Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa

Xu, Yidi; Yu, Le; Zhao, Feng; Cai, Xueliang; Zhao, Jiyao; Lu, Hui; Gong, Peng

doi:10.1016/j.rse.2018.09.008

Cited by 83 publications

(61 citation statements)

References 56 publications

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“…One challenge is for simultaneously distinguishing different types of landscape changes at large scales (Qiu et al, ; Qiu & Zhang, ; Zhu, ). Another challenge is the deficiency of automatic approaches applicable at large scales (Liu et al, ; Lu, Li, & Moran, ; Xu et al, ). Most studies on landscape change detection focused on one specific change or identifying the change areas instead of highlighting multiple landscape changes (Picoli et al, ; Wulder, Coops, Roy, White, & Hermosilla, ; Zhan, Gong, Liu, & Zhang, ).…”

Section: Discussionmentioning

confidence: 99%

“…The importance of consistency in land cover data has long been recognized, and it can be even more valuable than absolute accuracy (Pouliot et al, ). The consistency and continuity of continuous observations generated on the basis of independently classification are still far from reliable to investigate actual landscape changes (Pouliot et al, ; Xu et al, ; Xu & Chen, ). A shift from yearly land cover mapping to spatiotemporally continuous tracking landscape changes is needed (Wulder et al, ).…”

Section: Discussionmentioning

confidence: 99%

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Tracking spatial–temporal landscape changes of impervious surface areas, bare lands, and inundation areas in China during 2001–2017

Qiu

Chen

et al. 2019

Land Degrad Dev

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Efficient and timely quantification of landscape changes of urbanization, devegetation, and inundation in China is important for decision making in sustainable development. This study proposed a novel and automatic approach to track landscape changes in China based on 500 m 8‐day composite MODIS datasets from 2001 to 2017. An overall accuracy of 94.09% was achieved on the basis of 5,178 references sites. Results showed that newly impervious surface areas (ISA), bare lands, and inundated water bodies accounted for 0.51%, 0.23%, and 0.25% of the total land area in China, respectively. Rapid urbanization transformation was observed with the average annual land urbanization rate of 9.53%, almost three‐times higher than the global rate of urbanization. The speediest period of urbanization was exhibited in 2009 and 4,938.89 km2 of newly formed ISA were generated in this single year. The most obvious period of devegetation, which converted vegetation to bare lands, was observed from 2006 to 2009, with 2000–2100 km2 yr−1. Urbanization and devegetation dominantly occurred in humid/semihumid regions (>85%), particularly in the Yangtze River Delta. The remarkable devegetation was strongly associated with newly ISA; however, significant reduction of devegetation was observed in arid/semiarid regions in 2010s. Besides one hotspot surrounding newly ISA, the other hotspot of inundation in remote highlands was highlighted and distinct reduction in inundation was evidenced after 2010. A free download link for these datasets is attached (https://drive.google.com/drive/folders/1ZGSk3sP04ob77lmLTH01B0x1PEgjby7w?usp=sharing).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Tracking spatial–temporal landscape changes of impervious surface areas, bare lands, and inundation areas in China during 2001–2017

Qiu

Chen

et al. 2019

Land Degrad Dev

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“…These data have been used for several phenological application purposes in forestry [76,77], cropland mapping [64,[78][79][80], land cover classification [53,54], and carbon accounting [56]. Many land surface phenology studies have found that Landsat images can be used with high accuracy [25,28,37,50,51,56,64,[81][82][83].…”

Section: Collection Of Landsat Data and Image Compositementioning

confidence: 99%

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

et al. 2019

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As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.

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“…The moderate resolution of publicly available USGS Landsat data [5] makes them suitable for monitoring changes in agriculture fields [6][7][8]. Detection of cropland change can be based on comparisons between two or more Landsat images [9][10][11], and to date, many studies have applied Landsat time-series analysis to long-term monitoring of cropland change [12,13]. Such remote sensing time series can be used to separate long-term and short-term land-use changes more reliably in highly resilient land systems.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

Zhu

Liu

et al. 2019

Remote Sensing

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Tracking cropland change and its spatiotemporal characteristics can provide a scientific basis for assessments of ecological restoration in reclamation areas. In 1998, an ecological restoration project (Converting Farmland to Lake) was launched in Dongting Lake, China, in which original lake areas reclaimed for cropland were converted back to lake or to poplar cultivation areas. This study characterized the resulting long-term (1998–2018) change patterns using the LandTrendr algorithm with Landsat time-series data derived from the Google Earth Engine (GEE). Of the total cropland affected, ~447.48 km2 was converted to lake and 499.9 km2 was converted to poplar cultivation, with overall accuracies of 87.0% and 83.8%, respectively. The former covered a wider range, mainly distributed in the area surrounding Datong Lake, while the latter was more clustered in North and West Dongting Lake. Our methods based on GEE captured cropland change information efficiently, providing data (raster maps, yearly data, and change attributes) that can assist researchers and managers in gaining a better understanding of environmental influences related to the ongoing conversion efforts in this region.

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Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa

Cited by 83 publications

References 56 publications

Tracking spatial–temporal landscape changes of impervious surface areas, bare lands, and inundation areas in China during 2001–2017

Tracking spatial–temporal landscape changes of impervious surface areas, bare lands, and inundation areas in China during 2001–2017

Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform

Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery

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