scholarly journals PEMETAAN BIOMASSA TEGAKAN HUTAN HUJAN TROPIS DI BUKIT MANDIANGIN MENGGUNAKAN CITRA SENTINEL-2 MSI

2021 ◽  
Vol 9 (3) ◽  
pp. 299
Author(s):  
Mufidah Asy’ari ◽  
Syam’ani Syam’ani ◽  
Trisnu Satriadi
Keyword(s):  
Environmental Sustainability ◽  
Tropical Rainforest ◽  
Vegetation Indices ◽  
Percentage Error ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Average Biomass ◽  
Simple Ratio ◽  
Estimation Models ◽  
Sentinel 2

The preservation of standing biomass is one of the most vital elements for environmental sustainability and the sustainability of the forest itself. One of the actions that can be taken in an effort to maintain the sustainability of forest stand biomass is to map the distribution of biomass, and monitor changes or dynamics of stand biomass from time to time in a sustainable manner. This study aims to build a model based on remote sensing imagery to estimate the total biomass of tropical rainforest stands in Mandiangin Hill, South Kalimantan. The models developed in this study are based on vegetation indices extracted from Sentinel-2 MSI Imagery. A total of ten vegetation indices were tested in this study. For the construction process and validation of stand biomass estimation models, biomass information was measured directly in the field using a number of measuring plots. Stand biomass estimation models were made by correlating stand biomass information from the field with vegetation indices from Sentinel-2 MSI Imagery. The results showed that the most accurate model for estimating the biomass of tropical rainforest stands was 9.5806.exp (0.1454.PSSRa). Where PSSRa is Pigment Specific Simple Ratio. This model has a correlation coefficient (R2) of 0.876, a Mean Absolute Percentage Error (MAPE) of 16.8%, and a Root Mean Square Error (RMSE) of 32.6. The estimation results show that the total biomass of the Bukit Mandiangin tropical rainforest stands is between 11.7 to 998.5 Mg/ha, with an average biomass of 135.8 Mg/ha. Furthermore, the estimation of stand biomass in this study is limited to woody vegetation with a DBH of 10 cm and above. The PSSRa model with various improvements can be used to accurately estimate stand biomass

scholarly journals MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 85 ◽  
Author(s):  
Gathot Winarso ◽  
Yenni Vetrita ◽  
Anang D. Purwanto ◽  
Nanin Anggraini ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Vegetation Indices ◽  
Emission Trading ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Alos Palsar ◽  
Sar Data ◽  
Estimation Models

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

scholarly journals Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1942 ◽  
Author(s):  
Sosdito Mananze ◽  
Isabel Pôças ◽  
Mario Cunha
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Cost Functions ◽  
Support Vector ◽  
Area Index ◽  
Maize Leaf ◽  
Multispectral Data ◽  
Simple Ratio ◽  
Sentinel 2

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

scholarly journals Spectral variables, growth analysis and yield of sugarcane

2005 ◽  
Vol 62 (3) ◽  
pp. 199-207 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Lower Amount ◽  
Total Biomass ◽  
Area Index ◽  
Spectral Vegetation Indices ◽  
Green Vegetation ◽  
Highly Correlated ◽  
Simple Ratio

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

scholarly journals Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 711
Author(s):  
Amparo Cisneros ◽  
Peterson Fiorio ◽  
Patricia Menezes ◽  
Nieves Pasqualotto ◽  
Shari Van Wittenberghe ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Agricultural Science ◽  
Grass Species ◽  
Biomass Estimation ◽  
Panicum Maximum ◽  
Biophysical Parameters ◽  
Area Index ◽  
Tropical Grasslands ◽  
Urochloa Brizantha ◽  
Sentinel 2

Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)’s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species’ cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall’Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)–near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg−1. The estimation of grassland productivity based on red-edge and NIR bands showed a R2 = 0.54 and a RMSE = 1800 kg ha−1. For the LAI, the best index was the NAOC (R2 = 0.57 and RMSE = 1.4 m2 m−2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.

scholarly journals ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2

2018 ◽  
Vol IV-3 ◽  
pp. 29-36 ◽  
Author(s):  
Alvin Balidoy Baloloy ◽  
Ariel Conferido Blanco ◽  
Christian Gumbao Candido ◽  
Reginal Jay Labadisos Argamosa ◽  
John Bart Lovern Caboboy Dumalag ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mangrove Forest ◽  
Field Data ◽  
Vegetation Index ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Satellite Systems ◽  
Red Edge ◽  
Simple Ratio ◽  
Sentinel 2

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&amp;thinsp;m, 20&amp;thinsp;m and 60&amp;thinsp;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&amp;thinsp;ha) and 5 validation plots (0.2&amp;thinsp;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 (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r<sup>2</sup>) values were obtained using multispectral band predictors for Sentinel-2 (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.89) and Planetscope (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.80); and vegetation indices for RapidEye (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r<sup>2</sup> ranging from 0.62 to 0.92. Based on the r<sup>2</sup> and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.92) and RapidEye data (r<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.91).

scholarly journals EXPLORING THE POTENTIAL OF HIGH-RESOLUTION PLANETSCOPE IMAGERY FOR PASTURE BIOMASS ESTIMATION IN AN INTEGRATED CROP–LIVESTOCK SYSTEM

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 419-424
Author(s):  
A. A. Dos Reis ◽  
B. C. Silva ◽  
J. P. S. Werner ◽  
Y. F. Silva ◽  
J. V. Rocha ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Predictor Variables ◽  
Biomass Estimation ◽  
Remotely Sensed Data ◽  
Preliminary Study ◽  
Temporal And Spatial ◽  
Simple Ratio ◽  
New Generation ◽  
Livestock System

Abstract. Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May–August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g m−2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g m−2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.

scholarly journals Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions

2019 ◽  
Vol 11 (22) ◽  
pp. 2647
Author(s):  
Dongyan Zhang ◽  
Shengmei Fang ◽  
Bao She ◽  
Huihui Zhang ◽  
Ning Jin ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Land Surface ◽  
Vegetation Indices ◽  
Percentage Error ◽  
Growth Stages ◽  
Distance Method ◽  
Water Index ◽  
Spectral Bands ◽  
Heading Stage ◽  
Sentinel 2

Monitoring and mapping the spatial distribution of winter wheat accurately is important for crop management, damage assessment and yield prediction. In this study, northern and central Anhui province were selected as study areas, and Sentinel-2 imagery was employed to map winter wheat distribution and the results were verified with Planet imagery in the 2017–2018 growing season. The Sentinel-2 imagery at the heading stage was identified as the optimum period for winter wheat area extraction after analyzing the images from different growth stages using the Jeffries–Matusita distance method. Therefore, ten spectral bands, seven vegetation indices (VI), water index and building index generated from the image at the heading stage were used to classify winter wheat areas by a random forest (RF) algorithm. The result showed that the accuracy was from 93% to 97%, with a Kappa above 0.82 and a percentage error lower than 5% in northern Anhui, and an accuracy of about 80% with Kappa ranging from 0.70 to 0.78 and a percentage error of about 20% in central Anhui. Northern Anhui has a large planting scale of winter wheat and flat terrain while central Anhui grows relatively small winter wheat areas and a high degree of surface fragmentation, which makes the extraction effect in central Anhui inferior to that in northern Anhui. Further, an optimum subset data was obtained from VIs, water index, building index and spectral bands using an RF algorithm. The result of using the optimum subset data showed a high accuracy of classification with a great advantage in data volume and processing time. This study provides a perspective for winter wheat mapping under various climatic and complicated land surface conditions and is of great significance for crop monitoring and agricultural decision-making.

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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