Challenges and opportunities in mapping land use intensity globally

Kuemmerle, Tobias; Erb, Karl‐Heinz; Meyfroidt, Patrick; Müller, Daniel; Verburg, Peter H.; Estel, Stephan; Haberl, Helmut; Hostert, Patrick; Jepsen, Martin Rudbeck; Kastner, Thomas; Levers, Christian; Lindner, Marcus; Plutzar, Christoph; Verkerk, Pieter Johannes; Zanden, Emma H. van der; Reenberg, Anette

doi:10.1016/j.cosust.2013.06.002

Cited by 315 publications

(243 citation statements)

References 56 publications

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“…However, assessing where sustainable intensification or recultivation could be fostered, requires improved maps that go beyond broad land cover classes such as cropland and are sensitive to land use intensity and that include information on active and fallow/abandoned agriculture. Unfortunately, such information does not exist for most parts of the globe [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Agricultural land use intensity can be measured via inputs to agriculture (e.g., fertilizer, pesticide application rate), the outputs from agriculture (e.g., yields), or in terms of system properties that change due to management (e.g., human appropriation of net primary production) [15,24,25]. Unfortunately, remote sensing can only rarely measure any of these dimensions directly and therefore a combination of remote sensing and ground-based data is often needed to determine land use intensity metrics.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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Abstract: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, Remote Sens. 2014, 6 5280 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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“…Landscape analysis is therefore considered a suitable approach for monitoring these distinct land cover and land use patterns, and transitions between them [2,40]. Geographical information sciences and remote sensing provide powerful tools to undertake such research [37,41,42].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

et al. 2017

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This study spatially assesses, quantifies, and visualizes the agricultural expansion and land use intensification in the northwestern foothills of Mount Kenya over the last 30 years: processes triggered by population growth, and, more recently, by large-scale commercial investments. We made use of Google Earth Engine to access the USGS Landsat data archive and to generate cloud-free seasonal composites. These enabled us to accurately differentiate between rainfed and irrigated cropland, which was important for assessing agricultural intensification. We developed three land cover and land use classifications using the random forest classifier, and assessed land cover and land use change by creating cross-tabulation matrices for the intervals from 1987 to 2002, 2002 to 2016, and 1987 to 2016 and calculating the net change. We then applied a landscape mosaic approach to each classification to identify landscape types categorized by land use intensity. We discuss the impacts of landscape changes on natural habitats, biodiversity, and water. Kappa accuracies for the three classifications lay between 78.3% and 82.1%. Our study confirms that rainfed and irrigated cropland expanded at the expense of natural habitats, including protected areas. Agricultural expansion took place mainly in the 1980s and 1990s, whereas agricultural intensification largely happened after 2000. Since then, not only large-scale producers, but also many smallholders have begun to practice irrigated farming. The spatial pattern of agricultural expansion and intensification in the study area is defined by water availability. Agricultural intensification and the expansion of horticulture agribusinesses increase pressure on water. Furthermore, the observed changes have heightened pressure on pasture and idle land due to the decrease in natural and agropastoral landscapes. Conflicts between pastoralists, smallholder farmers, large-scale ranches, and wildlife might further increase, particularly during the dry seasons and in years of extreme drought.

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“…Habitat fragmentation results in a reduction in area, an increase in remnant isolation, the creation of edge, and an alteration in the habitat structure of the remnants, all of which may contribute either directly or indirectly to changes in species survival (Newmark et al, 2011). There are also other disturbance factors such as multiple cropping intensity, levels of fertilizer and pesticide use in cropland, and grassland management systems (Grondin et al, 2013;Kleijn et al, 2009;Kuemmerle et al, 2013;Tao et al, 2008;Vačkář et al, 2012;Yan et al, 2013). Different patterns of cropping intensity or land use management systems would exert varying pressure on local biodiversity.…”

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confidence: 99%

Disturbance impacts of land use change on biodiversity conservation priority areas across China: 1990–2010

et al. 2015

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Land use change is one of the main drivers of biodiversity loss. In the last 20 years, China's land use pattern has undergone profound changes. This study constructs an Ecosystem Comprehensive Anthropogenic Disturbance Index (ECADI) to assess disturbance impacts of land use change between 1990 and 2010 on biodiversity conservation priority areas at national and regional scales. Four levels of biodiversity conservation areas were categorized: generally important areas, moderately important areas, important areas, and very important areas. The results indicated a higher ECADI value in 2010 in Central and Eastern China than in Western China, and the values of the moderately important, important and very important regions were lower than the average value of the whole country at all levels. Notably, in recent 20 years, the change extent of ECADI values in Central and Eastern China were much greater compared with that in Western China, and ECADI values in the moderately important, important and very important biodiversity conservation areas all showed increasing trends, with the increasing extent lower than that of whole China at all levels. Due to human activities such as urbanization in Eastern China and cropland reclamation in Northeast China and Xinjiang, ECADI values showed a medium increase trend (the change rate was about 1%-5% in 10 years), which indicated the need for more conservation efforts in those regions. However, ECADI values in the Loess Plateau presented a low decline trend (the change rate was about -1% to -0.1% in 10 years) after 2000 because of the obvious effectiveness of Green for Grain Project. Furthermore, the variation was negligible in the Tibetan Plateau.

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Challenges and opportunities in mapping land use intensity globally

Cited by 315 publications

References 56 publications

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

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

Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective

Disturbance impacts of land use change on biodiversity conservation priority areas across China: 1990–2010

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