Decadal land cover change dynamics in Bhutan (original) (raw)
Related papers
Civil Engineering and Architecture, 2022
The urbanisation and multifarious upsurge of infrastructures in Bhutan have caused intense alteration of land cover topographies. These rapid changes undergoing are predominately snow cover, vegetation, water bodies, built-up, barren land, and agricultural land which are commonly called land use/land cover (LULC) change. The current research attempts to analyse concerning temporal and spatial frameworks features to observe the nature of development sprawling processes of Thimphu over 30 years (1990-2020), by using multi-temporal remote sensing data. Landsat 5, 7 and sentinel 2B imageries have been adopted for estimating land use/cover change in Thimphu for the past 30 years. The confusion matrix and Kappa coefficient methods were adopted for the classification accuracy assessment. This is further validated by field visit essentially on water bodies and barren land which were quite perplexing. The paper concludes that the largest proportion of the area (65.97%) in 1990 was under vegetation cover, followed by barren land (31.63%) and the third biggest (1.39%) was under snow cover. The current research will provide significant aid to the planners and architects to understand the pattern of development sprawling in the past and facilitate futuristic mapping the developmental activities.
2017
The rapid phase of urbanization and infrastructure development in Bhutan has been observed recently. This leads to causing of decrease in vegetation cover and growth in urban sprawl undergoing rapid land use/land cover change (LULC). This paper attempts to analyze the temporal and spatial patterns of LULC change and detects the urbanization processes of Phuentsholing city over a period of three decades (1996-2016) using multi temporal remotely sensed data. For this, the satellite images of Landsat 5, 7 and 8 were used to assess the changes of vegetation cover, built form and water bodies. This study has found that urban built area was increased from 6.7% in 1996 to 17% in 2016 and similarly vegetation cover was declined from 48.4% in 1996 to 49.9% in 2016. This urban expansion causes loss of vegetation cover that hinders the country’s regulation of retaining 60% forest according to The Constitution of the Kingdom of Bhutan. These finding can provide city planners and decision makers...
2020
The aim of the present study is to assess land use and land cover change in Dong Na Tard Provincial Protected Area (DNT PPA), Savannakhet Province, Lao PDR. The study was applied the integrated approach of remote sensing (RS) and Geographic Information System (GIS) for the land cover change detection. Historical land use and cover data of the Dong Na Tard PPA were obtained from a classification of Landsat ETM+ imagery obtained from the United States Geological Survey (USGS) Earth Explorer. Land use and land cover classification maps of 2007 and 2017 were extracted from Landsat TM/ETM images. Satellite images were preprocessed before classification. Layer stacking was performed to combine bands and then the images were performed radio-metrically corrected. The supervised classification was carried out using the Maximum Likelihood and a composition of bands 2, 3, 4 for Landsat 5 and bands 3, 4, 5 for Landsat 8. Kapa statistics were applied for accuracy assessment of images classificat...
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.
International Journal of Scientific Research in Science, Engineering and Technology, 2021
Using remote sensing and GIS technique, we analyse the change detection of different land use/land cover (LULC) types that has taken place in Puthimari river basin during a two-decade period from 1999 to 2019. Supervised classification method with maximum likelihood algorithm have been applied to prepare the LULC maps. The LULC change detection has been performed employing a post-classification detection method. Puthimari is a north bank sub-catchment of River Brahmaputra, the northern part of which falls in Bhutan and the rest falls in the Assam state of India. The primary LULC types of the basin are, dense vegetation which is predominant in the upper catchment, crop land and rural settlement. Thus, five different classes have been considered for the analysis, viz., dense vegetation, water bodies, silted water, cropland and rural settlement. The results showed that the rural settlement and water bodies in the basin increased by 42.70% and 30.31% from 1999 to 2019. However, dense vegetation, silted water and cropland decreased by 9.24%, 27.47% and 28.10% during these two decades.