Revisiting the coupling between NDVI trends and cropland changes in the Sahel drylands: A case study in western Niger

Tong, Xiaoye; Brandt, Martin; Hiernaux, Pierre; Herrmann, Stefanie M.; Tian, Feng; Prishchepov, Alexander V.; Fensholt, Rasmus

doi:10.1016/j.rse.2017.01.030

Cited by 68 publications

(47 citation statements)

References 43 publications

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“…Existing land use and land cover studies commonly merge the cropped and fallowed fields in a common class [19]. Due to the wide range of existing fallow practices, mapping of these areas is a difficult task [16].…”

Section: Crop-fallow Rotation/fallowingmentioning

confidence: 99%

“…Due to the wide range of existing fallow practices, mapping of these areas is a difficult task [16]. A fallowed field may be confused with cropped fields due to climatic and soil conditions [20], cropping techniques [19], crop failure [16], or may be confused with surrounding ecosystems due to its natural regeneration.…”

Section: Crop-fallow Rotation/fallowingmentioning

confidence: 99%

“…Xie et al [21] showed that masking the non-agricultural areas blended with agricultural fields was a critical pre-processing step for distinguishing active crops and fallow lands. In Niger, Tong et al [19] developed a classification approach based on the different phenological characteristics of the vegetation, including Normalized Difference Vegetation Index (NDVI), amplitude, and rate of decrease rate, as monitored by a MODerate-resolution Imaging Spectroradiometer (MODIS) 250 m resolution data. They showed that fallowed fields generally have a higher NDVI and a more rapid decrease than unmanured cropped fields (Figure 2).…”

Section: Crop-fallow Rotation/fallowingmentioning

confidence: 99%

“…Scatter plots illustrating the MODIS Normalized Difference Vegetation Index (NDVI) based phenological differences between unmanured cropped and fallowed land in Niger, from Tong et al [19]. After the rainy season, a larger amplitude and a faster decrease were observed for the fallowed fields compared to the unmanured cropped fields.…”

Section: Figurementioning

confidence: 99%

“…As comprehensively described by Aksoy et al [25], although some spectral features analysis succeeded in discriminating structured plots [26], automatic orchards and vineyards mapping methods have mostly been developed based on the textural approach, using various structural indicators such as indices derived from the co-occurrence matrix [27,28], autocorrelograms [29,30], Markov random fields [31], and frequential analysis [32][33][34][35]. Peña-Barragan et al [36] showed Scatter plots illustrating the MODIS Normalized Difference Vegetation Index (NDVI) based phenological differences between unmanured cropped and fallowed land in Niger, from Tong et al [19]. After the rainy season, a larger amplitude and a faster decrease were observed for the fallowed fields compared to the unmanured cropped fields.…”

Section: Tree Crop Planting Patternmentioning

confidence: 99%

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Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Section: Crop-fallow Rotation/fallowingmentioning

confidence: 99%

Section: Crop-fallow Rotation/fallowingmentioning

confidence: 99%

Section: Crop-fallow Rotation/fallowingmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Tree Crop Planting Patternmentioning

confidence: 99%

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Remote Sensing and Cropping Practices: A Review

et al. 2018

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Major forest increase on the Loess Plateau, China (2001–2016)

et al. 2018

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The Loess Plateau in China is prone to widespread land degradation (soil erosion, deforestation, and water loss), and therefore, ecological restoration programmes aiming to re-establish the ecosystem by revegetation have been implemented during recent decades. Consequently, a widespread increase in vegetation cover has been reported, but the state and dynamics of forests remain largely unknown. Here, we used field and satellite data to produce annual forest probability scores at 250 × 250 m between 2001 and 2016. We classified the forest dynamics into three types: net gain, net loss, and fluctuation. Assuming that an increasing probability score reflects forest growth, we categorised the annual forest areas into different growing stages indicating the transition between planting and mature forest. In total, the area of forest increase was 48,786 km 2 , and the percentage of forested areas of the region changed from 8.19% to 15.82% within 16 years. In areas of forest net gain, there was a clear transition from low-to high-probability forest areas, providing evidence that a

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Grassland greening on the Mongolian Plateau despite higher grazing intensity

et al. 2020

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Changes in land management and climate alter vegetation dynamics, but the determinants of vegetation changes often remain elusive, especially in global drylands. Here we assess the determinants of grassland greenness on the Mongolian Plateau, one of the world's largest grassland biomes, which covers Mongolia and the province of Inner Mongolia in China. We use spatial panel regressions to quantify the impact of precipitation, temperature, radiation, and the intensity of livestock grazing on the normalized difference vegetation indices (NDVI) during the growing seasons from 1982 to 2015 at the county level. The results suggest that the Mongolian Plateau experienced vegetation greening from 1982 to 2015. Precipitation and animal density were the most influential factors contributing to higher NDVI on the grasslands of Inner Mongolia and Mongolia. Our results highlight the dominant effect of climate variability, and especially of the precipitation variability, on the grassland greenness in Mongolian drylands. The findings challenge the common belief that higher grazing pressure is the key driver for land degradation. The analysis exemplifies how representative wall‐to‐wall results for large areas can be attained from exploring space–time data and adds empirical insights to the puzzling relationship between grazing intensity and vegetation growth in dryland areas.

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Revisiting the coupling between NDVI trends and cropland changes in the Sahel drylands: A case study in western Niger

Cited by 68 publications

References 43 publications

Remote Sensing and Cropping Practices: A Review

Remote Sensing and Cropping Practices: A Review

Major forest increase on the Loess Plateau, China (2001–2016)

Grassland greening on the Mongolian Plateau despite higher grazing intensity

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