Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery

2009 ◽  
Vol 90 (7) ◽  
pp. 2270-2277 ◽  
Author(s):  
C. Castañeda ◽  
D. Ducrot
Keyword(s):  
Land Cover ◽  
Agricultural Landscape ◽  
Landsat Imagery ◽  
Land Cover Mapping

A Novel Unsupervised Sample Collection Method for Urban Land-Cover Mapping Using Landsat Imagery

2019 ◽  
Vol 57 (6) ◽  
pp. 3933-3951 ◽  
Author(s):  
Jiayi Li ◽  
Xin Huang ◽  
Ting Hu ◽  
Xiuping Jia ◽  
Jon Atli Benediktsson
Keyword(s):  
Land Cover ◽  
Landsat Imagery ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Sample Collection ◽  
Collection Method ◽  
Urban Land Cover

scholarly journals Optimal Land Cover Mapping and Change Analysis in Northeastern Oregon Using Landsat Imagery

2015 ◽  
Vol 81 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Michael Campbell ◽  
Russell G. Congalton ◽  
Joel Hartter ◽  
Mark Ducey
Keyword(s):  
Land Cover ◽  
Landsat Imagery ◽  
Land Cover Mapping ◽  
Change Analysis

scholarly journals Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach

2019 ◽  
Vol 11 (9) ◽  
pp. 1056 ◽  
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Xidong Chen ◽  
Shuai Xie ◽  
Yuan Gao
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Earth Science ◽  
Landsat Imagery ◽  
European Space Agency ◽  
Accurate Method ◽  
Land Cover Mapping ◽  
Land Cover Types ◽  
Multi Temporal ◽  
Land Cover Map

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

2019 ◽  
Vol 3 (1) ◽  
pp. 14-27
Author(s):  
Barry Haack ◽  
Ron Mahabir
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Window Size ◽  
Radar Data ◽  
Optical Data ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Texture Image ◽  
Site Class ◽  
Spectral Signatures

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

scholarly journals Land Cover Effects on Selected Nutrient Compounds in Small Lowland Agricultural Catchments

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 182
Author(s):  
Maksym Łaszewski ◽  
Michał Fedorczyk ◽  
Sylwia Gołaszewska ◽  
Zuzanna Kieliszek ◽  
Paulina Maciejewska ◽  
...  
Keyword(s):  
Land Cover ◽  
Nutrient Dynamics ◽  
Agricultural Landscape ◽  
Predictive Performance ◽  
Special Focus ◽  
Spatial And Temporal Variability ◽  
Buffer Zones ◽  
Agricultural Catchments ◽  
Landscape Water ◽  
Seasonal Impact

The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO3 and P-PO4 concentrations and their landscape dependence was documented in the Świder River catchment in central Poland. From April 2019 to March 2020, water samples were collected from fourteen streams in the monthly timescale and the concentrations of N-NO3 and P-PO4 were correlated with land cover metrics based on the Corine Land Cover 2018 and Sentinel 2 Global Land Cover datasets. It was documented that agricultural lands and forests have a clear seasonal impact on N-NO3 concentrations, whereas the effect of meadows was weak and its direction was dependent on the dataset. The application of buffer zones metrics increased the correlation performance, whereas Euclidean distance scaling improved correlation mainly for forest datasets. The concentration of P-PO4 was not significantly related with land cover metrics, as their dynamics were driven mainly by hydrological conditions. The obtained results provided a new insight into landscape–water quality relationships in lowland agricultural landscape, with a special focus on evaluating the predictive performance of different land cover metrics and datasets.

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