A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand

Danylo, Olha; Pirker, J.; Lemoine, Guido; Ceccherini, Guido; See, Linda; McCallum, Ian; Hadi,; Kraxner, Florian; Achard, Frédéric

doi:10.1038/s41597-021-00867-1

Cited by 54 publications

(54 citation statements)

References 28 publications

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“…The quantification of positive and negative outcomes from plantations, such as yield and aboveground biomass estimate, usually depend on the planting year or tree age because this directly determines the biophysical attributes, including canopy size, tree height, root structure, and soil properties 15 – 17 . Although regional and national tree age datasets/annual datasets for certain plantations have been developed recently 18 – 20 , the planting years of plantations remain unknown at the global scale. To forecast the supply and demand of tree crops and quantify the benefits or costs of planted forests, it is essential to generate and share global planting year maps for plantations.…”

Section: Background and Summarymentioning

confidence: 99%

“…The global plantation extent dataset was composed of the SDPT 35 and Descals’ oil palm map 36 , which can be downloaded at https://www.wri.org/research/spatial-database-planted-trees-sdpt-version-10 and 10.5281/zenodo.4473715, respectively. The validation dataset was composed of Danylo’s oil palm planting year product 20 , Chen’s orchard planting year product in California 19 and FAST 37 from https://dare.iiasa.ac.at/85/ , 10.1016/j.isprsjprs.2019.03.012 and 10.1016/j.scib.2017.03.011, respectively.…”

Section: Data Recordsmentioning

confidence: 99%

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A global map of planting years of plantations

Yang

et al. 2022

Sci Data

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Plantation is an important land use type that differs from natural forests and affects the economy and the environment. Tree age is one of the key factors used to quantify the impact of plantations. However, there is a lack of datasets explicitly documenting the planting years of global plantations. Here we used time-series Landsat archive from 1982 to 2020 and the LandTrendr algorithm to generate global maps of planting years based on the global plantation extent products in Google Earth Engine (GEE) platform. The datasets developed in this study are in a GeoTIFF format with 30-meter spatial resolution by recording gridded specie types and planting years of global plantations. The derived dataset could be used for yield prediction of tree crops and social and ecological cost-benefit analysis of plantations.

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Section: Background and Summarymentioning

confidence: 99%

Section: Data Recordsmentioning

confidence: 99%

A global map of planting years of plantations

Yang

et al. 2022

Sci Data

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“…All pictures were color corrected to improve their interpretability. The pictures were made available online similar to the implementation described by Danylo et al (2021). The online platform chosen to host the pictures and record the selections was SurveyLegend (surveylegend.com).…”

Section: Expert Visual Interpretationmentioning

confidence: 99%

Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem

Rowan

Kalácska

Inamdar

et al. 2021

Front. Environ. Sci.

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Optical remote sensing has been suggested as a preferred method for monitoring submerged aquatic vegetation (SAV), a critical component of freshwater ecosystems that is facing increasing pressures due to climate change and human disturbance. However, due to the limited prior application of remote sensing to mapping freshwater vegetation, major foundational knowledge gaps remain, specifically in terms of the specificity of the targets and the scales at which they can be monitored. The spectral separability of SAV from the St. Lawrence River, Ontario, Canada, was therefore examined at the leaf level (i.e., spectroradiometer) as well as at coarser spectral resolutions simulating airborne and satellite sensors commonly used in the SAV mapping literature. On a Leave-one-out Nearest Neighbor criterion (LNN) scale of values from 0 (inseparable) to 1 (entirely separable), an LNN criterion value between 0.82 (separating amongst all species) and 1 (separating between vegetation and non-vegetation) was achieved for samples collected in the peak-growing season from the leaf level spectroradiometer data. In contrast, samples from the late-growing season and those resampled to coarser spectral resolutions were less separable (e.g., inter-specific LNN reduction of 0.25 in late-growing season samples as compared to the peak-growing season, and of 0.28 after resampling to the spectral response of Landsat TM5). The same SAV species were also mapped from actual airborne hyperspectral imagery using target detection analyses to illustrate how theoretical fine-scale separability translates to an in situ, moderate-spatial scale application. Novel radiometric correction, georeferencing, and water column compensation methods were applied to optimize the imagery analyzed. The SAV was generally well detected (overall recall of 88% and 94% detecting individual vegetation classes and vegetation/non-vegetation, respectively). In comparison, underwater photographs manually interpreted by a group of experts (i.e., a conventional SAV survey method) tended to be more effective than target detection at identifying individual classes, though responses varied substantially. These findings demonstrated that hyperspectral remote sensing is a viable alternative to conventional methods for identifying SAV at the leaf level and for monitoring at larger spatial scales of interest to ecosystem managers and aquatic researchers.

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“…This imagery consists of pansharpened (60 cm) satellite imagery acquired in 2012. Similar to the Picture Pile tool (https://geo-wiki.org/games/picturepile/) implemented by Danylo et al (2021), the Satellite Streetview subsets were arranged and displayed online in Survey Legend (Malmö, Sweden) (Figure 6). A total of 10 visual interpreters examined the 159 ground truth points and determined if Phragmites was present within the buffers or not.…”

Section: Validation Of the Phragmites Extent Mapmentioning

confidence: 99%

Mapping the Extent of Invasive Phragmites australis subsp. australis From Airborne Hyperspectral Imagery

Elmer

Kalácska

Arroyo‐Mora

2021

Front. Environ. Sci.

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Invasive species pose one of the greatest threats to global biodiversity. Early detection of invasive species is critical in order to prevent or manage their spread before they exceed the ability of land management groups to control them. Optical remote sensing has been established as a useful technology for the early detection and mapping of invasive vegetation populations. Through the use of airborne hyperspectral imagery (HSI), this study establishes a target detection methodology used to identify and map the invasive reed Phragmites australis subsp. australis within the entire extent of Îles-de-Boucherville National Park (Quebec, ON, Canada). We applied the Spectral Angle Mapper (SAM) target detection algorithm trained with a high accuracy GNSS ground truth data set to produce a park-wide map illustrating the extent of detected Phragmites. The total coverage of detected Phragmites was 26.74 ha (0.267 km2), which represents 3.28% of the total park area of 814 ha (8.14 km2). The inherent spatial uncertainty of the airborne HSI (∼2.25 m) was accounted for with uncertainty buffers, which, when included in the measurement of detected Phragmites, lead to a total area of 59.17 ha (0.591 km2), or 7.26% of the park. The overall accuracy of the Phragmites map was 84.28%, with a sensitivity of 76.32% and a specificity of 91.57%. Additionally, visual interpretation of the validation ground truth dataset was performed by 10 individuals, in order to compare their performance to that of the target detection algorithm. The overall accuracy of the visual interpretation was lower than the target detection (i.e., 69.18%, with a sensitivity of 59.21% and a specificity of 78.31%). Overall, this study is one of the first to utilize airborne HSI and target detection to map the extent of Phragmites over a moderately large extent. The uses and limitations of such an approach are established, and the methodology described here in detail could be adapted for future remote sensing studies of Phragmites or other vegetation species, native or invasive, at study sites around the world.

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A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand

Cited by 54 publications

References 28 publications

A global map of planting years of plantations

A global map of planting years of plantations

Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem

Mapping the Extent of Invasive Phragmites australis subsp. australis From Airborne Hyperspectral Imagery

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