scholarly journals Preliminary study of land cover/land use and geomorphic change of the near shoreline of Aceh Jaya Regency: a multi-temporal and multi-image approach

2021 ◽  
Vol 667 (1) ◽  
pp. 012029
Author(s):  
S Sugianto ◽  
M Rusdi ◽  
Y Syahputri ◽  
Y D Fazlina
Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská

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.


2020 ◽  
Vol XIX (1) ◽  
pp. 72-77
Author(s):  
Sushma Shastri ◽  
Prafull Singh ◽  
Pradipika Verma ◽  
Praveen Kumar Rai ◽  
A. P. Singh

2013 ◽  
Vol 39 (4) ◽  
pp. 59-70 ◽  
Author(s):  
Fredrick Ao Otieno ◽  
Olumuyiwa I Ojo ◽  
George M. Ochieng

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.


2011 ◽  
Vol 32 (24) ◽  
pp. 9547-9558 ◽  
Author(s):  
Rucha R. Joshi ◽  
Mangesh Warthe ◽  
Sharad Dwivedi ◽  
Ritesh Vijay ◽  
Tapan Chakrabarti

2020 ◽  
Vol 4 (2) ◽  
pp. 55-78
Author(s):  
Modibbo Babagana-Kyari ◽  
Babagana Boso

The fragile Sudano-Sahelian ecological zone of Nigeria has been classified as a hotspot of land cover change (LCC) that has been suffering from serious anthropogenic and biophysical stresses. Damaturu, being the fastest growing town situated in the region happened to be a victim of this negative development. The purpose of this study is to remotely observe and assess the prevailing land-use/land-cover (LULC) dynamics of Damaturu town and its delicate surrounding lands from the year 1987-2017 study periods. To achieve this, a supervised image classification technique with Maximum Likelihood Classifier (MLC) algorithm was used in ERDAS Imagine version 15 software to classify the three epochs multi-temporal and multi-spectral Landsat imageries (TM 1987, ETM+7 2000 and OLI 2017). The classified LULC maps and their resulting statistics were then used to assess the spatio-temporal aspects of the observed changes by placing the results within the wider context of previous related literature and evidences. Findings revealed that the built-up area has been expanding since 1987 with an annual change rate of 4.5% between 1987-2000, and 5.3% during 2000-2017 respectively. The growth of the town is being accompanied by massive farmlands expansion and vegetal cover (trees and shrubs) lost making the surrounding arable lands seriously disturbed. Thus, if the observed trends continue, the entire studied region will be subjected to severe environmental hazard such as desertification. Overall, the study provides valuable information required for sustainable  environmental management.


2019 ◽  
Vol 42 (4) ◽  
pp. 362-368
Author(s):  
Ram Kumar Singh ◽  
◽  
Vinay Shankar Prasad Sinha ◽  
Pawan Kumar Joshi ◽  
Manoj Kumar ◽  
...  

Land use land cover characterization and mapping have become a prerequisite in all environmental Planaing. The array of satellites deployed in the space provides multi-temporal images that can be used for the land use land cover classification. But, much often these multi-temporal images have data noise and anomaly owing to the cloud and atmospheric effects. This brings pseudo hikes and lows in data adding classification with possible errors. We present a method for the removal of data anomaly where monthly data of MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index (MODIS 13Q1) was used for the classification of images over a large area encompassing the SAARC nations. MODIS multi-temporal data were filtered usinga Savitzky-Golay (S-G) algorithm which provided smoothened data and the seasonality (start, end of the season) were identified. Phenology profile curves were created for the characterization of the agriculture and forestry feature classes. The S-G filtered images and raw MODIS data phenology profile curves were compared for the eleven classes of land cover, viz., ever green needle forest, ever green broad leave, deciduous broad leave, shrub, savannas, grass, agriculture, built-up, water, snow (ice), and barren. Spectral signature separability was also compared using Euclidean spectral distance method. In conclusion, it was observed that multi-spectral S-G filtered data were more useful for the classification of agriculture and forestry classes for a larger coverage.


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