Toward a general tropical forest biomass prediction model from very high resolution optical satellite images

Ploton, Pierre; Barbier, Nicolas; Couteron, Pierre; Antin, Cécile; Narayanan, Ayyappan; Balachandran, N.; Barathan, N.; Bastin, Jean-François; Chuyong, Georges; Dauby, Gilles; Droissart, Vincent; Gastellu-Etchegorry, Jean-Philippe; Kamdem, Narcisse Guy; Kenfack, David; Libalah, Moses B.; Mofack, Gislain Ii; Takoudjou, Stéphane Momo; Pargal, Sourabh; Pétronelli, Pascal; Proisy, Christophe; Réjou‐Méchain, Maxime; Sonké, Bonaventure; Texier, Nicolas; Thomas, Daniel; Verley, Philippe; Dongmo, D. Zebaze; Berger, Uta; Pélissier, Raphaël

doi:10.1016/j.rse.2017.08.001

Cited by 56 publications

(42 citation statements)

References 62 publications

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“…Hence this is a very important issue for the automated processing of VHR images. However, procedures for the correction of such geometries (Barbier and Couteron 2015) and the fusion of remote sensing metrics (Ploton et al 2017) help to address some of these issues.…”

Section: Problems Associated With the Quality Of Vhr Imagerymentioning

confidence: 99%

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

et al. 2019

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The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change.

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Section: Problems Associated With the Quality Of Vhr Imagerymentioning

confidence: 99%

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

et al. 2019

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“…We performed the pre-processing using the free software Sentinel Toolbox, which allows the derivation of backscatter coefficients and processing of the range Doppler terrain corrections using the 3 arc-seconds SRTM Digital Elevation Model. We derived 9 indicators from the grey level co-occurrence matrix (GLCM) that are based on the statistical relationship between the values of the pixels within a 9 × 9 pixels window [60]. These indicators are relevant to quantify forest canopy texture [61].…”

Section: • Modismentioning

confidence: 99%

“…The integration of Sentinel-1 and Sentinel-2 time series would be an interesting way to improve the model performance combined with daily Planet images that can map at 3-m resolution the forest canopy. Very high resolution remote sensing offers a unique opportunity to characterize degraded forest structure using canopy texture mapping [60,83]. These analysis provide perspective for future space-borne LIDAR and RADAR data satellites (US GEDI mission and ESA Biomass) which will enable us to provide data sets on forest structure dynamics and forest biomass around the pantropical belt [84].…”

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confidence: 99%

The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest

Bourgoin

Blanc

Bailly

et al. 2018

Forests

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In the agricultural frontiers of Brazil, the distinction between forested and deforested lands traditionally used to map the state of the Amazon does not reflect the reality of the forest situation. A whole gradient exists for these forests, spanning from well conserved to severely degraded. For decision makers, there is an urgent need to better characterize the status of the forest resource at the regional scale. Until now, few studies have been carried out on the potential of multisource, freely accessible remote sensing for modelling and mapping degraded forest structural parameters such as aboveground biomass (AGB). The aim of this article is to address that gap and to evaluate the potential of optical (Landsat, MODIS) and radar (ALOS-1 PALSAR, Sentinel-1) remote sensing sources in modelling and mapping forest AGB in the old pioneer front of Paragominas municipality (Para state). We derived a wide range of vegetation and textural indices and combined them with in situ collected AGB data into a random forest regression model to predict AGB at a resolution of 20 m. The model explained 28% of the variance with a root mean square error of 97.1 Mg·ha −1 and captured all spatial variability. We identified Landsat spectral unmixing and mid-infrared indicators to be the most robust indicators with the highest explanatory power. AGB mapping reveals that 87% of forest is degraded, with illegal logging activities, impacted forest edges and other spatial distribution of AGB that are not captured with pantropical datasets. We validated this map with a field-based forest degradation typology built on canopy height and structure observations. We conclude that the modelling framework developed here combined with high-resolution vegetation status indicators can help improve the management of degraded forests at the regional scale. IntroductionDeforestation and forest degradation are major sources of greenhouse gas emissions [1,2], contributing to forest carbon losses [3], global climate change, affecting biodiversity [4] and the entire forest ecosystem. While deforestation refers to the rapid conversion from forest to non-forest areas, degradation implies changes in the forest structure with no change in land use [5,6]. In Amazonia, over the last decades, deforestation and forest degradation have shaped the rural landscape, resulting in a complex mosaic of fragmented forests associated with agricultural lands [7]. A total area of 766,448.5 km 2 was cleared in 2015 [8], representing 20% of Amazonia [9,10].Since 2005, deforestation in Brazil has drastically decreased thanks to coercive measures taken by the Brazilian government associated with private initiatives (soy and beef moratoria), among other factors [11]. However, these measures are not effective for reducing forest degradation [12,13]. Most of the remaining forested lands are degraded due to the accumulation over time and space of severe degradation processes mainly triggered by anthropogenic impacts through unsustainable logging practices, fire, shifting cultivation ...

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“…Canopy grain evidenced by the TG-2 was no longer associated with the forest canopy but to shrub canopy texture that was visible only with panchromatic information. If the potential of FOTO for tropical forest management is well-known (for e.g., [67][68][69]), its aptitude has not been demonstrated for the monitoring of the development stage of continuous covers of shrubs. Textural information derived from panchromatic images shows potential in that matter and it may open up promising opportunities for the monitoring of vegetation flammability through biomass estimation which is a main issue for fire risk management in Mediterranean areas [70].…”

Section: Implication Of the Use Of Spaceborne Data For Monitoring Vegmentioning

confidence: 99%

Complementarity between Textural and Radiometric Indices From Airborne and Spaceborne Multi VHSR Data: Disentangling the Complexity of Heterogeneous Landscape Matrix

et al. 2019

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The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pléiades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics; related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.

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Toward a general tropical forest biomass prediction model from very high resolution optical satellite images

Cited by 56 publications

References 62 publications

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

The Potential of Multisource Remote Sensing for Mapping the Biomass of a Degraded Amazonian Forest

Complementarity between Textural and Radiometric Indices From Airborne and Spaceborne Multi VHSR Data: Disentangling the Complexity of Heterogeneous Landscape Matrix

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