Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data

Remote Sensing

Jan Stefanski²  Tobias Kuemmerle¹  Oleh Chaskovskyy³  Patrick Griffiths¹  Vassiliy Havryluk³  Jan Knorn¹  Nikolas Korol³  Anika Sieber¹ 

[1] Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany; E-Mails:;Institute of Geographical Sciences, Freie Universität Berlin, Malteserstrasse 74-100, 12249 Berlin, Germany; E-Mail:;Institute of Forest Management, Ukrainian National Forestry University, vul. Gen. Chuprynky, 103, 79031 Lviv, Ukraine; E-Mails:

关键词: land use intensity;    post-soviet land use change;    multi-sensor;    landsat;    SAR;    land systems;    land management;    Ukraine;   

DOI  :  10.3390/rs6065279

来源: mdpi

【 摘 要 】

The global demand for agricultural products is surging due to population growth, more meat-based diets, and the increasing role of bioenergy. Three strategies can increase agricultural production: (1) expanding agriculture into natural ecosystems; (2) intensifying existing farmland; or (3) recultivating abandoned farmland. Because agricultural expansion entails substantial environmental trade-offs, intensification and recultivation are currently gaining increasing attention. Assessing where these strategies may be pursued, however, requires improved spatial information on land use intensity, including where farmland is active and fallow. We developed a framework to integrate optical and radar data in order to advance the mapping of three farmland management regimes: (1) large-scale, mechanized agriculture; (2) small-scale, subsistence agriculture; and (3) fallow or abandoned farmland. We applied this framework to our study area in western Ukraine, a region characterized by marked spatial heterogeneity in management intensity due to the legacies from Soviet land management, the breakdown of the Soviet Union in 1991, and the recent integration of this region into world markets. We mapped land management regimes using a hierarchical, object-based framework. Image segmentation for delineating objects was performed by using the Superpixel Contour algorithm. We then applied Random Forest classification to map land management regimes and validated our map using randomly sampled in-situ data, obtained during an extensive field campaign. Our results showed that farmland management regimes were mapped reliably, resulting in a final map with an overall accuracy of 83.4%. Comparing our land management regimes map with a soil map revealed that most fallow land occurred on soils marginally suited for agriculture, but some areas within our study region contained considerable potential for recultivation. Overall, our study highlights the potential for an improved, more nuanced mapping of agricultural land use by combining imagery of different sensors.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland