ASSESSMENT OF VOLUME AND ABOVE-GROUND BIOMASS IN ARAUCARIA FOREST THROUGH SATELLITE IMAGES, COMPARING DIFFERENT METHODS IN THE SOUTH OF CHILE

Abstract. Initial results of biomass estimation in the La Fusta area from existing equations found in literature are presented. As expected, accuracy of general equations suffer from the equation coefficients being obtained from fitting training data from different sites. It is also clear from the results that there is a high variance between different methods, in particular when complex data mixture is applied. Biomass is difficult to assess for dense forests, as pixels are saturated. This must be considered when planning field-data collection, with more samples in dense forest to provide more robust estimators from the training phase. The SAR-only (PALSAR) method from eq. 4 provided the most bias in results, overestimating with respect to the other methods

Absolute Density Measures Estimation Functions with Very High Resolution Satellite Images

Assessment and monitoring of forest structure is frequently done with absolute density measures with field forest inventory data and expansion methods. The development of basal area and the number of trees estimation functions with data derived from very high spatial resolution satellite images enable their short-term and cost-effective evaluation, allowing also the estimation for the area not requiring extrapolation methods. The functions of basal area and the number of trees per hectare are based on crown cover obtained with very high spatial resolution satellite images for two evergreen oaks and umbrella pine. The three tree species are especially important in the agroforestry systems of the Mediterranean region. The linear functions fitted for pure stands of the three species and mixed stands of cork and holm oak and of cork oak and umbrella pine showed a better performance for basal area than for the number of trees per hectare. The inclusion of dummy variables for species composition improved the accuracy of the functions

Estimating biomass of woody plants that grow in the different As-contaminated techno-soils in the ore-bearing provinces of Eastern Germany

Establishing the role of woody species as an instrument for heavy metal bioaccumulation is a relevant issue today in the context of the development of the phytoremediation system. The article presents the results of studies on the influence of different Arsenic (As) concentrations in soil on the development of aboveground biomass in Betula pendula Roth. and Populus tremula L. stands under conditions of reclamation plantings. The studies were conducted in 30 locations of birch and poplar tree plantations within the ore-producing regions of Saxony (Eastern Germany) in soil with different levels of As contamination. The highest As content was noted in the technosoil of the Davidschacht site, where the metalloid content was 229.3 times greater compared with a value in a conditionally uncontaminated area (Großschirma). The values of leaf area index and aboveground biomass obtained in field measurements were presented. The aboveground biomass values in the investigated plantations ranged from 189.9 ±10.16 to 201.8 ± 19.09 t/ha, and leaf area index values ranged from 1.74 ± 0.29 to 2.05 ± 0.16 m2/m2. Sentinel-2A multispectral images were processed for the construction of a map of the aboveground biomass distribution within the region under study. The values of the spectral indices for leaf area index were obtained with subsequent construction of the regression dependence of the aboveground biomass in the plantings on this indicator. The RMSE value for the developed model of the dependence of aboveground biomass on the leaf area index was 17.84 t/ha, which could be considered as satisfactory and can serve as a basis for practical application of the model developed. The inverse trend in relation to locations with different levels of soil contamination with As was determined for the aboveground biomass indicator. Within the region under study, the highest value of aboveground biomass in the stands was found for the area with the lowest As level. The results showed that the correlation coefficient between the highest of the optimal spectral indices, the leaf area index, and the aboveground biomass in B. pendula and P. tremula plantings was statistically significant and approached the value of 0.7. The results presented can become a theoretical basis for monitoring the accumulation of aboveground biomass of tree stands in areas with different levels of soil contamination with As. In perspective, the presented model of biomass estimation based on spectral technologies can serve as an application basis for rapid assessment of the growth and development parameters of forest stands in As-contaminated areas

Regional carbon predictions in a temperate forest using satellite lidar

Large uncertainties in terrestrial carbon stocks and sequestration predictions result from insufficient regional data characterizing forest structure. This study uses satellite waveform lidar from ICESat to estimate regional forest structure in central New England, where each lidar waveform estimates fine-scale forest heterogeneity. ICESat is a global sampling satellite, but does not provide wall-to-wall coverage. Comprehensive, wall-to-wall ecosystem state characterization is achieved through spatial extrapolation using the random forest machine-learning algorithm. This forest description allows for effective initialization of individual-based terrestrial biosphere models making regional carbon flux predictions. Within 42/43.5 N and 73/71.5 W, aboveground carbon was estimated at 92.47 TgC or 45.66 MgC ha−1, and net carbon fluxes were estimated at 4.27 TgC yr−1 or 2.11 MgC ha−1 yr−1. This carbon sequestration potential was valued at 47% of fossil fuel emissions in eight central New England counties. In preparation for new lidar and hyperspectral satellites, linking satellite data and terrestrial biosphere models are crucial in improving estimates of carbon sequestration potential counteracting anthropogenic sources of carbon

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

Mineração de dados aplicada a métodos de seleção de variáveis para a modelagem de estoque de carbono acima do solo

The objective of this work was to apply the random forest (RF) algorithm to the modelling of the aboveground carbon (AGC) stock of a tropical forest by testing three feature selection procedures – recursive removal and the uniobjective and multiobjective genetic algorithms (GAs). The used database covered 1,007 plots sampled in the Rio Grande watershed, in the state of Minas Gerais state, Brazil, and 114 environmental variables (climatic, edaphic, geographic, terrain, and spectral). The best feature selection strategy – RF with multiobjective GA – reaches the minor root-square error of 17.75 Mg ha-1 with only four spectral variables – normalized difference moisture index, normalized burn ratio 2 correlation texture, treecover, and latent heat flux –, which represents a reduction of 96.5% in the size of the database. Feature selection strategies assist in obtaining a better RF performance, by improving the accuracy and reducing the volume of the data. Although the recursive removal and multiobjective GA showed a similar performance as feature selection strategies, the latter presents the smallest subset of variables, with the highest accuracy. The findings of this study highlight the importance of using near infrared, short wavelengths, and derived vegetation indices for the remote-sense-based estimation of AGC. The MODIS products show a significant relationship with the AGC stock and should be further explored by the scientific community for the modelling of this stock.O objetivo deste trabalho foi aplicar o algoritmo “random forest” (RF) à modelagem do estoque de carbono acima do solo (CAS) de uma floresta tropical, por meio da testagem de três procedimentos de seleção de variáveis: remoção recursiva e algoritmos genéticos (AGs) uniobjetivo e multiobjetivo. Os dados utilizados abrangeram 1.007 parcelas amostradas na bacia hidrográfica do Rio Grande, no estado de Minas Gerais, Brasil, e 114 variáveis ambientais (climáticas, edáficas, geográficas, de terreno e espectrais). A melhor estratégia de seleção de variáveis – a RF com AG multiobjetivo – chega ao menor erro quadrático de 17,75 Mg ha-1 com apenas quatro variáveis espectrais – índice de umidade por diferença normalizada, textura de correlação do índice de queimada por razão normalizada 2, cobertura arbórea e fluxo de calor latente –, o que representa redução de 96,5% no tamanho do banco de dados. As estratégias de seleção de variáveis ajudam a obter melhor desempenho da RF, ao melhorar a acurácia e reduzir o volume dos dados. Embora a remoção recursiva e o AG multiobjetivo mostrem desempenho semelhante como estratégias de seleção de variáveis, esta último apresenta menor subconjunto de variáveis, com maior precisão. As descobertas deste trabalho destacam a importância do uso de infravermelho próximo, comprimentos de onda curtos e índices de vegetação derivados para a estimativa de CAS baseada em sensoriamento remoto. Os produtos MODIS mostram relação significativa com o estoque de CAS e precisam ser melhor explorados pela comunidade científica para a modelagem deste estoque

Regression Models for Estimating Aboveground Biomass and Stand Volume Using Landsat-Based Indices in Post-Mining Area

This paper describes the use of remotely sensed data to measure vegetation variables such as basal area, biomass and stand volume. The objective of this research was developed regression models to estimate basal area (BA), aboveground biomass (AGB), and stand volume (SV) using Landsat-based vegetation indices. The examined vegetation indices were SAVI, MSAVI, EVI, NBR, NBR2 and NDMI.   Regression models were developed based on least-squared method using several forms of equation, i.e., linear, exponential, power, logarithm and polynomial.  Among those models, it was recognized that the best fit of model was obtained from the exponential model, log (y) = ax + b for estimating BA, AGB & SV.  The MSAVI had been identified as the most accurate independent variable to estimates basal area with R² of 0.70 and average verification values of 16.39% (4%-32.66%); while the EVI become the best independent variable for estimating aboveground biomass (AGB) with R2 of 0.72 and average of verification values of 18,10% (9%-28.01%); and the NDMI was recognized to be the best independent variable to estimate stand volume with R2 of 0.69 and average of verification values of 24.37% (-15%-38.11%)

Abordagem bibliométrica da produção científica sobre sensoriamento remoto para florestas

Orientador: Prof. Msc. Verônica Satomi KazamaMonografia (especialização) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Curso de Especialização MBA em Manejo Florestal de PrecisãoInclui referênciasResumo: Este estudo propõe-se a fornecer um panorama geral dos estudos de sensoriamento remoto para florestas por meio de análise bibliométrica. Posteriormente à compilação da literatura disponível na base selecionada, a Scopus, foi realizada uma análise bibliométrica. Nesta etapa, utilizou-se os pacotes bibliometrix e biblioshiny desenvolvidos para o software R, possibilitando a filtragem de documentos e representação dos dados em gráficos e Tabelas. Para este trabalho, optou-se pelos seguintes parâmetros: relevância e participação dos autores, número de citações, número de participações em artigos de cada país e instituições, índice-H, rede de cooperação entre países e coocorrência de palavras-chave. As instituições que mais participam nos artigos se concentram nos Estados Unidos das Américas, China, Alemanha e, consequentemente, a participação desses países também é maior. Houve um crescimento anual de 6,46% na quantidade de publicações, subindo de 571 artigos em 2009 para 1006 artigos no ano de 2018. Existem três grandes clusters de palavras-chave, indicando quais temas os pesquisadores estudam: o primeiro aborda como tema central a Ecologia; o segundo aborda estudos de carbono, biomassa e inventário florestal associados ao uso de sensores espectrais e à laser especialmente em florestas tropicais; o terceiro grupo estuda por meio das imagens orbitais, as mudanças de fragmentos florestais, uso e ocupação de terra. Conclui-se que os pesquisadores estão se aprofundando em trabalhos envolvendo sensoriamento remoto, para estudos de florestas, da mesma forma que os periódicos passaram a ganhar mais relevância. A maior quantidade de artigos produzidos se concentra em poucos países, o que acaba sugerindo maior facilidade de cooperação e investimento desses países. Por fim, recomenda-se uma investigação aprofundada sobre artigos publicados por instituições brasileiras e o impacto desses artigos.Abstract:This study aims to provide an overview of the general statistics of remote sensing studies through bibliometric analysis. After compiling the literature available in the selected database, Scopus, a bibliometric analysis was performed. In this step, we used the bibliometrix and biblioshiny package developed for the R software, enabling the filtering of documents and representation of the results in graphs and tables. For this work, we chose to work with the relevance and participation of the authors, number of citations, number of participations in articles from each country and institutions, H-index, cooperation network between countries, and co-occurrence of keywords. The institutions that participate most in the articles are concentrated in the United States of America, China, Germany, and consequently, the participation of these countries is also higher. There was an annual growth of 6.46% in the number of publications, rising from 571 articles in 2009 to 1006 articles in 2018. There are three significant clusters of keywords, indicating which themes the researcher's study. The cluster first addresses issues of monitoring the evapotranspiration, changes in land use and occupation, climates, fires and forest phenology; the second deals with carbon, biomass, and forest inventory studies associated with the use of laser and spectral sensors especially in tropical forests; The third group studies, through orbital images, changes in forest fragments, land use, and occupation. Our conclusion is that the researchers are deepening in works involving remote sensing, the journals have started to gain more relevance, the most significant number of articles produced is concentrated in a few countries, which ends up suggesting more natural cooperation and investment from these countries. Finally, an in-depth investigation into articles published by Brazilian institutions, and the impact of these articles is recommended