Automated batch processing of mass remote sensing and geospatial data to meet the needs of end users

Zhao, Jianjun; Wang, Yeqiao

doi:10.1109/igarss.2011.6049966

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…HDF4 files were converted into a GeoTIFF format, assigned a projection and averaged to produce a single map. Sampling points could then be linked directly to specific average aerosol thicknesses over a 25 km radius (Zhao et al, 2011;Barona et al, 2006). Mine sites, industrial point sources, wastewater treatment sites, industrial sites, and other point source locations were obtained from the Virginia Department of Environmental Quality and Virginia Department of Mines, Minerals, and Energy.…”

Section: Sediment Laboratory Analysesmentioning

confidence: 99%

The impact of urban expansion and agricultural legacies on trace metal accumulation in fluvial and lacustrine sediments of the lower Chesapeake Bay basin, USA

Coxon

Odhiambo

Giancarlo

2016

Science of The Total Environment

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Section: Sediment Laboratory Analysesmentioning

confidence: 99%

The impact of urban expansion and agricultural legacies on trace metal accumulation in fluvial and lacustrine sediments of the lower Chesapeake Bay basin, USA

Coxon

Odhiambo

Giancarlo

2016

Science of The Total Environment

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“…This study concluded that the variations and trends of LSP metrics in the Appalachian Mountains were comparable to those obtained for the Appalachian Trail corridor area. 82 The LOS anomalies in 1987, 1994, and 1998 followed the pattern of warm and cold episodes based on a threshold of þ∕ − 0.5°C for the Oceanic Niño Index (ONI). The reversed LOS patterns correspond to El Niño in 1997 to 1998 and La Niña in 1999 to 2000 events.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Variation and trends of landscape dynamics, land surface phenology and net primary production of the Appalachian Mountains

Wang

Zhao

Zhou

2012

J. Appl. Remote Sens

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The gradients of elevations and latitudes in the Appalachian Mountains provide a unique regional perspective on landscape variations in the eastern United States and southeastern Canada. We reveal patterns and trends of landscape dynamics, land surface phenology, and ecosystem production along the Appalachian Mountains using time series data from Global Inventory Modeling and Mapping Studies and Advanced Very High Resolution Radiometer Global Production Efficiency Model datasets. We analyze the spatial and temporal patterns of the normalized difference vegetation index (NDVI), length of growing season (LOS), and net primary production (NPP) of selected ecoregions along the Appalachian Mountains regions. We compare the results in different spatial contexts, including North America and the Appalachian Trail corridor area. To reveal latitudinal variations, we analyze data and compare the results between the 30°-to-40°N and the 40°-to-50°N latitudes. The result reveal significant decreases in annual peak NDVI in the Appalachian Mountains regions. The trend for the Appalachian Mountains regions was a −0.0018 (R 2 ¼ 0.55, P < 0.0001) NDVI unit decrease per year during 25 years from 1982 to 2006. The LOS was prolonged by 0.3 days per year −1 during the 25-year percent. The NPP increased by 2.68 g Cm −2 yr −2 from 1981 to 2000. © 2012Society of Photo-Optical Instrumentation Engineers (SPIE).

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“…On the other hand, the classification of the EO data using machine learn-ing (ML) methods has been fairly successful. For example, to solve these issues, ML algorithms present automation of image classification [54,55], which is achieved through computer vision algorithms of pattern recognition and analysis that enables the recognition of geometrical complexity [48].…”

Section: Theoretical Framework and Motivationmentioning

confidence: 99%

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

Lemenkova

2024

JMSE

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This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

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Automated batch processing of mass remote sensing and geospatial data to meet the needs of end users

Cited by 7 publications

References 5 publications

The impact of urban expansion and agricultural legacies on trace metal accumulation in fluvial and lacustrine sediments of the lower Chesapeake Bay basin, USA

The impact of urban expansion and agricultural legacies on trace metal accumulation in fluvial and lacustrine sediments of the lower Chesapeake Bay basin, USA

Variation and trends of landscape dynamics, land surface phenology and net primary production of the Appalachian Mountains

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

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