Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data
Mapping Land Management Regimes in Western Ukraine Using Optical and SAR Data
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DOI：
10.3390/rs6065279
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发表时间：
2014-06
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通讯作者：
Jan Stefanski;T. Kuemmerle;Oleh Chaskovskyy;P. Griffiths;V. Havryluk;J. Knorn;N. Korol;Anika Sieber;B. Waske
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文献类型：
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作者：
Jan Stefanski;T. Kuemmerle;Oleh Chaskovskyy;P. Griffiths;V. Havryluk;J. Knorn;N. Korol;Anika Sieber;B. Waske
由于人口增长、更多以肉类为基础的饮食以及生物能源日益重要的作用，全球对农产品的需求正在激增。三种策略可以提高农业产量：(1)将农业扩展到自然生态系统；(2)加固现有耕地；（三）退耕退耕。由于农业扩张需要大量的环境权衡，集约化和再耕作目前正受到越来越多的关注。然而，评估在何处可实施这些战略需要改进土地利用强度的空间信息，包括活跃和休耕的农田。我们开发了一个框架来整合光学和雷达数据，以推进三种农田管理制度的制图：(1)大规模机械化农业；(2)小规模自给农业；（三）休耕或者废弃的农田。我们将这一框架应用于乌克兰西部的研究区域，该地区的管理强度具有明显的空间异质性，这是由于苏联土地管理的遗留问题、1991年苏联解体以及该地区最近融入世界市场。我们使用分层的、基于对象的框架绘制了土地管理制度。采用超像素轮廓算法对目标进行分割。然后，我们将随机森林分类应用于土地管理制度地图，并使用在广泛的实地活动中获得的随机抽样的原位数据验证我们的地图。我们的研究结果表明，农田管理制度被可靠地绘制出来，最终的地图总体精度为83.4%。将我们的土地管理制度图与土壤图进行比较，发现大多数休耕土地发生在略微适合农业的土壤上，但我们研究区域内的一些地区具有相当大的复垦潜力。总的来说，我们的研究强调了通过结合不同传感器的图像来改进、更细致地绘制农业用地地图的潜力。
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.