Object-based cropland degradation identification: a case study in Uzbekistan. Dubovyk, O., Menz, G., Conrad, C., Khamzina, A., & Dech, S. In Habib, S., Michel, U., Civco, D., Messinger, D., & Maltese, A., editors, SPIE Remote Sensing, volume 8538, of Earth Resources and Environmental Remote Sensing/GIS Applications III, 2012. SPIE. ![Object-based cropland degradation identification: a case study in Uzbekistan [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Sustainability of irrigated agriculture-based economies, such as in Central Asia, is threatened by cropland degradation. The field-based identification of the degraded agricultural areas can aid in developing appropriate land rehabilitation and monitoring programs. This paper combined the object-based change detection and spectral mixture analysis to develop an approach for identifying parcels of irrigated degraded cropland in Northern Uzbekistan, Central Asia. A linear spectral unmixing, followed by the object-based change vector analysis, was applied to the multiple Landsat TM images, acquired in 1987 and 2009. Considering a spectral dimensionality of Landsat TM, a multiple 4-endmember model (green vegetation, water, dark soil, and bright soil) was set up for the analysis. The spectral unmixing results were valid, as indicated by the overall root mean square errors of <2.5% reflectance for all images. The results of change detection revealed that about 33% (84,540 ha) of cropland in the study area were affected by the degradation processes to varying degrees. Spatial distribution of degraded fields was mainly associated with the abandoned fields and lands with inherently low fertile soils. The proposed approach could be elaborated for a field-based monitoring of cropland degradation in similar landscapes of Central Asia and elsewhere.
@inproceedings{dlr79205,
abstract = {Sustainability of irrigated agriculture-based economies, such as in Central Asia, is threatened by cropland degradation. The field-based identification of the degraded agricultural areas can aid in developing appropriate land rehabilitation and monitoring programs. This paper combined the object-based change detection and spectral mixture analysis to develop an approach for identifying parcels of irrigated degraded cropland in Northern Uzbekistan, Central Asia. A linear spectral unmixing, followed by the object-based change vector analysis, was applied to the multiple Landsat TM images, acquired in 1987 and 2009. Considering a spectral dimensionality of Landsat TM, a multiple 4-endmember model (green vegetation, water, dark soil, and bright soil) was set up for the analysis. The spectral unmixing results were valid, as indicated by the overall root mean square errors of \<2.5\% reflectance for all images. The results of change detection revealed that about 33\% (84,540 ha) of cropland in the study area were affected by the degradation processes to varying degrees. Spatial distribution of degraded fields was mainly associated with the abandoned fields and lands with inherently low fertile soils. The proposed approach could be elaborated for a field-based monitoring of cropland degradation in similar landscapes of Central Asia and elsewhere.},
added-at = {2020-09-11T12:04:50.000+0200},
author = {Dubovyk, Olena and Menz, Gunther and Conrad, Christopher and Khamzina, Asia and Dech, Stefan},
biburl = {https://www.bibsonomy.org/bibtex/2a2837dd4ba09c9deb40ebcff27d3ced7/earthobs_uniwue},
booktitle = {SPIE Remote Sensing},
editor = {Habib, Shahid and Michel, Ulrich and Civco, Daniel and Messinger, David and Maltese, Antonio},
interhash = {5a3d88498e01011046a470d2b0484e3a},
intrahash = {a2837dd4ba09c9deb40ebcff27d3ced7},
journal = {Proceedings of SPIE},
keywords = {conference Dech inproceedings},
publisher = {SPIE},
series = {Earth Resources and Environmental Remote Sensing/GIS Applications III},
timestamp = {2020-09-11T12:06:50.000+0200},
title = {Object-based cropland degradation identification: a case study in Uzbekistan},
url = {http://elib.dlr.de/79205/},
volume = 8538,
year = 2012
}
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Considering a spectral dimensionality of Landsat TM, a multiple 4-endmember model (green vegetation, water, dark soil, and bright soil) was set up for the analysis. The spectral unmixing results were valid, as indicated by the overall root mean square errors of <2.5% reflectance for all images. The results of change detection revealed that about 33% (84,540 ha) of cropland in the study area were affected by the degradation processes to varying degrees. Spatial distribution of degraded fields was mainly associated with the abandoned fields and lands with inherently low fertile soils. 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