Machine Learning Approach for Change Detection of Chandaka Wildlife Sanctuary with the Help of Remote Sensing Data
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Deforestation
Land remote‐sensing images are the primary means of assessing land change. There have been major land changes in the planet in the last decades, especially in tropical forest areas. Identifying the agents of deforestation is important for establishing public policies that can help preserve the environment. This paper proposes a method for detecting the agents of land change in remote‐sensing image databases. We associate each land‐change pattern, detected in a remote‐sensing image, to one of the agents of change. The proposed method uses a decision‐tree classifier to describe shapes found in land‐use maps extracted from remote‐sensing images and then associates these shape descriptions to the different types of social agents involved in land‐use change. We support our proposal with two case studies for detecting land‐change agents in Amazonia, using the remote‐sensing image database of the Brazilian National Institute for Space Research (INPE).
Deforestation
Tropical forest
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The study is aimed to apply remote sensing for purposes of land cover detection in researches of new territorial units in Ukraine. The example of forest detection using Landsat images is particularly presented in the study. While the study area presented by Korovyntsi amalgamated territorial community in the Sumy region. The forest classification and deforestation detection have been processed every 5 years from 1990 through 2020. The Landsat 5, 7, and 8 data from the United States Geological Survey (USGS) have been used for the research. The image choice depended on the date of data availability and reliability, but in time between mid-May to early July. The dataset of 11 total images was processed in the Harris Geospatial Solutions’ Environment for Visualizing Images (ENVI). The data were calibrated by using the ENVI Landsat calibration tool, the atmospheric correction applied by using the ENVI FLAASH tool, and seamless mosaicking was used for some periods with more than one image needed. Normalized Difference Vegetation Index (NDVI) is the basis for forest classification applied. Comparing remote sensing data from different years and different Landsat satellites allowed not just to identify vegetation type of forest, but also to detect land cover changes. The change detection has been analyzed in two ways. The first method was based on changes in classification status. The second method was based on a difference in NDVI values, while forest classification was held for masking out non-forest areas. The applied study observed ways of cost-efficient land use research for local communities. Those methods could be used by NGO’s, local activists, citizen scientists, local authorities for improving land use management with the most updated data, and identifying problems of deforestation, in the case of the study presented. Nonetheless, land cover change detection is not limited to forest cover presented in the study. Anyway, in the case of forest detection, Landsat images from different satellites could be compared and present historical data for the rural areas, which had a low research interest in the past, but it changed due to administrative reform in Ukraine and switching governance power to the local communities.
Deforestation
Land Cover
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Forest degradation affects biodiversity and human society and contributes to greenhouse gas emissions. However, since there is no consensus about the definition of forest degradation, it is difficult to operate its characterization, evaluation and monitoring. Unlike deforestation, forest degradation does not imply a change in land cover and it happens in fine scales and is difficult to detect by means of remote sensors. Images from MODIS, Landsat, Sentinel-2, and UAVs form the main source of data for the national forest cover monitoring. The assumption here is that satellite images of different spatial and temporal resolutions as input may affect the measurement of forest degradation. The main objective is to study the influence of the spatial and temporal resolution of remote sensing images on forest degradation patterns detected. Data of multi-temporal MODIS, Landsat-8, and Sentinel-2 for an area of Michoacán, Mexico were collected. Forests were classified into primary and secondary status based on the existing land cover maps and visual interpretation of the images. The obtained land cover maps with forests at different stage of degradation were compared to derive information of forest degradation and regeneration.
Forest degradation
Deforestation
Degradation
Land Cover
Environmental degradation
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Presents preliminary results of two case studies focussing on Sahelian regions of the Niger inland delta (Mali, West Africa). The focus is set on the important preprocessing step of data homogenization for supporting reasonable comparative investigations of multitemporal and multisensor Earth observation data. The first study deals with the preprocessing of radiometric and atmospheric corrections applied to a series of LANDSAT-MSS data from the dry season of 1972, 1974 and 1982, covering the region of Lac Faguibine, at the northern fringe of the Niger inland delta. The accurate assessment of landcover change depends on the correction of different sensor radiometry. The second study deals with computer assisted photo interpretation. Image analysis of rasterized photographic data has some positive drawbacks compared with traditional visual photo interpretation. Results of detection and evaluation of quantitative indicators are more accurate and thus finer landcover change evaluation is made possible.
Radiometry
Data pre-processing
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Gorilla
Poaching
Deforestation
Troglodytes
Game reserve
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Seasonality
Deforestation
Dry season
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There are serious environmental problems, such as deforestation, soil erosion, salinization, and desert encroachment in the western China, its natural conditions is very delicate. But little is known about change in this region. The remote sensing data and digital image processing techniques provide consistent, reliable and quantifiable regional scale land-cover for environmental change research. The objective of this study is analyzing the environment change in a year in western China by using remote sensing data. In this paper, three typical experiment sites, which are a desert and oasis area, a loess plateau area and the source area of the Yellow River, are selected for detail change analyses. The change of NDVI, land-cover classifications between 1980s and present are detected from the satellite data. The datasets of remote sensing include Landsat-TM, ETM, SPOT and ASTER, all of these image are normalized to 30 m resolution and same coordination system for change analyses. The results show that industry development, cultivated land increment, soil erosion, forest and grassland decrement are the main reason of environmental deterioration.
Deforestation
Land Cover
Environmental change
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An investigation on challenges of human settlement on wildlife was carried out in 2014/2015 in and around Bale Mountains National Park. Among 25 villages bordering the National Park, 10 villages were purposefully selected for data collection. During the study period, semi-structured interviews and direct observations were conducted within the selected communities. A total of 365 households (35 households per villages, except 50 households for Rira) were randomly selected. Many parts of the protected area were found to be under cultivation. The main socio-economic activities of the respondents were mixed farming (58.0%) and livestock keeping (28.9%). The major reasons for off settlement near/inside the National Park were forage (52%), farming (25.6%) and both forage and farming (21.5%). Human settlement, agricultural expansions, and livestock grazing are the major problems of wildlife management inprotected area. Most of the cropland and human settlement expansions have been increasing from time to time and resulting in excessive losses of natural habitats for wildlife. This phenomenon was also attributed to migration of people from other places for farming and livestock grazing which has led to deforestation and intense decline in vegetation of protected area. Therefore, provision of appropriate conservation education should be emphasized for the local communities at different levels in the study area. Active measures have to be implemented to control the human settlement and livestock impact and safeguard the future of wildlife management in the park. Key words: Bale Mountains, conservation, human settlement, park, wildlife.
Deforestation
Settlement (finance)
Human settlement
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Monitoring tropical deforestation poses a juxtaposition of urgency and difficulty. Because of nearly constant cloud cover in the tropics, heavily studied and widely implemented methods using multispectral satellite imagery to do large-scale land monitoring are not possible. Being an active sensing system that can penetrate through clouds, Synthetic Aperture Radar (SAR) solves this issue, making it one of the best opportunities for tropical forest change in the tropics. In Mamoní Valley, Panama, Multivariate Alteration Detection (MAD) analysis was used with SAR Ground Range Detected imagery from the European Space Agency’s (ESA) Sentinel 1 satellite to test its viability for detecting forest changes. Multispectral Unmanned Aerial Vehicle (UAV) imagery was used and tested for its viability to provide validation of the change detection analysis. Results of the methodology showed correlation between the SAR MAD results and various land cover types observed in the UAV imagery, though distinct multitemporal changes resulting from the analyses did not appear to correlate with changes shown in the UAV imagery. A comparison with Principal Component Analysis showed similar results, leading to the conclusion that the MAD method implemented was sound, but GRD SAR data may not be most suitable for this method. The results of the analyses can be used to focus future UAV mapping sites which may further develop the accuracy and implementation of SAR for tropical change detection.
Deforestation
Land Cover
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