This research result was the classification of a land cover class of Bogor city of plantation forest, open area, port/airport, settlement/land, dry farm, dry farm, shrub, rice field, plantation, and water body. In 1996 the land cover was dominated by vegetation, shrubs, and bushes. The most massive land cover change occurred in the class of settlements/land with an area of 6,683 hectares in 2006 and 7,532 ha in the year 2016. It is estimated that the area of land will be built will continue to grow as the population increases. The increase in population causes more space for housing and various activities, besides the socio-economic condition and the direction of government policy also affect the change of land cover in Bogor city.

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Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988–2018)

Sustainability ◽

10.3390/su10124433 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4433 ◽

Cited By ~ 21

Author(s):

Iman Rousta ◽

Md Sarif ◽

Rajan Gupta ◽

Haraldur Olafsson ◽

Manjula Ranagalage ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Heat Island ◽

Spatiotemporal Pattern ◽

Heat Island ◽

Land Use Land Cover ◽

Metropolitan City ◽

Open Land ◽

Urban Heat ◽

Normal Difference

This article summarized the spatiotemporal pattern of land use/land cover (LU/LC) and urban heat island (UHI) dynamics in the Metropolitan city of Tehran between 1988 and 2018. The study showed dynamics of each LU/LC class and their role in influencing the UHI. The impervious surface area expanded by 286.04 (48.27% of total land) and vegetated land was depleted by 42.06 km2 (7.10% of total land) during the period of 1988–2018. The mean land surface temperature (LST) has enlarged by approximately 2–3 °C at the city center and 5–7 °C at the periphery between 1988 and 2018 based on the urban–rural gradient analysis. The lower mean LST was experienced by vegetation land (VL) and water body (WB) by approximately 4–5 °C and 5–7 °C, respectively, and the higher mean LST by open land (OL) by 7–11 °C than other LU/LC classes at all time-points during the time period, 1988–2018. The magnitude of mean LST was calculated based on the main LU/LC categories, where impervious land (IL) recorded the higher temperature difference compared to vegetation land (VL) and water bodies (WB). However, open land (OL) recorded the highest mean LST differences with all the other LU/LC categories. In addition to that, there was an overall negative correlation between LST and the normal difference vegetation index (NDVI). By contrast, there was an overall positive correlation between LST and the normal difference built-up index (NDBI). This article, executed through three decadal change analyses from 1988 to 2018 at 10-year intervals, has made a significant contribution to delineating the long records of change dynamics and could have a great influence on policy making to foster environmental sustainability.

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Valuasi Dampak Banjir Di Kabupaten Landak, Kalimantan Barat

Jurnal Ilmu Lingkungan ◽

10.14710/jil.20.1.65-75 ◽

2021 ◽

Vol 20 (1) ◽

pp. 65-75

Author(s):

Gusti Rachmad Rabsanjani ◽

Aji Ali Akbar ◽

Henny Herawati

Keyword(s):

Land Cover ◽

Rainy Season ◽

Economic Valuation ◽

Economic Losses ◽

Land Cover Class ◽

Attribute Analysis ◽

Flood Disasters ◽

Open Land ◽

Cover Class ◽

Slope Class

Banjir merupakan becana yang kerap sekali terjadi pada musim hujan, banjir dapat menyebabkan kehilangan harta benda maupun korban jiwa. Valuasi ekonomi akibat terjadinya banjir adalah salah satu cara yang dapat digunakan untuk menghitung kerugian akibat terjadinya bencana banjir. Tidak adanya kajian mengenai kerentanan dan kerugian ekonomi akibat banjir pada tiga desa di Kecamatan Ngabang yaitu Desa Raja, Hilir Tengah dan Hilir Kantor adalah alasan dilakukannya penelitian ini. Tujuan dilakakukan penelitian ini adalah untuk mengidentifikasi dan menginventarisasi besarnya tingkat kerentanan terhadap banjir yang terjadi dan menghitung valuasi kerugian ekonomi akibat terjadinya bencana banjir. Metode yang digunakan dalam menganalisis kerentanan banjir menggunakan software ArcMap 10.3 adalah Analisa atribut meliputi pemberian skor kelas curah hujan, pemberian skor kelas tutupan lahan, pemberian skor kelas kemiringan lahan, pembobotan dan Analisa AHP. Metode yang digunakan untuk menghitung estimasi kerugian akibat banjir menggunakan metode ECLAC. Hasil yang didapat dalam penelitian ini adalah perubahan tutupan lahan mengalami penurunan dan peningkatan luasan permukiman, pertanian/sawah, dan lahan terbuka/semak, Curah hujan yang tinggi dan kelerengan daerah yang landai menjadi parameter penyebab terjadinya banjir. Pada estimasi nilai kerugian akibat banjir dengan nilai kerugian menggunakan USD dan Emas pada tahun yang ditentukan dengan hasil total kerugian pada tahun 1973 adalah 73,7 Juta dollar, tahun 1989 180 juta dollar, tahun 1994 261 juta dollar, tahun 2000 261juta dollar, tahun 2010 1,1 miliar dollar, dan tahun 2020 1,9 miliar dollar.ABSTRACTFlood is a plan that often occurs in the rainy season, floods can cause loss of property and fatalities. Economic valuation due to flooding is one way that can be used to calculate losses due to flood disasters. The absence of studies on vulnerability and economic losses due to flooding in three villages in Ngabang Subdistrict namely Desa Raja, Hilir Tengah and Hilir Kantor is the reason for this research. The purpose of this study is to identify and inventory the level of vulnerability to floods that occur and calculate the valuation of economic losses due to flood disasters. The methods used in analyzing flood vulnerabilities using ArcMap 10.3 software are attribute analysis including rainfall class scoring, giving land cover class scores, awarding land slope class scores, weighting and AHP Analysis. The method used to calculate the estimated loss due to flooding uses the ECLAC method. The results obtained in this study are changes in land cover experiencing a decrease and increase in the area of settlements, agriculture / rice fields, and open land / bush, high rainfall and marbles of sloping areas become parameters of the cause of flooding. In the estimated value of losses due to floods with the value of losses using USD and Gold in the specified year with the total loss in 1973 was 73.7 million dollars, in 1989 180 million dollars, in 1994 261 million dollars, in 2000 261 million dollars, in 2010 1.1 billion dollars, and in 2020 1.9 billion dollars.

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APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w4-363-2018 ◽

2018 ◽

Vol XLII-3/W4 ◽

pp. 363-367 ◽

Cited By ~ 3

Author(s):

H. T. T. Nguyen ◽

T. M. Doan ◽

V. Radeloff

Keyword(s):

Land Use ◽

Random Forest ◽

Land Cover ◽

Deciduous Forest ◽

Landsat 8 ◽

Land Use Land Cover ◽

Landsat 8 Oli ◽

Plantation Forest ◽

Barren Land ◽

Random Forest Classification

Abstract. This study used the Random Forest classifier (RF) running in R environment to map Land use/Land cover (LULC) of Dak Lak province in Vietnam based on the Landsat 8 OLI. The values of two RF parameters of ntree (number of tree) and mtry (the number of variables used to split at each node) were tested and compared. In current study the best results indicate the number of suitable decision trees involved in the classification process is 300 (ntree), and the suitable number of variables used to split at each node is 4 variables (mtry). These parameters were used to classify 7 bands multi-spectral resolution from 1&ndash;7 of Landsat 8 into ten classes of LULC including natural broad-leaved evergreen, semi-evergreen, dipterocarp deciduous forest, plantation forest, rubber, coffee land, crop land, barren land, residential area and water surface. The overall accuracy of 90.32&thinsp;% with Kappa coefficient of 0.8434 was found in this case.

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A Review of Land Use Land Cover Change in The Catchment Area of Musi Hydropower Plant in Bengkulu Province

E3S Web of Conferences ◽

10.1051/e3sconf/202130504001 ◽

2021 ◽

Vol 305 ◽

pp. 04001

Author(s):

Sukisno ◽

Widiatmaka ◽

Januar J. Purwanto ◽

Bambang Pramudya N ◽

Khursatul Munibah

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Catchment Area ◽

Negative Impact ◽

Land Use Changes ◽

Hydropower Plant ◽

Land Use Land Cover ◽

Dry Land ◽

Land Cover Map

This research was conducted to review land use land cover change in the catchment area of Musi Hydropower Plant in Bengkulu Province. The data used in this research is land use land cover map year 2000 to 2018 from Ministry of Environment and Forestry of the Republic of Indonesia. The analyse was done by overlaying time series map of land use land cover map from 2000 to 2018 on the map of forest area. The result shows that primary dryland forest degradated significantly, around 568 ha less than 20 years. In the other side, settlements and built-up area significantly increase, 1.331 ha in 20 years. Meanwhile, the land use of agricultural dry land mixed with shrubs, in agregat decreased by 1.078 ha. The area of agricultural dry land mixed with shrubs was increase during period of 2000 to 2014, and then slightly decrease in the period of 2014 to 2018. Land use changes on the catchment area have negative impact on the quality of environmental services, such as erosion and sedimentation on the reservoir of Musi Hydropower Plant. Intervention needed to reduce the negative impact of the land use change on ecosystem services.

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Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020102 ◽

2021 ◽

Vol 10 (2) ◽

pp. 102

Author(s):

Tomáš Řezník ◽

Jan Chytrý ◽

Kateřina Trojanová

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Cover ◽

Training Data ◽

External Information ◽

Land Use Land Cover ◽

Proof Of Concept ◽

Area Of Interest ◽

Open Land ◽

Sentinel 2

Land use and land cover are continuously changing in today’s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon’s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.

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VEGETATION ANALYSIS AND LAND USE LAND COVER CLASSIFICATION OF FOREST IN UTTARA KANNADA DISTRICT INDIA THROUGH GEO-INFORMATICS APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w1-219-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 219-223 ◽

Cited By ~ 1

Author(s):

A. G. Koppad ◽

B. S. Janagoudar

Keyword(s):

Land Use ◽

Land Cover ◽

Anthropogenic Activities ◽

Ground Truth ◽

Vegetation Pattern ◽

Land Use Land Cover ◽

Forest Plantation ◽

Ground Truth Data ◽

Dense Forest ◽

Open Land

The study was conducted in Uttara Kannada districts during the year 2012&ndash;2014. The study area lies between 13.92&deg;&thinsp;N to 15.52&deg;&thinsp;N latitude and 74.08&deg;&thinsp;E to 75.09&deg;&thinsp;E longitude with an area of 10,215&thinsp;km2. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32&thinsp;% (6468.70&thinsp;sq&thinsp;km) followed by agriculture 12.88&thinsp;% (1315.31&thinsp;sq.&thinsp;km), sparse forest 10.59&thinsp;% (1081.37&thinsp;sq.&thinsp;km), open land 6.09&thinsp;% (622.37&thinsp;sq.&thinsp;km), horticulture plantation and least was forest plantation (1.07&thinsp;%). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non-vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

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