Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Xu, Yidi; Yu, Le; Peng, Dailiang; Zhao, Jiyao; Cheng, Yuqi; Liu, Xiaoxuan; Li, Wei; Meng, Ran; Xu, Xinliang; Gong, Peng

doi:10.1007/s11430-019-9606-4

Cited by 84 publications

(52 citation statements)

References 67 publications

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“…In the final step, all these data were combined to update the continuous oil palm dataset from 2001 to 2016 following Xu et al (2020).…”

Section: Updating Annual Oil Palm Resultsmentioning

confidence: 99%

“…Some studies suggested that the fusion of low-and high-resolution satellite data requires high-resolution images at a certain frequency (Zhang et al, 2017). However, when aiming to conduct consecutive mapping and changes detection, there will always be a trade-off between spatial and temporal resolution (Yin et al, 2018), considering the availability of satellite data such as MODIS and Landsat data (i.e. MODIS has denser observations but lower spatial resolution than Landsat data).…”

Section: Uncertainty Of Aopdmentioning

confidence: 99%

“…A new approach based on change detection in a given period using time-series observations (i.e. MODIS NDVI, GIMMS NDVI) was successfully applied to fill the years with missing data in developing a nominal 30 m annual China land-use and land cover dataset (Xu et al, 2020). This approach takes advantage of dense observations by detecting break points in a time series using change-detection algorithms, combined with the preexisting knowledge from the mapped years, and thus reduces the inter-annual inconsistency.…”

Section: Introductionmentioning

confidence: 99%

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Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

et al. 2020

Earth Syst. Sci. Data

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Abstract. Increasing global demand of vegetable oils and biofuels results in significant oil palm expansion in southeastern Asia, predominately in Malaysia and Indonesia. The land conversion to oil palm plantations has posed risks to deforestation (50 % of the oil palm was taken from forest during 1990–2005; Koh and Wilcove, 2008), loss of biodiversity and greenhouse gas emission over the past decades. Quantifying the consequences of oil palm expansion requires fine-scale and frequently updated datasets of land cover dynamics. Previous studies focused on total changes for a multi-year interval without identifying the exact time of conversion, causing uncertainty in the timing of carbon emission estimates from land cover change. Using Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we produced an annual oil palm area dataset (AOPD) at 100 m resolution in Malaysia and Indonesia from 2001 to 2016. We first mapped the oil palm extent using PALSAR and PALSAR-2 data for 2007–2010 and 2015–2016 and then applied a disturbance and recovery algorithm (Breaks For Additive Season and Trend – BFAST) to detect land cover change time points using MODIS data during the years without PALSAR data (2011–2014 and 2001–2006). The new oil palm land cover maps are assessed to have an accuracy of 86.61 % in the mapping step (2007–2010 and 2015–2016). During the intervening years when MODIS data are used, 75.74 % of the detected change time matched the timing of actual conversion using Google Earth and Landsat images. The AOPD revealed spatiotemporal oil palm dynamics every year and shows that plantations expanded from 2.59 to 6.39×106 ha and from 3.00 to 12.66×106 ha in Malaysia and Indonesia, respectively (i.e. a net increase of 146.60 % and 322.46 %) between 2001 and 2016. The higher trends from our dataset are consistent with those from the national inventories, with limited annual average difference in Malaysia (0.2×106 ha) and Indonesia (−0.17×106 ha). We highlight the capability of combining multiple-resolution radar and optical satellite datasets in annual plantation mapping to a large extent by using image classification and statistical boundary-based change detection to achieve long time series. The consistent characterization of oil palm dynamics can be further used in downstream applications. The annual oil palm plantation maps from 2001 to 2016 at 100 m resolution are published in the Tagged Image File Format with georeferencing information (GeoTIFF) at https://doi.org/10.5281/zenodo.3467071 (Xu et al., 2019).

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“…In the final step, all these data were combined to update the continuous oil palm dataset from 2001 to 2016 following Xu et al (2020).…”

Section: Updating Annual Oil Palm Resultsmentioning

confidence: 99%

Section: Uncertainty Of Aopdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

et al. 2020

Earth Syst. Sci. Data

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“…In this study, we used a set of continuous annual land cover mapping products at 30-m resolution (Xu et al, 2020), based on China's Land-Use/cover Dataset (Liu et al, 2014b), which was generated from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM +), Operational Land Imager (OLI), and multispectral data from the Huanjing-1 satellite (HJ-1). In the annual land cover mapping products, Xu et al (2020) used multi-source remote sensing images including the Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) and the Advanced Very High Resolution Radiometer (AVHRR, 8 km) to derive original dataset. Normalized difference vegetation index (NDVI) data of the two remote sensing images were used via BFAST algorithm as a breakpoint detection analysis to identify the exact land cover change time in the annual land cover mapping products.…”

Section: Land Cover Datamentioning

confidence: 99%

“…Correspondingly, the exact point for the cropland change could not be detected in a certain year, which may lead to an ambiguous variation point when considering a longer period (Herfindal et al, 2012). In this study, we used a series of cropland parcelling products for the period 1980-2015 (Xu et al, 2020) and detected the variation of cropland heterogeneity, which represents the land-use status. The consistency analysis was carried out using statistical results of cropland size and the cropland heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Cropland heterogeneity changes on the Northeast China Plain in the last three decades (1980s–2010s)

Liu

Dong

et al. 2020

PeerJ

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The Northeast China Plain is one of the major grain-producing areas of China because of its fertile black soil and large fields adapted for agricultural machinery. It has experienced some land-use changes, such as urbanization, deforestation, and wetland reclamation in recent decades. A comprehensive understanding of these changes in terms of the total cropping land and its heterogeneity during this period is important for policymakers. In this study, we used a series of cropland products at the 30-m resolution for the period 1980–2015. The heterogeneity for dominant cropland decreased slowly over the three decades, especially for the large pieces of cropland, showing a general trend of increased cropland homogeneity. The spatial patterns of the averaged heterogeneity index were nearly the same, varying from 0.5 to 0.6, and the most heterogeneous areas were mainly located in some separate counties. Cropland expansion occurred across most of Northeast China, while cropland shrinking occurred only in the northern and eastern sections of Northeast China and around the capital cities, in the flat areas. Also, changes in land use away from cropland mainly occurred in areas with low elevation (50–200 m) and a gentle slope (less than 1 degree). The predominant changes in cropland were gross gain and homogeneity, occurring across most of the area except capital cities and boundary areas. Possible reasons for the total cropland heterogeneity changes were urbanization, restoration of cropland to forest, and some government land-use policies. Moreover, this study evaluates the effectiveness of cropland policies influencing in Northeast China.

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Dynamics arising from the impact of large‐scale afforestation on ecosystem services

Wang

2022

Land Degrad Dev

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Understanding how multiple ecosystem services interact, how trade-offs emerge, and how interactions among ecosystem services may change after the Grain-for-Green-Program (GFGP) and is critical for managing multiple ecosystem services.Here, we conducted a spatiotemporal approach to quantify the impact of large-scale afforestation on four key ecosystem services (i.e., crop production, soil conservation, carbon sequestration, and water conservation). We observed that three satellite datasets (i.e., GIMMS-LAI3g, GIMMS-NDVI3g, and NDVI-ESTARFM) time-series show a slightly improved and significantly improved trends, and crop production, carbon sequestration, soil conservation, and water conservation have improved after the implementation of GFGP. Simultaneously, almost 49% of the low levels and 58% of the high levels for all four of the ecosystem services considered changed after the implementation of GFGP. The results also show that relationships among ecosystem services have shifted from a trade-off in 1988 and 1999 to an apparent synergistic relationship in 2011.We conclude that GFGP is a successful programme for addressing trade-offs between services. Our study provides experiential support for largescale restoration programme in humid area around the World.

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Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Cited by 84 publications

References 67 publications

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

Cropland heterogeneity changes on the Northeast China Plain in the last three decades (1980s–2010s)

Dynamics arising from the impact of large‐scale afforestation on ecosystem services

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