Linear vs non-linear learning methods A comparative study for forest above ground biomass, estimation from texture analysis of satellite images

Hippolyte Tapamo (1, 2) , Adamou Mfopou (3, 2) , Blaise Ngonmang (3, 2) , Pierre Couteron (4) , Olivier Monga (5, 6)

1 Département d'Informatique [Yaoundé I]
2 LIRIMA - Laboratoire International de Recherche en Informatique et Mathématiques Appliquées
3 UMMISCO - Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]
4 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations
5 IRD - Institut de Recherche pour le Développement
6 UPMC - Université Pierre et Marie Curie - Paris 6

The aboveground biomass estimation is an important question in the scope of Reducing Emission from Deforestation and Forest Degradation (REDD framework of the UNCCC). It is particularly challenging for tropical countries because of the scarcity of accurate ground forest inventory data and of the complexity of the forests. Satellite-borne remote sensing can help solve this problem considering the increasing availability of optical very high spatial resolution images that provide information on the forest structure via texture analysis of the canopy grain. For example, the FOTO (FOurier Texture Ordination) proved relevant for forest biomass prediction in several tropical regions. It uses PCA and linear regression and, in this paper, we suggest applying classification methods such as k-NN (k-nearest neighbors), SVM (support vector machines) and Random Forests to texture descriptors extracted from images via Fourier spectra. Experiments have been carried out on simulated images produced by the software DART (Discrete Anisotropic Radiative Transfer) in reference to information (3D stand mockups) from forests of DRC (Democratic Republic of Congo), CAR (Central African Republic) and Congo. On this basis, we show that some classification techniques may yield a gain in prediction accuracy of 18 to 20%

Mots clés: Aboveground biomass; estimation; supervised learning; regression; support vector machines; random forests; k-nearest neighbor.

Domaines: Informatique [cs]; Mathématiques [math]

Hippolyte Tapamo, Adamou Mfopou, Blaise Ngonmang, Pierre Couteron, Olivier Monga. Linear vs non-linear learning methods A comparative study for forest above ground biomass, estimation from texture analysis of satellite images. Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, 2014, Volume 18, 2014, pp.114--131. ⟨10.46298/arima.1982⟩. ⟨hal-01300087⟩