Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation

Abstract

Temperate forests are under climatic and economic pressures. Public bodies, NGOs and the wood industry are looking for accurate, current and affordable data driven solutions to intensify wood production while maintaining or improving long term sustainability of the production, biodiversity, and carbon sequestration. Free tools and open access data have already been exploited to produce accurate quantitative forest parameters maps suitable for policy and operational purposes. These efforts have relied on different data sources, tools, and methods that are tailored for specific forest types and climatic conditions. We hypothesized we could build on these efforts in order to produce a generic method suitable to perform as well or better in a larger range of settings. In this study we focus on building a generic approach to create forest parameters maps and confirm its performance on a test site: a maritime pine (Pinus pinaster) forest located in south west of France. We investigated and assessed options related with the integration of multiple data sources (SAR L- and C-band, optical indexes and spatial texture indexes from Sentinel-1, Sentinel-2 and ALOS-PALSAR-2), feature extraction, feature selection and machine learning techniques. On our test case, we found that the combination of multiple open access data sources has synergistic benefits on the forest parameters estimates. The sensibility analysis shows that all the data participate to the improvements, that reach up to 13.7% when compared to single source estimates. Accuracy of the estimates is as follows: aboveground biomass (AGB) 28% relative RMSE, basal area (BA) 27%, diameter at breast height (DBH) 20%, age 17%, tree density 24%, and height 13%. Forward feature selection and SVR provided the best estimates. Future work will focus on validating this generic approach in different settings. It may prove beneficial to package the method, the tools, and the integration of open access data in order to make spatially accurate and regularly updated forest structure parameters maps effortlessly available to national bodies and forest organizations.

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

“…The results indicated that texture features of SAR data were much more helpful than original backscatters to forest volume prediction, which was consistent with existing researches [52,78]. However, textural indices from Sentinel-1 was marginal in this study for volume mapping compared to the previous finding [79]. It was resulted from the decrease in the heterogeneity by texture analysis and large variations of stand volume in the study area.…”

The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures

“…Haralick suggested a GLCM using 14 second order textural features of remotely sensed images [54]. As there exists no evidence in literature which features are best suitable for biomass prediction in grassland or legume-grass mixtures, eight of these GLCM texture features were used (Table 1), which were provided by the processing tool HaralickTextureExtraction of the Orfeo Toolbox library (OTB, open source, [55]) in QGIS. The eight Haralick texture parameters were computed for all four spectral bands separately with settings on default (window size: 2 × 2) and a texture set selection on "simple".…”

Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve

“…A total of 1803 30 m by 30 m plots were located and sampled (Figure 1). At each sample site, tree species, diameter at breast height (DBH, the diameter at 1.3 m from the ground), tree height, soil In this study, 18 predictor variables related to forest parameters were selected and extracted from multi-sensors imagery (Tables 3 and 4) [12,[78][79][80]. ALOS-2 PALSAR-2 yearly mosaic image of 2017 was masked and converted to gamma naught values in decibel unit (dB) from 16-bit digital number (DN) ( Table 4) using the following equation [81]: γ 0 = 10 log 10 DN 2 − 83…”

“…Previous studies explored numerous Sentinel-2 spectral indices. They found that four vegetation indices and two soil indicators were useful in modelling forest age and soil fertility, respectively [78,80,[83][84][85]. The MSI data had 13 spectral bands with 10 m (bands 2-4, 8), 20 m (band 5-7, 8a, [11][12], and 60 m (band 1, 9-10) spatial resolutions.…”