Vegetation mapping and characterization in West Siang District of Arunachal Pradesh, India - a satellite remote sensing-based approach (original) (raw)
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Vegetation Analysis with Reference to Topographic Variables using Remote Sensing Data
The study was carried out in Kalsa Watershed, Uttarakhand, India to analyze the vegetation composition, topographic attributes and distribution of different vegetation types in relation to topography. Remote Sensing data of Indian satellite IRS 1D-LISS III sensor was used to map vegetation and land use. Survey of India (SOI) topographic map was used to generate the Digital Elevation Model (DEM). A total of 10 vegetation and land use classes were mapped. Pine forest was recorded as most dominant forest type of study area. The study revealed the extension of pine forest in high altitude areas above its normal zone of occurrence.
International Journal of Computer Applications, 2013
Vegetation plays a key role in reducing ambient temperature, moisture and pollutant capture, energy use and subsequent ground level ozone reduction. In recent years vegetation mapping has become increasingly important, especially with advancements in environmental economic valuation. The spatial information from the remote sensing satellites enables researchers to quantify and qualify the amount and health of vegetation. The present study highlights significance of remote sensing in the vegetation mapping of western ghat region of Maharashtra using satellite imageries from Landsat TM. A supervised (full Gaussian) maximum likelihood classification was implemented in our approach. The final classification product provided identification and mapping of dominant land cover types, including forest types and nonforest vegetation. Remote sensing data sets were calibrated using a variety of field verification measurements. Field methods included the identification of dominant forest species, forest type and relative state-of-health of selected tree species. Ground truth information was used to assess the accuracy of the classification. The vegetation type map was prepared from the classified satellite image. The moist deciduous forests constitute major portion of the total forest area. The application of remote sensing and satellites imageries with spatial analysis of land use land cover provides policy and decision makers with current and improved data for the purposes of effective management of natural resources.
seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-IIIimages is presented. The map was created using an on-screen visual interpretation technique and has anaccuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potentialvegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mappedfurther the vegetation type distribution in the country in terms of occurrence and distribution, areaoccupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, meanannual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchicalclassification scheme that accommodates natural and semi-natural systems was conceptualized, and thenatural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent ofvegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broadrange of ecological, climatic and conservation applications from global, national and local perspectives.We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover,Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that themap prepared herein be used widely. This vegetation type map is the most comprehensive one developedfor India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples andinformation relating to the biogeography, climate and soil. The digital map is now available through aweb portal
Remote sensing imagery in vegetation mapping: a review
Journal of Plant Ecology, 2008
Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.
Journal of the Indian Society of Remote Sensing, 2005
Improving image classification and its techniques have been of interest while handling satellite data especially in hilly regions with evergreen forests particularly with indistinct ecotones. In the present study an attempt has been made to classify evergreen forests/vegetation in Moulitig National Park of Arunachal Pradesh in Eastern Himalayas using conventional unsupervised classification algorithms in conjunction with DEM. The study area represents climax vegetation and can be broadly classified into tropical, subtropical, temperate and sub-alpine forests. Vegetation pattern in the study area is influenced strongly by altitude, slope, aspect and other climatic factors. The forests are mature, undisturbed and intermixed with close canopy. Rugged terrain and elevation also affect the reflectance. Because of these discrimination among the various forest/vegetation types is restrained on satellite data. Therefore, satellite data in optical region have limitations in pattern recognition due to similarity in spectral response caused by several factors. Since vegetation is controlled by elevation among other factors, digital elevation model (DEM) was integrated with the LISS III multiband data. The overall accuracy improved from 40.81 to 83.67%. Maximum-forested area (252.80 km 2) in national park is covered by sub-tropical evergreen forest followed by temperate broad-leaved forest (147.09 km2). This is probably first attempt where detailed survey of remote and inhospitable areas of Semang subwatershed, in and around western part of Mouling Peak and adjacent areas above Bomdo-Egum and Ramsingh from eastern and southern side have been accessed for detailed ground truth collection for vegetation mapping (on 1:50,000 scale) and characterization. The occurrence of temperate conifer forests and Rhododendron Scrub in this region is reported here for the first time. The approach of DEM integrated with satellite data can be useful for vegetation and land cover mapping in rugged terrains like in Himalayas.
The present study has been conducted to analysis the accuracy in different indices to measure the vegetation cover. For the study three decadal remote sensing data have been selected such as, 1992, 2001 and 2012. These satellite images are belong to Lands at series, in which 1992 and 2012 are belong to TM and 2001 is belong to ETM+. To analyze the accuracy of different indices, the study have been conducted the vegetation cover of Baramulla district using NDVI, SAVI and MSI indices. The result of analysis represents that the indices SAVI is better than other two methods. 3985.pdf
2018
Rapid monitoring of vegetation cover with precision has always been a challenge for maintaining accuracy over a large area. Remote Sensing (RS) based satellite imagery has significantly contributed in monitoring vegetation and land cover categorization. As the vegetation has a close relationship with detachment of soil and its sedimentation, regular monitoring of vegetation is essential especially in the catchment area of dams and reservoirs. In this study, vegetation maps were prepared through imaging processing of satellite imageries. With the help of Vegetation Index (VI) based maps, we were able to study the vegetation phenology in the watershed. The Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI) were obtained using the spectral bands of Landsat 8 and Sentinel 2 A satellite data. The classes were made in accordance to no vegetation cover ( 0.4). The area under each category was calculated with vector files...