Small room for compromise between oil palm cultivation and primate conservation in Africa

Despite growing awareness about its detrimental effects on tropical biodiversity, land conversion to oil palm continues to increase rapidly as a consequence of global demand, profitability, and the income opportunity it offers to producing countries. Although most industrial oil palm plantations are located in Southeast Asia, it is argued that much of their future expansion will occur in Africa. We assessed how this could affect the continent’s primates by combining information on oil palm suitability and current land use with primate distribution, diversity, and vulnerability. We also quantified the potential impact of large-scale oil palm cultivation on primates in terms of range loss under different expansion scenarios taking into account future demand, oil palm suitability, human accessibility, carbon stock, and primate vulnerability. We found a high overlap between areas of high oil palm suitability and areas of high conservation priority for primates. Overall, we found only a few small areas where oil palm could be cultivated in Africa with a low impact on primates (3.3 Mha, including all areas suitable for oil palm). These results warn that, consistent with the dramatic effects of palm oil cultivation on biodiversity in Southeast Asia, reconciling a large-scale development of oil palm in Africa with primate conservation will be a great challenge

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

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)

Oil Palm (Elaeis guineensis) Mapping with Details: Smallholder versus Industrial Plantations and their Extent in Riau, Sumatra

Oil palm is rapidly expanding in Southeast Asia and represents one of the major drivers of deforestation in the region. This includes both industrial-scale and smallholder plantations, the management of which entails specific challenges, with either operational scale having its own particular social and environmental challenges. Although, past studies addressed the mapping of oil palm with remote sensing data, none of these studies considered the discrimination between industrial and smallholder plantations and, furthermore, between young and mature oil palm stands. This study assesses the feasibility of mapping oil palm plantations, by typology (industrial versus smallholder) and age (young versus mature), in the largest palm oil producing region of Indonesia (Riau province). The impact of using optical images (Sentinel-2) and radar scenes (Sentinel-1) in a Random Forest classification model was investigated. The classification model was implemented in a cloud computing system to map the oil palm plantations of Riau province. Our results show that the mapping of oil palm plantations by typology and age requires a set of optimal features, derived from optical and radar data, to obtain the best model performance (OA = 90.2% and kappa = 87.2%). These features are texture images that capture contextual information, such as the dense harvesting trail network in industrial plantations. The study also shows that the mapping of mature oil palm trees, without distinction between smallholder and industrial plantations, can be done with high accuracy using only Sentinel-1 data (OA = 93.5% and kappa = 86.9%) because of the characteristic backscatter response of palm-like trees in radar scenes. This means that researchers, certification bodies, and stakeholders can adequately detect mature oil palm stands over large regions without training complex classification models and with Sentinel-1 features as the only predictive variables. The results over Riau province show that smallholders represent 49.9% of total oil palm plantations, which is higher than reported in previous studies. This study is an important step towards a global map of oil palm plantations at different production scales and stand ages that can frequently be updated. Resulting insights would facilitate a more informed debate about optimizing land use for meeting global vegetable oil demands from oil palm and other oil crops

Developing and applying a multi-purpose land cover validation dataset for Africa

The production of global land cover products has accelerated significantly over the past decade thanks to the availability of higher spatial and temporal resolution satellite data and increased computation capabilities. The quality of these products should be assessed according to internationally promoted requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 validation). Providing updated accuracies for the yearly maps would require considerable effort for collecting validation datasets. To save time and effort on data collection, validation datasets should be designed to suit multiple map assessments and should be easily adjustable for a timely validation of new releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-purpose assessments and its applicability is demonstrated in three different assessments focusing on validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. The validation dataset is generated primarily to validate the newly released 100 m spatial resolution land cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 100 m × 100 m. Within site, reference land cover information was collected at 100 subpixels of 10 m × 10 m allowing the land cover information to be suitable for different resolution and legends. Firstly, using this dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6 ± 2.1% (at 95% confidence level) for the African continent. Fraction cover products were found to have mean absolute errors of 9.3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). Overall accuracies for these perspectives vary between 73.7% ± 2.1% and 93.5% ± 0.9%, depending on the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the Globeland30-2010 map at 30 m spatial resolution. Using the subpixel level validation data, we derived 15,252 sample pixels at 30 m spatial resolution. Based on these sample pixels, the overall accuracy of the Globeland30-2010 map was found to be 66.6 ± 2.4% for Africa. The three assessments exemplify the applicability of multi-purpose validation datasets which are recommended to increase map validation efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or updating the dataset with sample sites in identified change areas

Free satellite data key to conservation

Biodiversity is in crisis, with extinction rates orders of magnitude higher than background levels ([ 1 ][1]). Underfunded conservationists need to target their limited resources effectively. Over the past decade, satellite remote sensing has revolutionized our ability to monitor biodiversity globally, and is now used routinely, especially by nongovernmental organizations, to detect changes, set priorities, and target conservation action. The U.S. Geological Survey (USGS) unlocked high-resolution Landsat data in 2008 ([ 2 ][2]), making data available online ([ 3 ][3]), and the Copernicus program from the European Commission subsequently made their data available as well ([ 4 ][4]). These resources have been instrumental to biodiversity research. Assessments of environmental changes such as deforestation are now readily available. The current spatial and spectral resolution of Landsat data make them appropriate to many conservation applications, and although they are not always ideal, pragmatic researchers with limited resources use them regularly. Conservationists have already called for these data to remain free ([ 5 ][5]). Consequently, the news that USGS may charge for data ([ 6 ][6]) is deeply troubling. USGS has recently convened an advisory committee to determine whether users would be prepared to pay for increased spectral and spatial resolution images ([ 7 ][7]). Requiring users to pay would put these images beyond the reach of conservationists. It would halt time-series analyses that have been useful in monitoring the effects of climate change, land-cover change, and ocean surfaces, likely hindering the achievement of the Sustainable Development Goals ([ 8 ][8]). We urge the USGS to reconsider their position and continue to provide data from the Landsat program freely to all users. 1. [↵][9]1. J. DeVos et al ., Cons. Biol. 29, 452 (2015). [OpenUrl][10] 2. [↵][11]1. C. Woodcock et al . Science 320, 1011 (2008). [OpenUrl][12][CrossRef][13][PubMed][14] 3. [↵][15]USGS, Earth Explorer ([https://earthexplorer.usgs.gov/][16]). 4. [↵][17]European Commission, Copernicus (). 5. [↵][18]1. W. Turner et al ., Biol. Conserv. 182, 173 (2015). [OpenUrl][19] 6. [↵][20]1. G. Popkin , Nature 556, 417 (2018). [OpenUrl][21] 7. [↵][22]USGS, Landsat Advisory Group undertakes a Landsat Cost Recovery Study (2018); [www.usgs.gov/center-news/landsat-advisory-group-undertakes-a-landsat-cost-recovery-study][23]. 8. [↵][24]Sustainable Development Goals ([https://sustainabledevelopment.un.org/?menu=1300][25]). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #xref-ref-1-1 "View reference 1 in text" [10]: {openurl}?query=rft.jtitle%253DCons.%2BBiol.%26rft.volume%253D29%26rft.spage%253D452%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [11]: #xref-ref-2-1 "View reference 2 in text" [12]: {openurl}?query=rft.jtitle%253DScience%26rft.volume%253D320%26rft.spage%253D1011%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.320.5879.1011a%26rft_id%253Dinfo%253Apmid%252F18497274%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [13]: /lookup/external-ref?access_num=10.1126/science.320.5879.1011a&link_type=DOI [14]: /lookup/external-ref?access_num=18497274&link_type=MED&atom=%2Fsci%2F361%2F6398%2F139.2.atom [15]: #xref-ref-3-1 "View reference 3 in text" [16]: http://earthexplorer.usgs.gov/ [17]: #xref-ref-4-1 "View reference 4 in text" [18]: #xref-ref-5-1 "View reference 5 in text" [19]: {openurl}?query=rft.jtitle%253DBiol.%2BConserv.%26rft.volume%253D182%26rft.spage%253D173%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: #xref-ref-6-1 "View reference 6 in text" [21]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D556%26rft.spage%253D417%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [22]: #xref-ref-7-1 "View reference 7 in text" [23]: http://www.usgs.gov/center-news/landsat-advisory-group-undertakes-a-landsat-cost-recovery-study [24]: #xref-ref-8-1 "View reference 8 in text" [25]: http://sustainabledevelopment.un.org/?menu=130

WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping

The challenge of global food security in the face of population growth, conflict and climate change requires a comprehensive understanding of cropped areas, irrigation practices and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereals maps, seasonal irrigation maps, seasonal active cropland maps, and confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. The presented products are fully validated, e.g., global user's and producer's accuracies for the annual temporary crop product are 88.5 % and 92.1 %, respectively. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and push the boundaries for global agricultural mapping from space. The global products are available at https://doi.org/10.5281/zenodo.7875104 (Van Tricht et al., 2023)

A sourcebook of methods and procedures for monitoring essential biodiversity variables in tropical forest with remote sensing [Report version UNCBD COP-13, GOFC-GOLD Land Cover Project Office]

International audienceStandardised and harmonised biodiversity data and monitoring methods are required in order to assess how tropical forest biodiversity is evolving at the global scale, and what the drivers of change are. Collaborative efforts towards the development of such harmonised monitoring methods are carried out by national and regional forest agencies, the scientific and research community, and NGOs. These standardisation efforts are supported by the Essential Biodiversity Variables (EBV) concept that is currently developed by GEO-BON, and by Space Agencies and the Earth Observation research community at large. This sourcebook is developed by a wide group of forest researchers and practitioners, to promote the best operational monitoring practices based on scientific literature, and consensus. The first version of the biodiversity sourcebook was released during the IPBES Plenary 2017 and follows the 13th UNCBD Conference of Parties (COP) held in December 2016. Updates will be made on a yearly basis following policy, scientific, and technical developments. The biodiversity sourcebook is accessible in pdf format for free from the GOFC-GOLD Land Cover Office website and the GEO BON website

First Results From the Phenology-Based Synthesis Classifier Using Landsat 8 Imagery

A fully automatic phenology-based synthesis (PBS) classification algorithm was developed to map land cover based on medium spatial resolution satellite data using the Google Earth Engine cloud computing platform. Vegetation seasonality, particularly in the tropical dry regions, can lead conventional algorithms based on a single date image classification to "misclassify" land cover types, as the selected date might reflect only a particular stage of the natural phenological cycle. The PBS classifier operates with occurrence rules applied to a selection of single date image classifications of the study area to assign the most appropriate land cover class. Since the launch of Landsat 8 in 2013, it has been possible to acquire imagery at any point on the Earth every 16 days with exceptional radiometric quality. The relatively high global acquisition frequency and the open data policy allow near-real-time land cover mapping and monitoring with automated tools such as the PBS classifier. We mapped four protected areas and their 20-km buffer zones from different ecoregions in Sub-Saharan Africa using the PBS classifier to present its first results. Accuracy assessment was carried out through a visual interpretation of very high resolution images using a Web geographic information system interface. The combined overall accuracy was over 90%, which demonstrates the potential of the classifier and the power of cloud computing in geospatial sciences