Details
| Original language | English |
|---|---|
| Pages (from-to) | 859-877 |
| Number of pages | 19 |
| Journal | Remote sensing |
| Volume | 3 |
| Issue number | 5 |
| Publication status | Published - 27 Apr 2011 |
Abstract
Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland). They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90%) when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems.
Keywords
- Agriculture, Land use, Multi-temporal classification, Radar
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- General Earth and Planetary Sciences
Sustainable Development Goals
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In: Remote sensing, Vol. 3, No. 5, 27.04.2011, p. 859-877.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data
AU - Bargiel, Damian
AU - Herrmann, Sylvia
N1 - Copyright: Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011/4/27
Y1 - 2011/4/27
N2 - Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland). They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90%) when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems.
AB - Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland). They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90%) when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems.
KW - Agriculture
KW - Land use
KW - Multi-temporal classification
KW - Radar
UR - http://www.scopus.com/inward/record.url?scp=80052449896&partnerID=8YFLogxK
U2 - 10.3390/rs3050859
DO - 10.3390/rs3050859
M3 - Article
AN - SCOPUS:80052449896
VL - 3
SP - 859
EP - 877
JO - Remote sensing
JF - Remote sensing
SN - 2072-4292
IS - 5
ER -