Landscape Classification of Sariska National Park (India) and its Environments using Geospatial Technology (original) (raw)
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Current science
The Manas National Park is an important conservation area in the Bhabhar and flood plain ecosystem of Northeast India. Satellite imageries of 1977, 1998 and 2006 were analysed to detect the change in habitat types with the help of remote sensing and geographic information system tools. Results indicate landscapelevel changes in the vegetation and overall habitat quality within the Park. There is a substantial increase in savannah grassland (74.6%) accompanied by decline in alluvial grasslands (46.8%) from 1977 to 2006. A total of 20.47 km 2 has also been encroached during this period. Water sources in the Park have declined and there has been a significant shift towards a drier and woodland type of vegetation. These land-use changes were a result of non-implementation of habitat management/manipulation activities that are a prerequisite for supporting viable populations of specific endangered animal species in a given Protected Area. In this communication, we recommend a set of habitat management activities for restoration of key habitats in Manas.
Studies on land use aspects of ecosystem play an important role in identifying major problems and to take suitable action to maintain " Ecological equilibrium " in the region. The main objective of this study is to provide a baseline status of the study area so that present land use pattern and temporal changes occurred, on the surroundings can be evaluated. Comparative land use pattern and land cover classes of Ludhiana district, Punjab, India for the years 1991, 2003 and 2014 were studied which indicated that the area under all land use and land cover classes has changed in the due course of time using Remote Sensing and GIS tools. The LANDSAT imageries of three years were studied with a purpose to know the status of land use and land cover changes in the study area. Land use and land cover classes along with the changes under different categories were identified from the satellite imageries. It was found that green cover and agricultural land were decreasing at an alarming rate and land was turning to barren land.
International Journal of Current Microbiology and Applied Sciences, 2020
Mapping of land use land cover (LULC) rehearses in the Himalayas is indispensable for sustainable development, planning and management of resources. In light of remote sensing (RS) and geographic information system (GIS) techniques, the study is an endeavor to map LULC patterns of district Ganderbal of Kashmir Himalaya for the year 2018. Images from Landsat-8 (OLI) were used to extricate land cover maps. The study region was delineated through visual image interpretation technique into 10 Landuse/Landcover classes viz, forest, forest scrub, grassland, snow, wasteland, agriculture, TOF, built-up, water body and wetland. LULC map (2018) so generated revealed that among all the LULC classes, forest occupied maximum area of the map i.e. 33.96 % while as wetland with an area of 1.35 % occupied minimum portion of the map. The LULC map was likewise validated using ground truth points. The overall classification accuracy of LULC map came out to be 90.14 % with kappa coefficient of 0.8897. The outcomes of the study could be used as a spatial standard to illuminate land management and strategy choices made by organizers, specialists, environmentalists and different stakeholders for sustainable LULC management in the district Ganderbal of the
2014
Toposheet of Pathri reserve forest (1972), Landsat TM 7 images for the year 1990 and 2013 were used as data sets for the present study. LULC was assessed for 1990 and 2013 and change detection between the years mentioned was performed. LULC for 1990 indicated forest (31%), scrub forest (16%), crop land 1 (5%), fallow (8%), crop land 2 (4%), shrub land (13%) and water body (23%) and for 2013 it showed forest (16%), scrub forest (12%), crop land (20%), agriculture 1 (15%), agriculture 2 (12%), agriculture 3 (15%) and agriculture 4 (20%).Change detection matrix for 1990-2013 revealed that the LULC in the study area was converted to agricultural land. Predicted forest cover for the year 2036 showed a drastic change in the LULC indicating the presence of only three classes viz., forest, scrub forest and crop land. RS and GIS technologies has played a vital role in detecting the past, revealing the present and predicting the future LULC of Pathri reserve forest and these tools will certainly enable the decision and policy makers for a comprehensive idea to arrive at a solution for protection of wildlife and biodiversity.
Land Use Land Cover Analysis using Geospatial Techniques
IJRASET, 2021
Remote sensing and Geographic information system (GIS) techniques can be used for the changing pattern of landscape. The study was conducted in Dehradun, Haridwar and Pauri Garhwal Districts of Uttarakhand State, India. In order to understand dynamics of landscape and to examine changes in the land use/cover due to anthropogenic activities, two satellite images (Landsat 5 and Landsat 8) for 1998 and 2020 were used. Google Earth Engine was used to perform supervised classification. Spectral indices (NDVI, MNDWI, SAVI, NDBI) were calculated in order to identify land cover classes. Both 1998 and 2020 satellite images were classified broadly into six classes namely agriculture, built-up, dense forest, open forest, scrub and waterbody. Using high resolution google earth satellite images and visual interpretation, overall accuracy assessment was performed. For land cover/use change analysis, these images were imported to GIS platform. Landscape configuration was observed by calculating various landscape metrices Images. It was observed that scrub land area had increased from 11 % to 14 % but a decrease in agriculture by 4.65 %. The increased value of NP, PD, PLAND, LPI and decrease in AI landscape indices shows that land fragmentation had increased since 1998. The most fragmented classes were scrub (PD-3.32 to 5.18) and open forest (PD-3.57 to 5.07). Decrease in AI for open forest, agriculture, built-up indicated that more fragmented patches of these classes were present. The result confirmed increase in the fragmentation of landscape from 1998 onwards.
ijceronline.com
Achanakmar Amarkantak Biosphere Reserve located at the junction of hill ranges of Madhya Pradesh and Chhattisgarh state, India occupying total area of 3835.51sq.km. with topography ranging from high mountains, shallow valleys and plains. The core region of Achanakmar Amarkantak Biosphere Reserve falls in Chhattisgarh state lies between 22015' to 220 58' N and 810 25' to 820 50' E ,falls under the survey of India toposheet No. 64 F5,6,7,9,10,11,1314,15,64J1,J3.The Biosphere is bounded by Anuppur, Dindori and Bilaspur district.
Landscape characterization of Sariska National Park (India) and its surroundings
Geo-spatial Information Science, 2011
Landscape characterization gives an overall information on the status of Land Use and Land Cover (LULC), changes in its composition and the impact of natural and human influences operating at different spatial and temporal scales. This information can be used to monitor changes in natural forest resources and protected areas, delineate potential conservation areas and can serve in effective management
Journal of Environment and Waste Management, 2020
Earth's land use/land cover (LC/LU) classification provides valuable information particularly on natural resources, mapping and its monitoring. There is a significant change on LC/LU across the globe due to the climatic changes, rapid increase in population and over demand of economic natural resources. Remote Sensing (RS) satellite data with its synoptic view and multispectral data provides essential information in proper planning of LU/LC conditions of larger areas. The study aims to map and monitor the existing LU/LC classification scientifically using geospatial tools in database generation, analyses and information extraction. Thematic maps of the study area are prepared using satellite images in conjunction with collateral data Survey of India (SoI) toposheets, forest and wasteland maps. An attempt have been made to delineate the Level-I, Level-II and Level-III LU/LC classification system through NRSC guidelines (2011) using both Digital Image Processing (DIP) and Visual Image Interpretation Techniques (VIIT) by GIS software's with limited Ground Truth Check (GTC). More accurate classification is observed in case of digital technique as compared to that of visual technique in terms of area statistics. The final results highlight the potentiality of geospatial technique in optimal and sustainable land use planning of natural resource and its management.
Ecological Questions, 2010
Information about change is necessary for updating Land Use/ Land Cover LULC maps and the management of natural resources. The paper aims to map the changes in the LULC using hybrid classification methods and to quantify the land use/ land cover change that took place in the Rupnagar district of Punjab. The paper promotes the classification of LULC based on remote sensing information (obtained mainly through the utilization of Thematic Mapper TM) to generate data products that are both appropriate to, and immediately usable within different scientific applications. Satellite data provides the basis for geographically referenced land use/land cover characterization that is internally consistent, repeatable over time, and potentially more reliable. The main objective of this study is to quantify the change in the area of various LULC classes. Classification of four reflective bands of three Landsat images was carried out by using Isodata clustering algorithm with the aid of ground truth data. The second part focused on land use/ land cover changes by using the change detection comparison (pixel by pixel). The change analysis was performed by post image classification method, comparing the data from three different dates. The result indicates there was a rapid change in land use/land cover due to the increase in population. The results indicate that severe land cover changes have occurred in cropland (225.97 km 2), dense forest (128.57 km 2), settlement (93.5 km 2), salt affected land (9.74 km 2) and water body (11.69 km 2) areas from 1989 to 2006.