Understanding Land Cover Change using a harmonized classification system in the Himalayas: A case study from Sagarmatha National Park, Nepal (original) (raw)
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Assessment of Forest Cover Change of Dang, an Inner Terai District of Nepal
Journal of Forest and Natural Resource Management, 2019
This study analyzed the dynamics of changes of forest cover classes in the inner Terai District Dang, Nepal, based on Landsat Thematic Mapper (TM) images from two different years, viz., 1990 and 2011. Forest cover change analysis was performed through the analysis of a classified Landsat TM image using supervised classification. The overall classification accuracy for seven different land cover classes considered in this study were 80.37% and 80.56% for years 1990 and 2011, respectively. These classified images were further reclassified as forest and non-forest to analyze forest cover dynamics effectively using the post classification change detection. The results indicated that during 1990-2011, the total spatial areal coverage of forest land converted into other land cover was 20612 ha (shrubland), 8571 ha (agriculture), and 2787 ha (others) non-forest classes. A significant portion of non-forest classes was also converted into forest (e.g., 11433 ha of shrubland, 5663 ha of agriculture, and 5581 ha of other non forest classes). Sand and water bodies remained more or less constant during this period. While forest cover was estimated to be disappearing at the rate of 0.2% per year, dense forest appears to be converting into a sparse forest at the rate of 0.1% per year. Future study to assess the causes and driving forces of forest cover change in Nepal should get guidance from this study on where to target interventions.
International Journal of Remote Sensing, 2020
In this study, we analyzed time series of a vegetation index to identify land-cover/land-use changes in HHK mountain regions administered by Pakistan. Monthly MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) series were decomposed to retrieve changes in long-term mean, peaking magnitude and seasonal characteristics. Resulting linear trend patterns were used as a baseline to map syndromes over the study area. To address non-linear changes, inter-annual variation (IAV) and short-term variation (STV) was also computed. Distributed lagmodels (DLM) were used to determine significant correlation between GIMMS (Global Inventory Monitoring and Mapping) NDVI (Normalized Difference Vegetation Index) and rainfall as an additional syndrome to highlight climate sensitive regions. Our results indicate that land use is mainly controlled by two factors: elevation and river network. Based on that, there is a particular spatial distribution of agricultural intensification because of deforestation, forest degradation from unsustainable use, forest regeneration and human settlements near rivers. Most of the trends observed showed a persistent greening pattern compared to small groups of pixels with negative trends. Outcomes of DLM do not provide plausible links between rain and forest biomass. It does however suggest a positive response of crops to rainfall events in the arid zones of the study area. High short-term fluctuation in EVI residuals occurred in areas that experienced considerable land modification and where land-use patterns are not stable. IAV was high in regions around rivers and water works and its impact on forest dynamics could not be substantiated. Land change patterns described here can be used by decision makers for forest restoration programmes.
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It is required by the law that all community forests are surveyed before they are handed over to the designated CFUG for management. However, it is not regular practice to conduct such surveys using GPS rather the chain and compass method is normally used. Although in some cases a sketch map appears to have been submitted, a rapid field test of existing forest boundary maps indicated that they are not sufficiently reliable to allow direct digitization by comparison with other maps. Consequently, field collection of digital information of forest boundaries became necessary. GPS survey was intensively applied for the collection of digital information of forest boundaries in the study area. For this, 21 local individuals were trained to use GPS. Trained individuals were then mobilized in teams of 2 persons each for the forest boundary survey. Geographic information thus acquired i.e. latitude and longitude information of forest boundaries of all the surveyed forests in the study area was further processed to produce forest boundary maps. The forest boundary survey was limited to the forests which had not been thus surveyed at the time of study. In case of the forests which were already digitally surveyed the forest boundary maps were acquired from the respective sources, International Centre for Integrated Mountain Development (ICIMOD) being one of the main sources of such information. The forest boundary maps of community forests in Charnawati watershed, which covers major portions of Bhimeshwor Municipality, Boch, Lakuridanda and Magapauwa VDCs, were acquired from ICIMOD. The community forest maps included in the operational plans available at the DFO, Dolakha served as basis for verifying the forest boundary maps prepared from the GPS survey data. 3.2. Data analysis 3.2.1. Image classification The geo-referenced Landsat TM imageries acquired from the USGS-EROS archive consist of seven layers, one for each of seven spectral bands. These seven layers were stacked to produce seven band composite imageries. These imageries were then classified using ENVI 4.2 application (ITT Visual Information Solutions, Colorado). As the study team was highly familiar with the field situations and there was plenty of secondary information
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Land use/land cover changes in the central part of the Chitwan Annapurna Landscape, Nepal
PeerJ, 2022
Background: Land use/land cover assessment and monitoring of the land cover dynamics are essential to know the ecological, physical and anthropogenic processes in the landscape. Previous studies have indicated changes in the landscape of mid-hills of Nepal in the past few decades. But there is a lack of study in the Chitwan Annapurna Landscape; hence, this study was carried out to fill in study gap that existed in the area. Methods: This study evaluates land use/land cover dynamics between 2000 to 2020 in the central part of the Chitwan Annapurna Landscape, Nepal by using Landsat images. The Landsat images were classified into eight different classes using remote sensing and geographic information system (GIS). The accuracy assessment of classified images was evaluated by calculating actual accuracy, producer's accuracy, user's accuracy and kappa coefficient based on the ground-truthing points for 2020 and Google Earth and topographic maps for images of 2010 and 2000. Results: The results of land use/land cover analysis of Landsat image 2020 showed that the study area was composed of grassland (1.73%), barren area (1.76%), riverine forest (1.93%), water body (1.97%), developed area (4.13%), Sal dominated forest (15.4%), cropland (28.13%) and mixed forest (44.95%). The results of land cover change between 2000 to 2020 indicated an overall increase in Sal dominated forest (7.6%), developed area (31.34%), mixed forest (37.46%) and decrease in riverine forest (11.29%), barren area (20.03%), croplands (29.87%) and grasslands (49.71%). The classification of the images of 2000, 2010 and 2020 had 81%, 81.6% and 84.77% overall accuracy, respectively. This finding can be used as a baseline information for the development of a proper management plan to protect wildlife habitats and forecasting possible future changes, if needed.
Landscape characterisation of the forests of Himalayan foothills
2010
Deforestation and degradation are important aspects of landscape dynamics and have global significance. Quantification of landscape pattern using landscape metrics help in characterisation of landscapes and thus overall health of the forest cover. Himalayan foothills are one of the most important and fragile landscapes. Developmental activities and depdendence on the forest resources have altered the spatial pattern of these natural landscapes to a great extent. These changes in the landscape were analysed using satellite data from 1990, 2001 and 2006. The vegetation type maps of Dehradun forest division were prepared by supervised classification technique in order to study the landscape dynamics. Patch density, edge density, shape index, cohesion index, interspersion and juxtaposition index, normalised entropy, and relative richness are some important landscape metrics used in the study for quantifying the characteristics of landscape. The landscape metrics analysis and transformation analysis show that the forested areas are getting degraded and physical connectedness between the patches have also decreased making them isolated. The study demonstrates the importance of geospatial tools for monitoring the impact of disturbances on the forest ecosystem health, which can further help in landscape management.
Forest cover dynamics in Palas Valley Kohistan Hindu Kush Himalayan Mountains Pakistan
SCIENCE PRESS, 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA, 2021
Forest cover change in the mountainous region is driven by a variety of anthropogenic and natural factors. The Hindu Kush-Himalayan Mountains has experienced a considerable vegetation cover change due to intensive human activities, such as population growth, proximate causes, accessibility, unstable political situations, government policy failure and poverty. The present study seeks to find out the impact of population growth and road network expansion on forest cover of Palas valley based on remotely sensed data and employing geo-spatial techniques. Changes in forest cover were determined by classifying time-series satellite images of Landsat and Sentinel 2A. The images of October 1980, 2000, 2010 and 2017 were classified into six land cover classes and then the impact of population growth and accessibility on forest cover was analyzed. Furthermore, forest cover and land-use change detection map was prepared using classified images of 1980 and 2017. The data were collected mainly from field visits (ground verification), census reports, Communication and Works Department, Kohistan. Satellite imageries were obtained from the United States Geological Survey's websites and classified in ERDAS imagine 2014 and ESRI ArcGIS 10.2.1 using supervised classification-maximum likelihood algorithm. Result of this study revealed that a substantial reduction in forest cover has taken place mainly in the proximity of human settlements. On the average, during the study period, annually more than 460 hectares of forest area has been converted into other uses.
Vegetation and landuse mapping was carried out in the Kumaun Himalayan region, covering 21,034 km 2 area, with the help of multi-season AWiFS data of IRS-P6. Different vegetation and landuse categories were identified using a hybrid approach of classification including unsupervised, supervised and contextual refinement techniques. 41% of the total area was occupied by vegetation with a dominance of pine forest spread in an area of 1982.74 km 2 (23% of total forest area). SRTM DEM was used for post classification refinements. Classified map was assessed for accuracy, and an overall accuracy of 92% was obtained. Distribution of different vegetation types was also analyzed with respect to different topographic variables in the study area. Maximum area with vegetation was observed in mid elevation zone in comparison to other altitude zones. In different categories maximum distribution of forest area was under low followed by mid and higher slope categories. Southern aspect was observed with maximum forest area.