Mapping Tropical Forest Biomass by Combining ALOS-2, Landsat 8, and Field Plots Data

Nguyen, Luong Viet; Tateishi, Ryosuke; Kondoh, Akihiko; Sharma, Ram C.; Nguyen, Hoan Thanh; To, Tu Trong; Minh, Dinh Ho Tong

doi:10.3390/land5040031

Cited by 15 publications

(13 citation statements)

References 49 publications

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“…In addition, the shortwave infrared (SWIR) bands of Sentinel-2 and Landsat-7 were used to produce the normalized difference moisture index (NDMI), while the Sentinel-2 specific red-edge bands were used to produce inverted red-edge chlorophyll Index (IRECI). The C-band VV-polarization from Sentinel-1, as well as the L-band HH and HV polarizations from ALOS-2 PALSAR-2, were used to acquire backscatter intensities [35,56] and to calculate forest-specific backscatter (FB) values [56]. Vegetation indices were calculated for a 3 m × 3 m raster of pixels of Planetscope, for 10 m × 10 m raster pixels of Sentinel-2 10 m bands, as well as for 20 m × 20 m raster pixels of Sentinel-2 red-edge bands.…”

Section: Satellite Remote Sensing-derived Vegetation Indices and Backmentioning

confidence: 99%

Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

Mulatu

Decuyper

Brede

et al. 2019

Forests

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Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2-, and Landsat-7-derived vegetation indices (VIs), as well as ALOS-2 PALSAR-2- and Sentinel-1-derived backscatter intensities with a terrestrial laser scanner (TLS) and conventionally measured forest structure parameters acquired from 25 field plots in a tropical montane cloud forest in Kafa, Ethiopia. Results showed that canopy gap-related forest structure parameters had their highest correlation (|r| = 0.4 − 0.48) with optical sensor-derived VIs, while vegetation volume-related parameters were mainly correlated with red-edge- and short-wave infrared band-derived VIs (i.e., inverted red-edge chlorophyll index (IRECI), normalized difference moisture index), and synthetic aperture radar (SAR) backscatters (|r| = −0.57 − 0.49). Using stepwise multi-linear regression with the Akaike information criterion as evaluation parameter, we found that the fusion of different SRS-derived variables can improve the estimation of field-measured structural parameters. The combination of Sentinel-2 VIs and SAR backscatters was dominant in most of the predictive models, while IRECI was found to be the most common predictor for field-measured variables. The statistically significant regression models were able to estimate cumulative plant area volume density with an R2 of 0.58 and with the lowest relative root mean square error (RRMSE) value (0.23). Mean gap and number of gaps were also significantly estimated, but with higher RRMSE (R2 = 0.52, RRMSE = 1.4, R2 = 0.68, and RRMSE = 0.58, respectively). The models showed poor performance in predicting tree density and number of tree species (R2 = 0.28, RRMSE = 0.41, and R2 = 0.21, RRMSE = 0.39, respectively). This exploratory study demonstrated that SRS variables are sensitive to retrieve structural differences of tropical forests and have the potential to be used to upscale biodiversity relevant field-based forest structure estimates.

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Section: Satellite Remote Sensing-derived Vegetation Indices and Backmentioning

confidence: 99%

Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

Mulatu

Decuyper

Brede

et al. 2019

Forests

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“…If this is the case, then more precise interpretations of forest status can be visualized spatially, leading to better estimation of parameters such as AGB. This method could shed light on relationship analyses made with satellite data (e.g., SAR data), in which it is usually mentioned that collecting actual ground data for relationship analysis with forest parameters is better at larger sample sizes (e.g., 1 ha) to avoid bias; however, collecting such data in large areas at multiple locations is a very time-consuming and difficult task [47]. For example, Němec [11] showed that forest inventory field work, covering a 200 ha area with plot dimensions of 2000 m × 1000 m, required 14 people working for 25 days using calipers and laser rangefinders to measure DBH and tree height, respectively.…”

Section: Forest Parameter Extraction Using Uasmentioning

confidence: 99%

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

et al. 2017

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Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R 2 = 0.7786) and canopy area (R 2 = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures.

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“…Acredita-se que esse melhor resultado esteja relacionado ao tipo de vegetação estudada, ou seja, a saturação da banda C é determinada primariamente pela densidade da vegetação que no estudo foram os mangues, caracterizado por ser uma fitofisionomia que difere no seu grau de densidade com a floresta Amazônica, portanto possibilita melhor desempenho dos estimadores. Nguyen et al (2016) mapearam a biomassa de floresta decídua e tropical no Vietnã. Utilizaram de dados oriundos do satélite Landsat 8 e Alos-2 (banda L), bem como subprodutos oriundos desses satélites (textura, NDVI), além de dados de inventario.…”

Section: Huber Bisquareunclassified

“…O menor RMSE observado pelos autores foi de 35,18 Mg.ha -1 , para uma média de 136 Mg.ha -1 na floresta decídua e 309 Mg.ha -1 para floresta tropical, representando 25% e 11% de erro, respectivamente. Ao comparar a amplitude do erro relativo encontrado por Nguyen et al (2016), os resultados do presente estudo estão dentro da amplitude de valores (16%).…”

Section: Huber Bisquareunclassified

Regressões Robusta E Linear Para Estimativa De Biomassa via Imagem Sentinel Em Uma Floresta Tropical

Debastiani¹,

Moura²,

Rex³

et al. 2019

BIOFIX

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RESUMOA preocupação com as mudanças climáticas globais tem motivado diversos pesquisadores a encontrar métodos eficazes para a quantificação de biomassa florestal e carbono estocado em florestas tropicais, uma vez que, estas atuam de forma mitigatória e compensatória desses efeitos. O sensoriamento remoto tem sido utilizado de forma eficaz e com grande potencial para a estimativas em larga escala, com destaque para dados de Radar de Abertura Sintética (SAR) e imagens multiespectrais. Os estudos desenvolvidos com essa finalidade utilizaram diversas técnicas para associar a biomassa acima do solo (AGB) com os dados obtidos por sensoriamento remoto, entretanto, a aplicação da regressão robusta ainda não está sendo utilizada para tal finalidade. Sendo assim, o objetivo do presente estudo é avaliar o desempenho da regressão robusta comparando com a regressão linear, que é tradicionalmente utilizada, além de avaliar o potencial da utilização dos dados oriundos do satélite Sentinel 1 e 2 para a estimativa de AGB. Neste âmbito, foram utilizadas imagens multiespectrais (Sentinel 2), imagem SAR (Sentinel 1) e como variável resposta a AGB obtida a partir de dados Light Detection and Ranging (LiDAR). A AGB foi estimada por dois métodos de regressão: robusta e linear. Os modelos de regressão robusta e linear apresentaram desempenho semelhante, com R²aj. variando entre 0,33 a 0,34, erro padrão da estimativa de 48 Mg.ha -1 e raiz do erro médio quadrático de 16%. Conclui-se que não houve diferença significativa entre a regressão linear e a regressão robusta para esse conjunto de dados, indicando que a regressão robusta não se sobressaiu com a presença de outliers e que existe potencial na utilização de dados oriundos do satélite Sentinel. ABSTRACTConcern about global climate change has motivated several researchers to find effective methods for the quantification of forest biomass and carbon stored in tropical forests, since they act in a mitigating and compensatory way. Remote sensing has been used effectively and with great potential for large scale estimation, with emphasis on Synthetic Aperture Radar (SAR) data and multispectral images. The studies already developed for this purpose have used several techniques to associate above-ground biomass (AGB) with the data obtained by remote sensing, however, the application of robust regression is not yet being used for this purpose. Thus, the objective of the present study is to evaluate the robust regression performance in comparison with the linear regression, that is traditionally used, besides evaluating the potential of the data from Sentinel 1 and 2 satellite. In this context, multispectral images (Sentinel 2), SAR image (Sentinel 1) and as variable response to AGB obtained from data Light Detection And Ranging (LiDAR). AGB was estimated by two regression methods: robust and linear. The robust and linear regression models presented similar performance, with R²aj. ranging from 0.33 to 0.34, standard error of the estimate of 48 Mg.ha -1 and root mean square error of 16%. It is ...

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Mapping Tropical Forest Biomass by Combining ALOS-2, Landsat 8, and Field Plots Data

Cited by 15 publications

References 49 publications

Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

Regressões Robusta E Linear Para Estimativa De Biomassa via Imagem Sentinel Em Uma Floresta Tropical

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