In the most downstream irrigation system along the Syrdarya River in the Kazalinsk region (Republic of Kazakhstan), the run-down water supplying system and widespread cropland degradation challenge the agricultural production, which is mainly dedicated to the growing of rice. To understand the development of the production system in that ecologically endangered region, this study aims at the generation and analysis of an inventory of agricultural land use, with a focus on rice cultivation. The unsupervised k-means classifier was utilized for the generation of annual rice masks of the Kazalinsk region based on Landsat images from 1984 to 2017. Three indicators derived from the rice masks served for the analysis of the spatial pattern and the trend of rice cropping intensity and land abandonment. Finally, drivers of the identified spatial patterns of rice cropping intensity (1984-2017) were modeled by applying regression tree analysis. The rice classification returned 91.6% overall accuracy for independent Google Earth samplings of 2004 and 2011. During the study period, the area under annual rice cultivation declined from 20,737 ha towards 10,828 ha. However, after 2004 (5,015 ha) the area under rice cultivation increased again and abandoned fields experienced reclamation. Mainly those parts of the irrigation system have been intensively used for rice production where fields occur in agglomerations. This practice can be assessed as economic indicator for the reduction of production costs (reduced irrigation efforts and logistics for field preparation, and other management activities) by the rice growers. The developed approach is applicable in the future and may be utilized to map abandoned agricultural fields and to identify drivers of land abandonment and cropland reclamation in the region.
Real Time Impact Factor:
Pending
Author Name: Olga Degtyareva, Nadiya Muratova, Vitaly Salnikov, Michael Thiel
URL: View PDF
Keywords: Rice cropping intensity, remote sensing time series, land use change, driver analysis, Kazalinsk region, Aral Sea Basin,
ISSN:
EISSN: 2522-9060
EOI/DOI: https://doi.org/10.29258/CAJWR
Add Citation
Views: 1
