scholarly journals Satellite-based analysis of classification algorithms applied to the riparian zone of the Malaya Kokshaga river

2021 ◽  
Vol 932 (1) ◽  
pp. 012012
Author(s):  
L V Tarasova ◽  
L N Smirnova
Keyword(s):  
Land Cover ◽  
Riparian Zone ◽  
Riparian Forest ◽  
Field Studies ◽  
Neural Net ◽  
Maximum Value ◽  
Thematic Mapping ◽  
Bare Land ◽  
Sentinel 2A ◽  
Detailed Work

Abstract The paper comparatively analyses the accuracy of land cover classification in the riparian zone of the Malaya Kokshaga river in the Mari El Republic of Russia using Sentinel-2A satellite images with the algorithms of supervised classification: Maximum Likelihood (ML), Decision Tree (DT) and Neural Net (NN) in the ENVI-5.2 software package. Six main classes of land cover were identified based on field studies: coniferous, mixed (deciduous), shrublands, herbaceous, and water. The assessment of the area and the structure of land cover showed that forest covers 76% of the entire territory of the riparian area of the Malaya Kokshaga river. The analysis of the results of thematic mapping shows that the overall classification accuracy obtained by the ML algorithm is 96.09%, by NN - 94.51%, and by DT - 86.54%. The producer’s accuracy and user’s accuracy for most classes have the maximum value when the ML algorithm is used. For the NN algorithm, the maximum value of producer’s accuracy is observed for the mixed (deciduous) class, while for the DT algorithm – for the coniferous. When classified using all three algorithms the water and bare land classes were mixed, which requires more detailed work when estimating riparian forest ecosystems.

scholarly journals Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 345 ◽  
Author(s):  
Paria Ettehadi Osgouei ◽  
Sinasi Kaya ◽  
Elif Sertel ◽  
Ugur Alganci
Keyword(s):  
Land Cover ◽  
Support Vector ◽  
Special Focus ◽  
Study Region ◽  
Multi Index ◽  
Svm Classification ◽  
Bare Land ◽  
Kappa Value ◽  
Sentinel 2A ◽  
Index Combination

In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification.

scholarly journals Mapping Land use/land cover using a combination of Radar Sentinel-1A and Sentinel-2A optical images

2021 ◽  
Vol 652 (1) ◽  
pp. 012021
Author(s):  
T T H Nguyen ◽  
T N Q Chau ◽  
T A Pham ◽  
T X P Tran ◽  
T H Phan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Optical Images ◽  
Sentinel 2A

scholarly journals Non-Binary Snow Index for Multi-Component Surfaces

2021 ◽  
Vol 13 (14) ◽  
pp. 2777
Author(s):  
Mario Arreola-Esquivel ◽  
Carina Toxqui-Quitl ◽  
Maricela Delgadillo-Herrera ◽  
Alfonso Padilla-Vivanco ◽  
Gabriel Ortega-Mendoza ◽  
...  
Keyword(s):  
Spectral Characteristics ◽  
Ice Cover ◽  
Landsat 8 ◽  
Water Index ◽  
Bare Land ◽  
Snow Water ◽  
Precision Assessment ◽  
Normalized Difference Snow Index ◽  
Sentinel 2A ◽  
Water Vegetation

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.

scholarly journals Evaluation of the status of land use/land cover change using remote sensing and GIS in Jewha Watershed, Northeastern Ethiopia

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dereje Gebrie Habte ◽  
Satishkumar Belliethathan ◽  
Tenalem Ayenew
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Catchment Area ◽  
Environmental Conservation ◽  
Land Use Land Cover ◽  
Cultivated Land ◽  
Plantation Forest ◽  
Bare Land

AbstractEvaluation of land use/land cover (LULC) status of watersheds is vital to environmental management. This study was carried out in Jewha watershed, which is found in the upper Awash River basin of central Ethiopia. The total catchment area is 502 km2. All climatic zones of Ethiopia, including lowland arid (‘Kola’), midland semi-arid (‘Woinadega’), humid highland (Dega) and afro alpine (‘Wurch’) can be found in the watershed. The study focused on LULC classification and change detection using GIS and remote sensing techniques by analyzing satellite images. The data preprocessing and post-process was done using multi-temporal spectral satellite data. The images were used to evaluate the temporal trends of the LULC class by considering the years 1984, 1995, 2005 and 2015. Accuracy assessment and change detection of the classification were undertaken by accounting these four years images. The land use types in the study area were categorized into six classes: natural forest, plantation forest, cultivated land, shrub land, grass land and bare land. The result shows the cover classes which has high environmental role such as forest and shrub has decreased dramatically through time with cultivated land increasing during the same period in the watershed. The forest cover in 1984 was about 6.5% of the total catchment area, and it had decreased to 4.2% in 2015. In contrast, cultivated land increased from 38.7% in 1984 to 51% in 2015. Shrub land decreased from 28 to 18% in the same period. Bare land increased due to high gully formation in the catchment. In 1984, it was 1.8% which turned to 0.6% in 1995 then increased in 2015 to 2.7%. Plantation forest was not detected in 1984. In 1995, it covers 1.5% which turned to be the same in 2015. The study clearly demonstrated that there are significant changes of land use and land cover in the catchment. The findings will allow making informed decision which will allow better land use management and environmental conservation interventions.

scholarly journals Determinants of bird assemblage composition in riparian vegetation on sugarcane farms in the Queensland Wet Tropics

2015 ◽  
Vol 21 (1) ◽  
pp. 60 ◽  
Author(s):  
Anita F. Keir ◽  
Richard G. Pearson ◽  
Robert A. Congdon
Keyword(s):  
Riparian Vegetation ◽  
Wildlife Conservation ◽  
Agricultural Landscape ◽  
Riparian Zone ◽  
Riparian Forest ◽  
Agricultural Landscapes ◽  
Generalist Species ◽  
Assemblage Composition ◽  
Bird Assemblages ◽  
Wet Tropics

Remnant habitat patches in agricultural landscapes can contribute substantially to wildlife conservation. Understanding the main habitat variables that influence wildlife is important if these remnants are to be appropriately managed. We investigated relationships between the bird assemblages and characteristics of remnant riparian forest at 27 sites among sugarcane fields in the Queensland Wet Tropics bioregion. Sites within the remnant riparian zone had distinctly different bird assemblages from those of the forest, but provided habitat for many forest and generalist species. Width of the riparian vegetation and distance from source forest were the most important factors in explaining the bird assemblages in these remnant ribbons of vegetation. Gradual changes in assemblage composition occurred with increasing distance from source forest, with species of rainforest and dense vegetation being replaced by species of more open habitats, although increasing distance was confounded by decreasing riparian width. Species richness increased with width of the riparian zone, with high richness at the wide sites due to a mixture of open-habitat species typical of narrower sites and rainforest species typical of sites within intact forest, as a result of the greater similarity in vegetation characteristics between wide sites and the forest proper. The results demonstrate the habitat value for birds of remnant riparian vegetation in an agricultural landscape, supporting edge and open vegetation species with even narrow widths, but requiring substantial width (>90 m) to support specialists of the closed forest, the dominant original vegetation of the area.

scholarly journals Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Trinh Le Hung
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Urban Land ◽  
Landsat 8 ◽  
Land Classification ◽  
Infrared Band ◽  
Urban Land Cover ◽  
Bare Land ◽  
Sentinel 2

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.

scholarly journals Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

2019 ◽  
pp. 29-39 ◽  
Author(s):  
Stanislav Dugin ◽  
Oksana Sybirtseva ◽  
Stanislav Golubov ◽  
Yelizaveta Dorofey
Keyword(s):  
Pearson Correlation ◽  
Plant Cover ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Image Data ◽  
Multispectral Data ◽  
Maximum Value ◽  
Substantial Correlation ◽  
Satellite Image Data ◽  
Sentinel 2A

The study of plant cover have been performed by the hyperspectral remote sensing method using ASD FieldSpec® 3FR and DJI STS-VIS measurements. The orthophotoplans are compiled for the test plots of interest at the spatial resolution of 2.5 cm. The substantial correlation for the results of terrestrial verification for the satellite image data in the range of Sentinel-2A bands are confirmed. 15 vegetation indices for the Sentinel-2А wavelength bands were drawn at the Pearson correlation coefficient r > 0.97, with a maximum value of the correlation error of 0.07.

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