Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery

2017 ◽  
Vol 193 ◽  
pp. 11-28 ◽  
Author(s):  
Xiaodong Huang ◽  
Jinfei Wang ◽  
Jiali Shang ◽  
Chunhua Liao ◽  
Jiangui Liu
Keyword(s):  
Land Cover ◽  
Mechanism Analysis ◽  
Scattering Mechanism ◽  
Multi Temporal

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

scholarly journals A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations

2016 ◽  
Vol 100 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Grazia Caradonna ◽  
Antonio Novelli ◽  
Eufemia Tarantino ◽  
Raffaela Cefalo ◽  
Umberto Fratino
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Geospatial Data ◽  
Usage Patterns ◽  
Mediterranean Regions ◽  
Multi Temporal ◽  
Using Data ◽  
Set Up

Abstract Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.

scholarly journals Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2564
Author(s):  
Mauro Martini ◽  
Vittorio Mazzia ◽  
Aleem Khaliq ◽  
Marcello Chiaberge
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Atmospheric Conditions ◽  
Practical Applications ◽  
Area Of Interest ◽  
Multi Temporal ◽  
Significant Performance ◽  
Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

scholarly journals Assessment of spatial changes of land use/land cover dynamics, using multi-temporal Landsat data in Dadri Block, Gautam Buddh Nagar, India

2020 ◽  
Vol XIX (1) ◽  
pp. 72-77
Author(s):  
Sushma Shastri ◽  
Prafull Singh ◽  
Pradipika Verma ◽  
Praveen Kumar Rai ◽  
A. P. Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Landsat Data ◽  
Land Use Land Cover ◽  
Spatial Changes ◽  
Land Cover Dynamics ◽  
Multi Temporal

scholarly journals Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

2013 ◽  
Vol 39 (4) ◽  
pp. 59-70 ◽  
Author(s):  
Fredrick Ao Otieno ◽  
Olumuyiwa I Ojo ◽  
George M. Ochieng
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Management Program ◽  
Land Use Land Cover ◽  
Irrigation Scheme ◽  
Multi Temporal

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

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