Using Sentinel satellite image to estimate biomass of mangrove forest in Vinh Quang commune, Tien Lang district, Hai Phong city (original) (raw)
Related papers
Geosfera Indonesia
The mangroves' aboveground biomass significantly contributes to the global carbon cycle or economic and ecological values. This makes knowledge about the spatial extent of the mangroves indispensable for policymakers. The sequence of mangroves’ condition range also requires remote sensing data to update the geographical information and synthesize carbon stock in Bengkulu. Therefore, this study aims to create a spatial distrribution of mangroves and evaluate their carbon stock in Bengkulu City using Sentinel-2 imagery. The semi-empirical method uses Sentinel-2 imagery through NDVI to appraise and picture the mangroves' aboveground carbon stock. An allometric equation was used to compute the mangroves' aboveground carbon stock from field measurements. Non-linear regression was used to establish a connection between the NDVI calculated from the Sentinel-2 imagery and the mangroves' aboveground biomass measured in the field, which was subsequently used for aboveground ca...
Aboveground Biomass Estimation of Mangroves in Siargao Island, Philippines Using SENTINEL-1 Image
2019
Above ground biomass (AGB) of mangroves is considered as an important ecological and habitat management indicator of various environmental conditions and processes in mangrove ecosystems. In this study, Sentinel-1 images were used to model and estimate AGB of mangroves in Del Carmen, Siargao Islands, Philippines. There were three predictor variables derived from the Sentinel -1 image used for modeling the AGB: the backscatter value from VV polarization, backscatter value from VH polarization, and the combination of the backscatter values from VV and VH polarizations. The modeling was done through linear regression between the field-measured AGB and the predictor variables, and the coefficient of determination (R 2 ) and root mean square error (RMSE) were determined. Among the three predictor, the combination of the VV and VH polarizations produced a better model compared to the two predictor variables as it obtained the highest R 2 of 0.43 and the lowest RMSE of 12.65 Mg/ha. Based o...
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a <i>Rhizophoraceae</i>-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (Pl...
Mangrove Leaf Area Index Estimation Using Sentinel 2A Imagery in Teluk Ratai, Pesawaran Lampung
IOP Conference Series: Earth and Environmental Science
Mangroves are estuary dominant vegetation in tropic and subtropic area which have an important role to balance the coastal ecosystem. One of the main mangrove biophysics parameters is leaf area index, which is defined as an unitless quantity from the area of one side of the leaf on each unit of ground surface area. LAI measurement using satellite imagery is more efficient than direct measurement because it covers the isolated area in the mangrove forest. This paper discussion focus on mangrove leaf area index estimation model comparison to obtain the best estimation model based on accuracy test value. The imagery used in this research is Sentinel 2A with 10 meters resolution and generic vegetation index (NDVI) compared with nongeneric index vegetation (EVI). Normalized vegetation index is chosen because its sensitivity to chlorophyll tissue of the leaves beside, Enhanced Vegetation Index is chosen because its sensitivity to vegetation canopy structure. The LAI measurement result in Teluk Ratai, Pesawaran, Lampung showing values range from 0.37 until 1.39. The correlation analysis result showing an adequate strong relationship between NDVI and EVI with LAI field measurement value. The correlation value between NDVI and the field measurement value of LAI is 0.779 also the correlation value between EVI and the field measurement value of LAI is 0.762. Based on those value both of vegetation index have a strong relationship with the field measurement result of LAI. From the standard error estimation value, the LAI estimation model accuracy using NDVI is 79.8% and 78.78% using EVI. Visual comparison also done by compare vegetation density pattern in Sentinel 2A with estimation classes in NDVI and EVI model. The best model to estimate leaf area index mangrove is EVI model based on visual comparison, accuracy test, and saturation effect from NDVI.
Comparison of Different Vegetation Indices for Assessing Mangrove Density Using SENTINEL-2 Imagery
International journal of GEOMATE : geotechnique, construction materials and environment, 2018
Vegetation mapping provides important information for understanding ecological condition through calculation of vegetation density. It based on vegetation indices developed through algorithms of a mathematical model within the visible and near-infrared reflectance bands. The index is an estimate of either leaf density per species or vegetation types, respectively. This study aimed to evaluate those indices and find the best algorithm using Sentinel-2 satellite image. Twenty four algorithms of vegetation indices were analyzed for mangrove density mapping, i.
Modelling Above Ground Biomass of Mangrove Forest Using SENTINEL-1 Imagery
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23 cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75 cm to 7.5 cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a−c; and e) the identifie...
Estimation of Mangrove Carbon Stock with Hybrid Method Using Image SENTINEL-2
International Journal of GEOMATE, 2018
Field survey data combined with remote sensing data were an ideal and practical method for estimating carbon stocks. The objective of this research was to get an estimation model of mangrove carbon stock with good accuracy. Modeling used hybrid methods, by combining satellite image analysis and field data. The result of this research was to get the mangrove carbon estimation model. Model 1 merging between NNIP vegetation index equation using regression of power/geometry and six variables multiple regression (NDRE or WVVI vegetation index, sediment depth, soil density,% C soil depth 0-15 cm, 15-50 cm and >50 cm). RMSE test resulted 0.4778 t 100 m-2 and % RMSE 16.12%. Model 2 NNIP vegetation index and three variable regression (VIRRE vegetation index, sediment depth, soil density). RMSE test resulted 0.5639 t 100 m-2 and % RMSE 19.03%. Model 3 uses NNIP vegetation index and two variable regression (NDRE vegetation index and sediment depth). RMSE test resulted 0.7295 t 100 m-2 and RMSE % 24.63%. Model 4 incorporation of NNIP vegetation index and multiple regression of 3 variables (VIRRE vegetation index, average sediment depth value 100.63 cm, soil density value 1.02 g cm-3). RMSE test resulted 1.0043 t 100 m-2 and % RMSE 33.89%.
International Journal of Remote Sensing and Earth Sciences (IJReSES), 2018
Mangrove forest is one of the forest ecosystem types that have the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as on carbon sequestration. However, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods are very costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e. by using Landsat 8 and SPOT 5. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI of Landsat 8 and SPOT 5 have considerably high correlation coefficients with the standing biomass with a value of higher than 0.7071. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass for Landsat 8 is B=0.00023404 e (20 NDVI) with the R² value of 77.1% and B=0.36+25.5 NDVI² with the R² value of 49.9% for SPOT 5. In general, the concession area of Bina Silva Nusa (BSN) Group (PT Kandelia Alam and PT Bina Ovivipari Semesta) have the potential of biomass ranging from 45 to 100 ton per ha.
Biomass estimates by satellite data and ground measurements
2010
Mangrove forests in tropical and subtropical countries play important roles from the viewpoint of ecosystem services such as water quality maintenance, storm wave protection, fish habitat and ecotourism activities as well as carbon stocking. Several mapping techniques of mangrove area using satellite sensor with a couple of 10-meters ground resolution, i.e. Landsat and SPOT, were developed to protect, restore and monitor costal ecosystem in previous studies (Green et al., 1998; Gao 1999; Saito et al., 2003). The new generation of high resolution satellite data of finer ground resolution than 1-m 1-m such as IKONOS and QuickBird opened a new era for taking forest inventory and assessing forest biodiversity with remote sensing at landscape level. Forest inventory of mangrove forests is sometimes attended by the difficulty of the access because of the site environment and the complexity of the root system. Therefore, it is expected that high resolution satellite data are applied to the understanding of the present condition of mangrove forests as well as their dynamics (Rodriguez and Feller, 2004) and the classification of tree species using their properties of reflectance (Wang et al., 2004a; Dahdouh-Guebas et al., 2005). Wang et al., (2004b) indicated that both IKONOS and QuickBird data were suitable for classification of mangrove species from comparison of the results of texture analysis, likelihood classification and object-oriented classification. To estimate tree biomass, we need some allometric relationships. Normally, we estimate it from the stem diameter and tree height. But we can only observe directly crown diameter and tree number from high-resolution satellite data. Therefore, we should estimate stem diameter and tree height using allometric relationships between crown area and stem diameter or between stem diameter and tree height.In this study, we present methods to identify crown area of mangrove from high-resolution satellite data
International Journal of Remote Sensing, 2018
Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R2 = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha−1 (average = 87.67 Mg ha−1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.