25 research outputs found
Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal
Study of soft classification approaches for identification of earthquake-induced liquefied soil
Agricultural Drought Assessment: Operational Approaches in India with Special Emphasis on 2012
Mapping tree aboveground biomass and carbon in Omo Forest Reserve Nigeria using Landsat 8 OLI data
Wetland Monitoring, Serving as an Index of Land Use Change-A Study in Samaspur Wetlands, Uttar Pradesh, India
Remote observations with images from landsat satellites to determine the environmental impact of agrarian reform in the Brazilian Midwest between 2004 and 2014
Evaluation of an abandoned aggregate quarry used for uncontrolled waste disposal using remote sensing technologies (Atabey, Isparta-Turkey)
Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans
In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels
Identification of successional trajectory over 30 Years and evaluation of reclamation effect in coal waste dumps of surface coal mine
Spatiotemporal Pattern of Urban Forest Leaf Area Index in Response to Rapid Urbanization and Urban Greening
Rapid urbanization and urban greening have caused great changes to urban forests in China. Understanding spatiotemporal patterns of urban forest leaf area index (LAI) under rapid urbanization and urban greening is important for urban forest planning and management. We evaluated the potential for estimating urban forest LAI spatiotemporally by using Landsat TM imagery. We collected three scenes of Landsat TM (thematic mapper) images acquired in 1997, 2004 and 2010 and conducted a field survey to collect urban forest LAI. Finally, spatiotemporal maps of the urban forest LAI were created using a NDVI-based urban forest LAI predictive model. Our results show that normalized differential vegetation index (NDVI) could be used as a predictor for urban forest LAI similar to natural forests. Both rapid urbanization and urban greening contribute to the changing process of urban forest LAI. The urban forest has changed considerably from 1997 to 2010. Urban vegetated pixels decreased gradually from 1997 to 2010 due to intensive urbanization. Leaf area for the study area was 216.4, 145.2 and 173.7 km2 in the years 1997, 2004 and 2010, respectively. Urban forest LAI decreased sharply from 1997 to 2004 and increased slightly from 2004 to 2010 because of numerous greening policies. The urban forest LAI class distributions were skewed toward low values in 1997 and 2004. Moreover, the LAI presented a decreasing trend from suburban to downtown areas. We demonstrate the usefulness of TM remote-sensing in understanding spatiotemporal changing patterns of urban forest LAI under rapid urbanization and urban greening