Adyne Cardoso da Costa, José Roberto Rodrigues Pinto, Eder Pereira Miguel, Gabrielle de Oliveira Xavier, Ben Hur Marimon, Eraldo Aparecido Trondoli Matricardi
Measurement of forest biomass is a time-, money-, and labor-consuming activity. Developing methodological approaches for quantifying biomass in natural ecosystems has motivated countless investigations in several vegetation types worldwide. In this study, we developed a model to estimate the above-ground biomass (AGB) in a study site spatially located in Western Pará state, Brazilian Amazon. We applied artificial neural networks and remotely sensed data for adjusting our model based on forest inventory data. We tested four vegetation indices retrieved from a Landsat-5 thematic mapper scene and assessed their correlations with the field measured AGB. We trained 286 artificial neural networks using the vegetation stratus (two phytophysiognomies: dense lowland ombrophilous forest and dense submontane ombrophilous forest) and the normalized difference vegetation index (NDVI) and aerosol-free vegetation index as predictors variables of the AGB. We selected the artificial neural network model showing higher correlation coefficient and concentration of residuals in the center classes, which indicates higher predictive capabilities of biomass estimation. The generated model architecture (4-13-1) was composed by 4 predictor neurons in the input layer, 13 neurons in the hidden layer, activated by logistic functions, and a single neuron (the AGB in ton · ha − 1) in the output layer, activated by an identity function. We observed that the NDVI and aerosol free vegetation index showed the best performance to estimate the AGB in the study area. Using artificial neural network and a Landsat-5 image combined, we were able to accurately predict (estimation error of ∼20 % ) AGB in tropical forest. This is a promising methodological approach that can be applied to assess ecosystem services related to carbon stock in tropical regions.
Guido Vicente Briceño Castillo, Lucas José Mazzei de Freitas, Victor Almeida Cordeiro, Jorge Breno Palheta Orellana, Jorge Luis Reategui-Betancourt, Laszlo Nagy, Eraldo Aparecido Trondoli Matricardi
Several studies have assessed forest disturbance in tropical forests using Landsat imagery. However, the spatial resolution (30 m) of Landsat images has often been considered too coarse to accurately detect the extent and impacts of selective logging. The Sentinel-2 satellite launched in 2015 has been providing images at spatial resolutions of 10 to 20 m and those images have shown an improved potential for detecting forest disturbances in tropical regions. We compared Landsat-8 and Sentinel-2 imagery for detecting selective logging in a rain forest site in the Brazilian Amazon. The aerosol-free modified soil adjusted vegetation index (MSAVI_af) was retrieved from the satellite images acquired in August 2020 immediately following logging. A robust reference dataset of very-high-resolution imagery (0.5 m) acquired using a complementary metal oxide semiconductor sensor (visible bands) onboard of an unmanned aerial vehicle was used to image the area of interest and a map derived from it was used to assess the classification accuracies made using satellite-derived data. The overall accuracy of the classified Sentinel-2 and Landsat-8 images varied between 54% and 83%, depending on the applied classification parameters for distinguishing undisturbed from disturbed forest canopy. Images acquired using the UAV allowed us to detect subtle impacts of canopy openings by selective logging activities. Images acquired using the UAV allowed the detection of small canopy openings, but not Sentinel-2 or Landsat-8. Sentinel-2 provided more details of canopy disturbances than Landsat image. Our classification approach is fully implementable on the Google Earth Engine platform and is a promising technique to monitor selective logging impacts in tropical forests.
Forest inventory and monitoring is normally carried out based on field measurements of biophysical attributes such as diameter, height, and the number of trees, which is a labor, time, and money consuming activity. Satellite data associated with artificial intelligence tools are alternative approaches to estimate forest parameters at large scales. We assessed correlation of forest variables measured in the field with different vegetation indices (VIs) (normalized difference vegetation index, soil adjusted vegetation index, modified soil adjusted vegetation index, enhanced vegetation index, and enhanced vegetation index adjusted 2.2), retrieved from Sentinel-2 imagery to predict the volume of commercial trees (VCC) showing a minimum commercial diameter ( MCD ) ≥ 50 cm in a sustainable forest management plan in the Brazilian Amazon region. A total of 150 artificial neural networks (ANNs) of the multilayer perceptron type were trained and supervised. Subsequently, the five best-performing networks were retained based on the fit and accuracy statistics. The ANN-1 showed the best statistical results [root-mean-square error <10 % and correlation coefficient ( r ) > 0.98] to predict the VCC using as input variables the number of trees per hectare showing MCD ≥ 50 cm and all tested VIs. Our study shows promising results that may contribute to improving forest management planning at large scales in remote areas in tropical regions.
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