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 Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery

Forests ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 582 ◽  
Author(s):  
Lin Chen ◽  
Chunying Ren ◽  
Bai Zhang ◽  
Zongming Wang ◽  
Yanbiao Xi
Keyword(s):  
Machine Learning ◽  
Geographically Weighted Regression ◽  
Prediction Models ◽  
Forest Degradation ◽  
Weighted Regression ◽  
Changbai Mountains ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics

Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10−3, 0.07, 0.08 Mg·ha−1, and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha−1, and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha−1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques.

scholarly journals Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 601
Author(s):  
Prakriti Sharma ◽  
Larry Leigh ◽  
Jiyul Chang ◽  
Maitiniyazi Maimaitijiang ◽  
Melanie Caffé
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Vegetation Indices ◽  
Support Vector ◽  
Biomass Estimation ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
South Shore ◽  
Oat Biomass

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R2 = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R2 = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass.

scholarly journals Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam

2020 ◽  
Vol 12 (5) ◽  
pp. 777 ◽  
Author(s):  
Tien Dat Pham ◽  
Nga Nhu Le ◽  
Nam Thang Ha ◽  
Luong Viet Nguyen ◽  
Junshi Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Biosphere Reserve ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Extreme Gradient Boosting ◽  
Sentinel 2

This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coefficient of determination (R2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with R2 = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−1 (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in different geographical and climatic ecosystems.

Carbon stock assessment through above-ground biomass of trees at different forest composition in Mt. Malindawag, Lubilan, Naawan, Misamis Oriental, Philippines

2021 ◽  
Vol 11 (01) ◽  
pp. 100-113
Author(s):  
M. G. Origenes ◽  
R. L. Lapitan
Keyword(s):  
Carbon Stock ◽  
Diversity Index ◽  
Stock Assessment ◽  
Mixed Forest ◽  
Forest Composition ◽  
Above Ground Biomass ◽  
Plantation Forest ◽  
Species Diversity Index ◽  
Ground Biomass ◽  
Number Of Individuals

It is documented that the amounts of carbon stored and its level of degradation in different forests compositions and different types of forest is mostly unknown, Philippines is no exemption as little was done in some places of this country. This study was conducted to assess carbon stock through above ground biomass of trees at different forest composition in Mt. Malindawag. There were three (3) 20m x 20m sample plots (quadrats) (400m2 equivalent to 0.04 ha) established as replicate plots provided with 50m intervals. Forest composition such as the Agroforestry area, mixed forest area and the Plantation forest were assessed in terms of the number of individuals, number of species, diameter, and height to calculate the biomass, tree biomass density as well as the carbon stock. Based on the results of the study conducted in different forest compositions of Mt. Malindawag, plantation forest has the highest carbon stocking rate. However, these results were not significantly different from the other forest composition. This was associated with a higher accumulated diameter, which resulted in higher biomass and eventually carbon stock. Species found in this forest composition are productive and have lesser number of individuals; therefore, there is lesser competition for resources such as light. Such a mechanism might contribute to the higher biomass and carbon stock. However, the result may not be right to other areas due to uncontrollable factors, anthropogenic and environmental factors. Hence, it is recommended to have further studies on areas where trees have similar age, species diversity index as well as stand development and site productivity for a more accurate and quantifiable carbon stock.

Poplar breeding and testing strategies in the north-central U.S.: Demonstration of potential yield and consideration of future research needs

2001 ◽  
Vol 77 (2) ◽  
pp. 245-253 ◽  
Author(s):  
Don E. Riemenschneider ◽  
J. G. Isebrands ◽  
William E. Berguson ◽  
Donald I. Dickmann ◽  
Richard B. Hall ◽  
...  
Keyword(s):  
Principal Component ◽  
Future Research ◽  
Environment Interaction ◽  
Research Needs ◽  
Above Ground Biomass ◽  
University Of Minnesota ◽  
Potential Yield ◽  
North Central ◽  
Ground Biomass ◽  
The North

We present results from a Populus Regional Testing Program that has been conducted in Minnesota, Iowa, Wisconsin, and Michigan over the past six years. Our objectives have been to: 1) identify highly productive, disease resistant intra- and inter-specific clonal selections and 2) understand patterns of genotype × environment interactions within the region that would, logically, govern commercial deployment of new clones. Clones were developed by breeding and selection programs at the University of Illinois, Iowa State University, University of Minnesota, and the USDA Forest Service for experiments established in 1995. We report results of analyses of variance and principal component analyses of tree diameters and estimated above-ground biomass that demonstrate significant genotype main effects and significant genotype × environment interactions. Maximum mean annual above-ground biomass increments have surpassed 16 Mg ha−1 y−1, exceeding previously reported yields of poplars grown under similar conditions in the north-central U.S. We also discuss the breeding and selection of poplars in general with specific attention to regional research needs. Key words: Populus, biomass, multi-trait selection, genotype, genotype × environment interaction

scholarly journals Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

2020 ◽  
Vol 12 (19) ◽  
pp. 3121
Author(s):  
Roya Mourad ◽  
Hadi Jaafar ◽  
Martha Anderson ◽  
Feng Gao
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Semi Arid ◽  
Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

scholarly journals Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

2020 ◽  
Vol 12 (6) ◽  
pp. 958 ◽  
Author(s):  
Luofan Dong ◽  
Huaqiang Du ◽  
Ning Han ◽  
Xuejian Li ◽  
Di’en Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Data ◽  
Vegetation Indices ◽  
Spatial Data Analysis ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
State Of Art ◽  
Combined Data

Above-ground biomass (AGB) directly relates to the productivity of forests. Precisely, AGB mapping for regional forests based on very high resolution (VHR) imagery is widely needed for evaluation of productivity. However, the diversity of variables and algorithms and the difficulties inherent in high resolution optical imagery make it complex. In this paper, we explored the potentials of the state-of-art algorithm convolutional neural networks (CNNs), which are widely used for its high-level representation, but rarely applied for AGB estimation. Four experiments were carried out to compare the performance of CNNs and other state-of-art Machine Learning (ML) algorithms: (1) performance of CNN using bands, (2) performance of Random Forest (RF), support vector regression (SVR), artificial neural network (ANN) on bands, and vegetation indices (VIs). (3) Performance of RF, SVR, and ANN on gray-level co-occurrence matrices (GLCM), and exploratory spatial data analysis (ESDA), and (4) performance of RF, SVR, and ANN based on all combined data and ESDA+VIs. CNNs reached satisfactory results (with R2 = 0.943) even with limited input variables (i.e., only bands). In comparison, RF and SVR with elaborately designed data obtained slightly better accuracy than CNN. For examples, RF based on GLCM textures reached an R2 of 0.979 and RF based on all combined data reached a close R2 of 0.974. However, the results of ANN were much worse (with the best R2 of 0.885).

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