Comparing synthetic aperture radar and LiDAR for above-ground biomass estimation in Glen Affric, Scotland

Quantifying above-ground biomass (AGB) and carbon sequestration has been a significant focus of attention within the UNFCCC and Kyoto Protocol for improvement of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of research has been carried out in relatively flat and homogeneous forests (Ranson & Sun, 1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al., 2005), yet forests in the highlands, which generally form heterogeneous forest cover and sparse woodlands with mountainous terrain have been largely neglected in AGB studies (Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al., 2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately 28% of the total global forest area (Price and Butt, 2000), a better understanding of the slope effects is of primary importance in AGB estimation. The main objective of this research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using both SAR and LiDAR data. Two types of Synthetic Aperture Radar (SAR) data were used in this research: TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were acquired on 09 June 2007. Two field measurement campaigns were carried out, one of which was done from winter 2006 to spring 2007 where physical parameters of trees in 170 circular plots were measured by the Forestry Commission team. Another intensive fieldwork was organised by myself with the help of my fellow colleagues and it comprised of tree measurement in two transects of 200m x 50m at a relatively flat and dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is estimated for both the transects to investigate the effectiveness of the proposed method at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data for AGB estimation by investigating the relationship between the SAR backscattering coefficient and AGB and also the relationship between the decomposed scattering mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the result from the backscatter-AGB analysis show that these forests present a challenge for simple AGB estimation. As an alternative, polarimetric techniques were applied to the problem by decomposing the backscattering information into scattering mechanisms based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR data are compared with AGB derived from LiDAR data. Since tree height is often correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was performed before AGB of individual trees were calculated allometrically. Results were validated by comparison to the fieldwork data. The amount of overestimation varies across the different canopy conditions. These results give some indication of when to use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with very accurate height measurement and consequent three-dimensional (3D) stand profiles which allows investigation into the relationship between height, number density and AGB, it's limited to small coverage area, or large areas but at large cost. ALOS PALSAR, on the other hand, can cover big coverage area but it provide a lower resolution, hence, lower estimation accuracy

Estimativa de biomassa na região amazônica utilizando técnicas de aprendizado de máquina

Tese (doutorado) — Universidade de Brasília, Instituto de Geociências, Pós-Graduação em Geociências Aplicadas, 2021.No ano de 2016 mais de 190 países participaram da 21ª Conferência das Partes das Nações Unidas sobre Mudança Climática, realizada em Paris. Apesar de intensos trabalhos visando elaborar um tratado, os resultados não atenderam às expectativas devido à falta de metodologias que medem com precisão a quantidade de biomassa florestal. Imagens de sensoriamento remoto podem ser usadas para que seja realizada uma quantificação mais precisa e viável da biomassa existente em regiões de difícil acesso, como a região amazônica, com destaque para as imagens na faixa do micro-ondas, mais especificamente as de radares. Em função da grande quantidade de dados de sensoriamento remoto disponíveis, faz-se necessário o desenvolvimento de técnicas e ferramentas que visem organizá-los e analisá-los de forma inteligente e automática, como as técnicas de Aprendizado de Máquina (Machine Learning). A presente tese tem por objetivo geral desenvolver e aplicar uma metodologia para estimar a quantidade de biomassa arbórea em uma área da região amazônica, a partir de dados de SAR, utilizando técnicas de Aprendizado de Máquina. As etapas metodológicas de tese encontra-se divididas em três artigos técnicos sequenciais que cobrem os objetivos propostos. O primeiro artigo possui como hipótese a possibilidade de ajuste da altura interferométrica, atributos de InSAR, a partir da identificação de áreas de solo exposto, isto é, onde o valor é teoricamente igual a 0 (zero). Além de inovadora, a hipótese previa o ajuste do modelo digital da região visando aprimorar a modelagem referente à estimativa de biomassa. Entretanto, como resultado, o método proposto no primeiro artigo não possibilitou a melhora significativa da estimativa de biomassa florestal, não sendo adotado nas próximas etapas do trabalho. O segundo artigo dá continuidade ao primeiro e apresenta a aplicação de técnicas de Aprendizado de Máquina sobre os atributos de SAR extraídos dos dados disponíveis. De forma inédita avalia e compara modelos de estimativa de biomassa baseados em atributos qualitativos e quantitativos. O segundo artigo conclui que as diferentes regiões da Floresta Amazônica e suas respectivas características demandam modelos e técnicas específicas, não se enquadrando em um único padrão. Neste caso não foi possível identificar uma única técnica de Aprendizado de Máquina que se mostrasse como a mais adequada ao objetivo, apesar dos melhores resultados apontarem para o uso das redes neurais artificiais. O terceiro e último artigo conclui o trabalho da presente tese por meio da análise e construção de produtos temáticos de biomassa. Neste último artigo é apresentado um sistema computacional desenvolvido que visa otimizar o processo de categorização, necessário à representação visual da geoinformação. Os resultados obtidos no terceiro artigo mostram que o algoritmo de Otimização de Categorização proposto demonstrou capacidade de encontrar novos subintervalos de categorias que aumentaram o índice de concordância Kappa. Como resultado, foram construídos produtos temáticos que apresentaram acurácia temática superior aos obtidos pelos métodos clássicos de categorização. Juntamente, do ponto de vista computacional, a heurística proposta no algoritmo possibilitou a identificação de resultados de forma eficiente, evitando os altos custos de processamento. A hipótese proposta na tese, isto é, de que a aplicação de técnicas de aprendizado de máquina sobre dados de SAR permitem obter a estimativa de biomassa da região amazônica com erros abaixo de 20%, atendendo os padrões preceituados por organismos internacionais, não foi confirmada. Os resultados obtidos nos modelos elaborados são classificados somente como moderados. Dentre os fatores que podem ter contribuído para este resultado, está a quantidade reduzida de amostras de biomassa, com pequena variação de valores, o que prejudicou o ajuste dos modelos gerados e o acesso restrito aos dados de SAR das bandas X e P, não sendo possível gerar novos atributos coerentes.In 2016, more than 190 countries participated in the 21st United Nations Conference of Parties on Climate Change, held in Paris. Despite the intense work aiming at preparing a treaty, the results did not meet expectations due to the lack of methodologies that accurately measures the amount of forest biomass. Remote sensing images can be used to make a more accurate and viable quantification of the existing biomass in regions with difficult access, such as the Amazon region, with emphasis on images in the microwave range, more specifically those from radar. Due to the large amount of remote sensing data available, it is necessary to develop techniques and tools that aims to organize and analyze them in an intelligent and automatic way, such as Machine Learning techniques. The present thesis has as general objective to develop and apply a methodology to estimate the amount of arboreal biomass in an area of the Amazon region, using SAR data and Machine Learning techniques. The thesis methodological steps are divided into three sequential technical articles that covers the proposed objectives. The first article hypothesizes the possibility of adjusting the interferometric height, InSAR feature, using the exposed soil areas identified in the image, that is, where the value is theoretically equal to 0 (zero). In addition to being innovative, the hypothesis predicted the adjustment of the region digital model in order to improve the biomass estimation modeling. However, as a result, the method proposed in the first article did not present a significant improvement in the estimation of forest biomass and was not adopted in the next stages of the work. The second article gives sequence for the first and presents the application of Machine Learning techniques over SAR features extracted from the available data. In an unprecedented way, it presents a methodology that evaluates and compares biomass estimation models based on qualitative and quantitative features. The second article concludes that the different Amazon Forest regions and their respective characteristics demands specific models and techniques, not fitting into a single pattern. In this case, it was not possible to identify a single Machine Learning technique that proved to be the most adequate for the purpose, despite the best results pointing to the use of artificial neural networks. The third and last article concludes the work of this thesis through the analysis and construction of thematic biomass products. In this last article, a computational system that aims to optimize the categorization process was developed, necessary for the visual representation of geoinformation. The results obtained in the third article shows that the proposed Categorization Optimization algorithm demonstrated the ability to find new subintervals of categories that increased the Kappa agreement index. As a result, thematic products were constructed and presented thematic accuracy superior to those obtained by the classical categorization methods. Besides that, from a computational point of view, the heuristic proposed in the algorithm enabled the identification of results in an efficient way, avoiding high processing costs. The hypothesis proposed in the thesis, that is, that the application of machine learning techniques over SAR data allows to obtain an estimate of biomass in the Amazon region with errors below 20%, attending to the standards established by international organizations, was not confirmed. The results obtained in the constructed models were classified only moderate. Among the factors that may have contributed to this result, there is the reduced amount of biomass samples, with little variation in values, which impaired the adjustment of the generated models and the restricted access to the X and P bands SAR data, not being possible to generate new coherent features