A COMPARATIVE ANALYSIS OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING RANDOM FOREST MODEL

Providing an accurate above-ground biomass (AGB) map is of paramount importance for carbon stock and climate change monitoring. The main objective of this study is to compare the performance of pixel-based and object-based approaches for AGB estimation of temperate forests in north-eastern of New York State. Second, the capabilities of optical, SAR, and optical + SAR data were investigated. To achieve the goals, the random forest (RF) regression algorithm was used to model and predict the AGB values. Optical (i.e. Landsat 5TM, Landsat 8 OLI, and Sentinel-2), synthetic aperture radar (SAR) (Sentinel-1 and global phased array type L-band SAR (PALSAR/PALSAR-2)), and their integration have been used to estimate the AGB. It is worth mentioning that the airborne light detection and ranging (LiDAR) AGB raster has been used as a reference data for training/testing purposes. The results demonstrate that the OBIA approach enhanced the RMSE of AGB estimation about 5.32 Mg/ha, 8.9 Mg/ha, and 5.29 Mg/ha for optical, SAR, and optical + SAR data, respectively. Moreover, optical + SAR data with the RMSE of 42.63 Mg/ha and R2 of 0.72 for pixel-based and RMSE of 37.31 Mg/ha and R2 of 0.77 for object-based approach provided the best results

Estimation of tropical forest aboveground biomass in Nepal using multiple remotely sensed data and deep learning

This study assessed the prediction accuracy of the forest aboveground biomass (AGB) model using remotely sensed data sources (i.e. airborne laser scanning (ALS), RapidEye, Landsat), and the combination of ALS with RapidEye/Landsat using parametric weighted least squares (WLS) regression. We also analysed the AGB model using random forests, extremely randomized trees, and deep learning stacked autoencoder (SAE) network from nonparametric statistics to compare the performance with WLS regression. We also compared the widths of the 95% confidence intervals for estimates of the mean AGB per unit area using the model-based estimator. The study site in the Terai Arc Landscape, Nepal, comprised 14 protected areas extending from the southern part of Nepal to India and encompassed mosaics of continuous dense forest and tall grassland. The ALS data provided the largest prediction accuracy (0.30–0.35 relative root mean squared error (rRMSE)), whereas RapidEye and Landsat had smaller prediction accuracies (0.48‒0.54 and 0.47‒0.55 rRMSE, respectively) for the estimation of AGB. The combined use of ALS and RapidEye predictors in the AGB model reduced the rRMSE and narrowed the confidence interval compared with ALS alone, but the improvements were minor. The SAE prediction technique provided the largest prediction accuracy, with inputs of combined ALS and RapidEye predictors that yielded an R2 of 0.80, an rRMSE of 0.30, and a confidence interval of 176‒184 compared to other tested prediction techniques. The SAE prediction technique can become more powerful than other tested prediction techniques if properly adjusted and tuned for accurate forest AGB mapping applications. To our knowledge, this is the first study assessing the performance of the SAE in AGB modelling with a range of hyper-parameter values

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass

Estimativas do estoque e dinâmica de biomassa acima do solo utilizando diferentes abordagens estatístcas e dados LiDAR em floresta tropical

Orientadora: Prof.ª Drª Ana Paula Dalla CorteCoorientadores: Dr. Carlos Alberto Silva, Prof. Dr. Carlos Roberto Sanquetta; Prof. Dr. Sebastião do Amaral MachadoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa : Curitiba, 25/02/2019Inclui referênciasÁrea de Concentração: Manejo FlorestalResumo As florestas tropicais são consideradas como os ecossistemas vegetais que mais estocam carbono, devido ao acúmulo de biomassa em seus tecidos durante seu desenvolvimento. A floresta Amazônica se destaca pela sua extensão, sendo considerada a maior floresta tropical do mundo. Assim, frente às principais discussões de mudanças climáticas globais, a floresta Amazônica tem sido apontada como umas das alternativas para redução de gases do efeito estufa, principais responsáveis pelas mudanças climáticas globais. Nesse sentido, esta pesquisa foi dividida em duas etapas: A primeira etapa objetivou realizar uma análise da dinâmica da biomassa acima do solo (AGB) em floresta tropical através de dados de LiDAR aerotransportado, relacionando com mudanças estruturais identificadas automaticamente. A segunda etapa teve como objetivo comparar diferentes abordagens para a estimativa de AGB em floresta tropical, bem como analisar a dinâmica da AGB em uma floresta tropical que foi seletivamente explorada. O primeiro estudo foi conduzido na Floresta Nacional (FLONA) do Jamari, em Rondônia - Brasil. Neste estudo, foram utilizados dados de LiDAR e de inventário florestal. A metodologia foi constituída de processamento dos dados LiDAR para obtenção do modelo de altura de copa (CHM) e das métricas para estimativa de AGB. Dois modelos disponíveis na literatura foram utilizados para as estimativas de AGB de campo e via métricas LiDAR. Após este procedimento, uma análise das mudanças dos estoques de AGB em nível de paisagem e também das mudanças estruturais identificadas foi realizada. O segundo estudo foi conduzido em uma floresta tropical seletivamente explorada no leste da Amazônia. Os dados de campo foram constituídos de 85 parcelas, enquanto que os dados LiDAR foram obtidos em 2012, 2014 e 2017. Modelos no nível da parcela foram primeiramente desenvolvidos usando 6 métricas baseadas na análise de PCA e quatro abordagens de aprendizado de máquina foram implementados e comparados com o modelo de regressão linear (OLS). Os resultados de ambos os capítulos mostraram que o LiDAR é uma ferramenta de grande potencial para a estimativa do estoque e da dinâmica de AGB em florestas tropicais, permitindo desta forma que diferentes análises sejam desenvolvidas. Foram mensuradas de forma automática 40 copas para cada ano no estrato superior da floresta, e com estas, observou-se que houve acréscimos estruturais positivos que não influenciaram nas estimativas dos estoques de AGB. Os resultados do segundo capítulo mostraram que entre as abordagens utilizadas, o método de regressão linear foi superior às demais abordagem, entretanto, abordagens não paramétricas como Random Forest e Support Vector Machine também mostraram potencial para estimativas de AGB e podem ser utilizadas quando necessário. Os resultados do segundo capítulo também revelaram que era possível mapear os estoques de AGB com uma precisão aceitável (RMSE <20%), dessa forma, foi possível analisar com precisão as mudanças ao longo do tempo dos estoques de biomassa em uma floresta seletivamente explorada. Palavras-chave: Amazônia, sensoriamento remoto, predição de biomassa, métodos de estimação.Abstract Tropical forests are considered the most carbon-storing plant ecosystems due to the accumulation of biomass in their tissues during their development. The Amazon rainforest stands out for its extension, being considered the largest tropical forest in the world. Faced with the main discussions of global climate change, the Amazon rainforest has been identified as an alternative to reduce greenhouse gases, which are the main cause of global climate change. Thereby, this research was divided into two stages. The first stage was to perform an analysis of the aboveground biomass (AGB) dynamics in tropical forest from airborne LiDAR data, relating them to the structural changes identified automatically. The second stage was aimed at comparing different approaches to estimate AGB in tropical forest, as well as to analyze the dynamics of AGB in a tropical forest that was selectively explored. The first study was conducted in the Jamari National Forest (FLONA), in Rondônia - Brazil. Airborne LiDAR and forest inventory were used and the methodology was consisted of processing the LiDAR data to obtain Canopy Height Models (CHM) and also the metrics for estimating AGB at plot and landscape level. Two allometric models available in the literature were used for AGB estimates. The first was used to estimate the AGB from the field and the other was used for the estimations via LiDAR metrics. After this, an analysis was made of the changes in AGB stocks at the landscape level and also of the structural changes identified. The second study was conducted in a selectively exploited tropical forest in eastern Amazonia. The field data were composed of 85 plots. LiDAR data were obtained in 2012, 2014 and 2017. Plotlevel models were first developed using 6 metrics based on PCA analysis and four machine learning approaches were implemented and compared with the linear regression model (OLS). The results of both chapters showed that the LiDAR is a tool with great potential for the estimation of the stock and the dynamics of AGB in tropical forests, thus allowing different analyzes to be developed. From two LiDAR surveys (2011-2013), 40 crowns were automatically measured for each year in the superior stratum of the forest, and with these, it was observed that there were positive structural increases that did not influence of AGB stocks. The results of the second chapter showed that among the approaches used, the linear regression method was superior to the other approaches, however, non-parametric approaches such as Random Forest and Support Vector Machine also shown potential for AGB estimates and may be used when required. The results of the second chapter also revealed that it was possible to map AGB stocks with acceptable accuracy (RMSE <20%), so it was possible to safely analyze the changes over time in biomass stocks in a forest that was selectively exploited. Keywords: Amazon, remote sensing, biomass prediction, methods

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion