Characterizing the Congo Basin Forests by a Detailed Forest Typology Enriched with Forest Biophysical Variables

Dalimier, Juliette; Claverie, Martin; Goffart, Benjamin; Jungers, Quentin; Lamarche, Céline; Maet, Thomas De; Defourny, Pierre

doi:10.1109/igarss47720.2021.9553905

Cited by 3 publications

(7 citation statements)

References 5 publications

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“…4. It is composed of labeled and unlabeled pixels in a boundary zone around the boundary of two regions 6 . Several buffer sizes B have been investigated: B ∈ {100, 200, 500, 1000, 1500, 2000} meters, the total width of the buffer being equal to 2 × B.…”

Section: A Data Generationmentioning

confidence: 99%

“…Several buffer sizes B have been investigated: B ∈ {100, 200, 500, 1000, 1500, 2000} meters, the total width of the buffer being equal to 2 × B. All available labeled pixels were selected except those included 6 Examples with real data are given in Supplementary Material, Section II.…”

Section: A Data Generationmentioning

confidence: 99%

“…They are essential to understand the challenges related to climate change [2]. Since their launch in 2015 and 2017, SITS from the twin Sentinel-2 satellites have already shown a clear benefit in biodiversity monitoring [3], [4], forest mapping [5], [6], water quality [7], [8], agricultural monitoring [9], [10] or disaster management [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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Land Cover Classification With Gaussian Processes Using Spatio-Spectro-Temporal Features

Bellet

Fauvel

Inglada

2023

IEEE Trans. Geosci. Remote Sensing

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In this article, we propose an approach based on Gaussian Processes (GP) for large scale land cover pixel-based classification with Sentinel-2 satellite image time-series (SITS). We used a sparse approximation of the posterior combined with variational inference to learn the GP's parameters. We applied stochastic gradient descent and GPU computing to optimize our GP models on massive data sets. The proposed GP model can be trained with hundreds of thousands of samples, compared to few thousands for traditional GP methods. Moreover, we included the spatial information by adding the geographic coordinates into the GP's covariance function to efficiently exploit the spatiospectro-temporal structure of the SITS. We ran experiments with Sentinel-2 SITS of the full year 2018 over an area of 200 000 km 2 (about 2 billion pixels) in the south of France, which is representative of an operational setting. Adding the spatial information significantly improved the results in terms of classification accuracy. With spatial information, GP models have an overall accuracy of 79.8. They are more than three points above Random Forest (the method used for current operational systems) and more than one point above a multi-layer perceptron. Compared to a Transformer-based model (which provides state of the art results in the literature, but are not applied in operational systems), GP models are only one point below.

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Section: A Data Generationmentioning

confidence: 99%

Section: A Data Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Land Cover Classification With Gaussian Processes Using Spatio-Spectro-Temporal Features

Bellet

Fauvel

Inglada

2023

IEEE Trans. Geosci. Remote Sensing

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“…Paradoxically, the system of agriculture practiced similarly remains an important threat to the CBF’s wealth of biodiversity. It is estimated that in Cameroon, for instance, about 80% of the deforestation is due to small-scale farmers using extensive slash and burn techniques (Dalimier et al, 2021; Tyukavina et al, 2018). Reversing deforestation in the Congo Basin in particular, and increasing agricultural productivity to meet the demands of a growing population while promoting conservation are mutual objectives, which are vital for sustainable development (Tegegne et al, 2016; Molua, 2012; Endamana et al, 2010; Nkem et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…km, the forest extends to six countries namely Cameroon, the Republic of Congo, the Democratic Republic of Congo (DRC), the Central African Republic (CAR), Gabon and Equatorial Guinea; with about 65 million people living inside or at the margins of the Basin, depending on it directly for livelihood. Subsistence small-scale slash-and-burn shifting cultivation is the dominant economic activity and farm practice of the inhabitants (Dalimier et al, 2021; Couturier, 2019; Tyukavina et al, 2018). Poverty and underdevelopment are significant in the region.…”

Section: Introductionmentioning

confidence: 99%

Climate-Smart Conservation Agriculture, Farm Values and Tenure Security: Implications for Climate Change Adaptation and Mitigation in the Congo Basin

Molua

Sonwa²,

Bele³

et al. 2023

Tropical Conservation Science

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Background and Research Aims: Agriculture through deforestation is an important threat to biodiversity conservation in the Congo Basin’s tropical forest. The policy challenge is not only to promote adaptation to perceived climate change but also to promote forest conservation. The aim of this study is to provide empirical evidence on the impact of farm-level investments in climate-smart agricultural practices related to conservation agriculture in some Congo Basin countries. The hypothesis is that property rights to land and trees play a fundamental role in governing the patterns of investment, forestland management for conservation, as well as in the profitability of agriculture. Methods: A Simulated Maximum Likelihood Estimation using a Mixed Logit model is used to test farmers’ choice of agricultural system and a farmland value model for each agricultural system which includes determinants of tenure or property rights, climate, soils, and socioeconomic variables such as education and gender. The data was collected from more than 600 farms covering 12 regions and 45 divisions in 3 countries, Cameroon, the Central African Republic and the Democratic Republic of Congo. Results: Farmers choose one of three agricultural systems to maximize farm profit mindful of the current tenure regime and environmental conditions. Conservation agriculture techniques within climate-smart practices show benefits for smallholder farmers through improvements in soil health, soil moisture retention and enhanced crop yields. The rights to access, withdraw, manage, as well as exclude others from land and trees affect both the farmers' choice of system and the profit earned from the chosen system. Conclusion: Farm-level investments improve farm incomes and enhance conservation effort for farmers perceiving climate change. Implications for Conservation: Climate change adaptation through planting of trees improves soil stability, restores ecosystems and creates a safe haven for biodiversity. Secure land tenure promotes better forestland management and reduces land degradation in vulnerable communities.

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R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

Lemenkova

Debeir

2022

Applied Sciences

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In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.

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Characterizing the Congo Basin Forests by a Detailed Forest Typology Enriched with Forest Biophysical Variables

Cited by 3 publications

References 5 publications

Land Cover Classification With Gaussian Processes Using Spatio-Spectro-Temporal Features

Land Cover Classification With Gaussian Processes Using Spatio-Spectro-Temporal Features

Climate-Smart Conservation Agriculture, Farm Values and Tenure Security: Implications for Climate Change Adaptation and Mitigation in the Congo Basin

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

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