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

Xu, Yidi; Yu, Le; Li, Wei; Ciais, Philippe; Cheng, Yuqi; Gong, Peng

doi:10.5194/essd-2019-137

Cited by 8 publications

(33 citation statements)

References 54 publications

(95 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of the CNN classification was compared with existing oil palm maps of Sumatra for the year 2019 (Descals et al, 2019) and Southeast Asia for the year 2016 (Xu et al, 2020). Also, we compared our oil palm area estimates with the oil palm harvested area included in Food and Agriculture Organization of the United Nations (FAOSTAT) data at the country level and with the area estimates obtained from an oil palm map developed in Gaveau et al (2021) over Indonesia for the year 2019.…”

Section: Comparison With Other Oil Palm Datasetsmentioning

confidence: 99%

“…Also, we compared our oil palm area estimates with the oil palm harvested area included in Food and Agriculture Organization of the United Nations (FAOSTAT) data at the country level and with the area estimates obtained from an oil palm map developed in Gaveau et al (2021) over Indonesia for the year 2019. The oil palm maps in Descals et al (2019) and Xu et al (2020) were generated with a random forest classification, while the map developed by was generated by digitizing the oil palm plantations in Landsat and SPOT6 images.…”

Section: Comparison With Other Oil Palm Datasetsmentioning

confidence: 99%

“…This characteristic backscatter response is a consequence of the canopy structure of palm-like trees and allows for the detection of closed-canopy palm plantations, particularly oil palm. Several studies have taken advantage of this characteristic backscatter response for mapping oil palm at the local and the regional scale (Koh et al, 2011;Lee et al, 2016;Nomura et al, 2019;Oon et al, 2019) and similarly for using supervised classification models (Descals et al, 2019;Shaharum et al, 2020;Xu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations

Descals

Wich

Meijaard

et al. 2021

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. Oil seed crops, especially oil palm, are among the most rapidly expanding agricultural land uses, and their expansion is known to cause significant environmental damage. Accordingly, these crops often feature in public and policy debates which are hampered or biased by a lack of accurate information on environmental impacts. In particular, the lack of accurate global crop maps remains a concern. Recent advances in deep-learning and remotely sensed data access make it possible to address this gap. We present a map of closed-canopy oil palm (Elaeis guineensis) plantations by typology (industrial versus smallholder plantations) at the global scale and with unprecedented detail (10 m resolution) for the year 2019. The DeepLabv3+ model, a convolutional neural network (CNN) for semantic segmentation, was trained to classify Sentinel-1 and Sentinel-2 images onto an oil palm land cover map. The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy =98.52±0.20 %), outperforming the accuracy of existing regional oil palm datasets that used conventional machine-learning algorithms. The user's accuracy, reflecting commission error, in industrial and smallholders was 88.22 ± 2.73 % and 76.56 ± 4.53 %, and the producer's accuracy, reflecting omission error, was 75.78 ± 3.55 % and 86.92 ± 5.12 %, respectively. The global oil palm layer reveals that closed-canopy oil palm plantations are found in 49 countries, covering a mapped area of 19.60 Mha; the area estimate was 21.00 ± 0.42 Mha (72.7 % industrial and 27.3 % smallholder plantations). Southeast Asia ranks as the main producing region with an oil palm area estimate of 18.69 ± 0.33 Mha or 89 % of global closed-canopy plantations. Our analysis confirms significant regional variation in the ratio of industrial versus smallholder growers, but it also confirms that, from a typical land development perspective, large areas of legally defined smallholder oil palm resemble industrial-scale plantings. Since our study identified only closed-canopy oil palm stands, our area estimate was lower than the harvested area reported by the Food and Agriculture Organization (FAO), particularly in West Africa, due to the omission of young and sparse oil palm stands, oil palm in nonhomogeneous settings, and semi-wild oil palm plantations. An accurate global map of planted oil palm can help to shape the ongoing debate about the environmental impacts of oil seed crop expansion, especially if other crops can be mapped to the same level of accuracy. As our model can be regularly rerun as new images become available, it can be used to monitor the expansion of the crop in monocultural settings. The global oil palm layer for the second half of 2019 at a spatial resolution of 10 m can be found at https://doi.org/10.5281/zenodo.4473715 (Descals et al., 2021).

show abstract

Section: Comparison With Other Oil Palm Datasetsmentioning

confidence: 99%

Section: Comparison With Other Oil Palm Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations

Descals

Wich

Meijaard

et al. 2021

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…The AOPD in Malaysia and Indonesia from 2001 to 2016 at 100 m resolution is available to the public at https://doi.org/10.5281/zenodo.3467071 (Xu et al, 2019). The dataset includes a set of GeoTIFF images in the WGS_1984_World_mercator projected coordinate system.…”

Section: Data Availabilitymentioning

confidence: 99%

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

et al. 2020

Earth Syst. Sci. Data

Self Cite

View full text Add to dashboard Cite

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).

show abstract

“…(2020) applied the BEAST to detect the change characteristics of wetland vegetation in the Dongting Lake Wetland from 2000 to 2019(Cai et al ., 2020). The BEAST algorithm has been proven to be able to effectively identify subtle changes (Xu et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

Characterizing the long‐term dynamics of aerosol optical depth in the Yangtze River Middle‐Reach urban agglomeration, China

Zhang

et al. 2020

Intl Journal of Climatology

View full text Add to dashboard Cite

Along with the rapid urbanization and economic development, the Yangtze River Middle‐Reach urban agglomeration (YRMR) has suffered from increasingly serious environmental problems. Characterizing the long‐term dynamics of aerosol optical depth (AOD) in YRMR is of important significance for scientific control of aerosol pollutants over this region. Previously, numerous researches have analysed the temporal and spatial characteristics of AOD in different regions through statistical values of AOD time series or using trend analysis methods based on linear regression models. It is challenging to approximate nonlinear trends and identify change points of AOD time series. This paper proposed a new methodology for investigating the dynamics of AOD over the YRMR region on a monthly basis by using the Terra MODIS Collection 6.1 Level 2 aerosol products (MOD04_L2) during 2001–2017. A three‐dimensional spatio‐temporal interpolation method was used to generate gap‐filled monthly AOD maps, and then a generic Bayesian time series decomposition algorithm was applied to analyse the trends and change points of monthly AOD time series. The results showed that the areas with high AOD values were mainly located in the core cities and the connecting belts. Strong positive AOD anomalies had been observed during 2006–2011, whereas the positive anomalies were typically intermingled with negative anomalies and it indicated that the evolution patterns of AOD for different regions were different and influenced by fluctuations. Trend analysis of the monthly mean AOD values indicated that AOD in YRMR increased first, then fluctuated, and finally decreased. Although it was difficult to separate the increasing and fluctuating periods from each other due to the fluctuation, the decreasing trend after 2011 was significant, and a significant drop of AOD had been observed since 2014. Long‐term AOD variations were closely related to national policies, and the results could provide useful references for regional environmental assessment and decision‐making.

show abstract

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

Cited by 8 publications

References 54 publications

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations

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

Characterizing the long‐term dynamics of aerosol optical depth in the Yangtze River Middle‐Reach urban agglomeration, China

Contact Info

Product

Resources

About