scholarly journals Revealing land cover change in California with satellite data

Eos ◽  
2007 ◽  
Vol 88 (26) ◽  
pp. 269-274 ◽  
Author(s):  
Christopher Potter ◽  
Vanessa Genovese ◽  
Peggy Gross ◽  
Shyam Boriah ◽  
Michael Steinbach ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Satellite Data

Using Satellite Data for Assessing the Land Use and Land Cover Change in Bosnia and Herzegovina

2021 ◽  
pp. 694-708
Author(s):  
Melisa Ljuša ◽  
Hamid Čustović ◽  
Jasmin Taletović ◽  
Mirza Ponjavić ◽  
Almir Karabegović
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Bosnia And Herzegovina ◽  
Satellite Data

scholarly journals Monitoring Land Cover Changes in the Tropics using Satellite Remote Sensing Data

2019 ◽  
Vol 8 (12) ◽  
pp. 3631-3636
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Satellite Data ◽  
Land Cover Changes ◽  
Classification Methods ◽  
Land Covers ◽  
The Tropics ◽  
Land Cover Change Detection

Changes in land cover are inevitable phenomena that occur in all parts of the world. Land cover changes can occur due to natural phenomena that include runoff, soil erosion and sedimentation besides man-made phenomena that include deforestation, urbanization and conversion of land covers to suit human needs. Several works on change detection have been carried out elsewhere, however there were lack of effort in analyzing the issues that affect the performance of existing change detection techniques. The study presented in this paper aims to detect changes of land covers by using remote sensing satellite data. The study involves detection of land cover changes using remote sensing techniques. This makes use satellite data taken at different times over a particular area of interest. The data has resolution of 30 m and records surface reflectance at approximately 0.4 to 0.7 micrometers wavelengths. The study area is located in Selangor, Malaysia and occupied with tropical land covers including coastal swamp water, sediment plumes, urban, industry, water, bare land, cleared land, oil palm, rubber and coconut. Initially, region of interests (ROI) were drawn on each of the land covers in order to extract the training pixels. Landsat satellite bands 1, 2, 3, 4, 5 and 7 were then used as the input for the three supervised classification methods namely Support Vector Machine (SVM), Maximum Likelihood (ML) and Neural Network (NN). Different sizes of training pixels were used as the input for the classification methods so that the performance can be better understood. The accuracy of the classifications was then assessed by analyzing the classifications with a set of reference pixels using a confusion matrix. The classification methods were then used to identify the conversion of land cover from year 2000 to 2005 within the study area. The outcomes of the land cover change detection were reported in terms quantitative and qualitative analyses. The study shows that SVM gives a more accurate and realistic land cover change detection compared to ML and NN mainly due to not being much influenced by the size of the training pixels. The findings of the study serve as important input for decision makers in managing natural resources and environment in the tropics systematically and efficiently.

scholarly journals Monitoring Effects of Land Cover Change on Biophysical Drivers in Rangelands Using Albedo

Land ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 33 ◽  
Author(s):  
Zahn Münch ◽  
Lesley Gibson ◽  
Anthony Palmer
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Carbon Storage ◽  
Water Use ◽  
Satellite Data ◽  
Management Practices ◽  
Net Primary Production ◽  
Seasonal Effects ◽  
Positive Trend ◽  
Normalised Difference Vegetation Index

This paper explores the relationship between land cover change and albedo, recognized as a regulating ecosystems service. Trends and relationships between land cover change and surface albedo were quantified to characterise catchment water and carbon fluxes, through respectively evapotranspiration (ET) and net primary production (NPP). Moderate resolution imaging spectroradiometer (MODIS) and Landsat satellite data were used to describe trends at catchment and land cover change trajectory level. Peak season albedo was computed to reduce seasonal effects. Different trends were found depending on catchment land management practices, and satellite data used. Although not statistically significant, albedo, NPP, ET and normalised difference vegetation index (NDVI) were all correlated with rainfall. In both catchments, NPP, ET and NDVI showed a weak negative trend, while albedo showed a weak positive trend. Modelled land cover change was used to calculate future carbon storage and water use, with a decrease in catchment carbon storage and water use computed. Grassland, a dominant dormant land cover class, was targeted for land cover change by woody encroachment and afforestation, causing a decrease in albedo, while urbanisation and cultivation caused an increase in albedo. Land cover map error of fragmented transition classes and the mixed pixel effect, affected results, suggesting use of higher-resolution imagery for NPP and ET and albedo as a proxy for land cover.

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