scholarly journals Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Fardin Moradi ◽  
Ali Asghar Darvishsefat ◽  
Manizheh Rajab Pourrahmati ◽  
Azade Deljouei ◽  
Stelian Alexandru Borz
Keyword(s):  
Aboveground Biomass ◽  
Nearest Neighbor ◽  
Vegetation Indices ◽  
Field Measurements ◽  
K Nearest Neighbor ◽  
Remotely Sensed Data ◽  
Northern Iran ◽  
Hyrcanian Forests ◽  
Sentinel 2

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

scholarly journals Predicting Tree Species Diversity Using Geodiversity and Sentinel-2 Multi-Seasonal Spectral Information

2020 ◽  
Vol 12 (21) ◽  
pp. 9250
Author(s):  
Irene Chrysafis ◽  
Georgios Korakis ◽  
Apostolos P. Kyriazopoulos ◽  
Giorgos Mallinis
Keyword(s):  
Tree Species ◽  
Field Measurements ◽  
Biodiversity Loss ◽  
Diversity Indices ◽  
Early Spring ◽  
Tree Diversity ◽  
Remotely Sensed Data ◽  
Random Forest Regression ◽  
Sentinel 2

Measuring and monitoring tree diversity is a prerequisite for altering biodiversity loss and the sustainable management of forest ecosystems. High temporal satellite remote sensing, recording difference in species phenology, can facilitate the extraction of timely, standardized and reliable information on tree diversity, complementing or replacing traditional field measurements. In this study, we used multispectral and multi-seasonal remotely sensed data from the Sentinel-2 satellite sensor along with geodiversity data for estimating local tree diversity in a Mediterranean forest area. One hundred plots were selected for in situ inventory of tree species and measurement of tree diversity using the Simpson’s (D1) and Shannon (H′) diversity indices. Four Sentinel-2 scenes and geodiversity variables, including elevation, aspect, moisture, and basement rock type, were exploited through a random forest regression algorithm for predicting the two diversity indices. The multi-seasonal models presented the highest accuracy for both indices with an R2 up to 0.37. In regard to the single season, spectral-only models, mid-summer and mid-autumn model also demonstrated satisfactory accuracy (max R2 = 0.28). On the other hand, the accuracy of the spectral-only early-spring and early-autumn models was significant lower (max R2 = 0.16), although it was improved with the use of geodiversity information (max R2 = 0.25).

Effect of Soil Depth on the Quantification of Soil Moisture Content Value Estimated from NOAA Satellite Images

2014 ◽  
Vol 567 ◽  
pp. 705-710
Author(s):  
Abdalhaleem A. Hassaballa ◽  
Abdul Nasir Matori ◽  
Helmi Z.M. Shafri
Keyword(s):  
Soil Moisture ◽  
Critical Parameter ◽  
Satellite Images ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Spatial Estimation ◽  
Optical Images

Soil moisture (MC) is considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes especially over humid areas. However, MC is very critical parameter to measure because of its variability in both space and time. The fluctuation of MC along the soil depth in turn, makes it so difficult to assess from optical satellite techniques. The study aims to produce a rectified satellite’s surface temperature (Ts) in order to enhance the spatial estimation of MC. The study also aims to produce MC estimates from three variable depths of the soil using optical images from NOAA 17 in order to examine the potential of satellite techniques in assessing the MC along the soil depths. The universal triangle (UT) algorithm was used for MC assessment based on Ts, vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths. The study area was divided into three classes according to the nature of surface cover. The resultant MC extracted from the UT method with rectified Ts, produced accuracies of MC ranging from 0.65 to 0.89 when validated with in-situ measured MC at depths 5cm and 10 cm respectively.

scholarly journals Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

2020 ◽  
Vol 12 (2) ◽  
pp. 266 ◽  
Author(s):  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kayvan Ghaderi ◽  
Ebrahim Omidvar ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Statistical Error ◽  
Remote Sensing Data ◽  
Susceptibility Mapping ◽  
Training Dataset ◽  
Mapping Technique ◽  
K Nearest Neighbor ◽  
Northern Iran ◽  
Ensemble Models ◽  
Flood Prone Areas

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

scholarly journals Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Askar ◽  
Narissara Nuthammachot ◽  
Worradorn Phairuang ◽  
Pramaditya Wicaksono ◽  
Tri Sayektiningsih
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Natural Forests ◽  
Private Forests ◽  
Indonesian Government ◽  
Nations Programme ◽  
Reduce Carbon Dioxide ◽  
Sentinel 2

Private forests have a crucial role in maintaining the functioning of the Indonesian forest ecosystem especially because of the continuous degradation of natural forests. Private forests are a part of social forestry which becomes a tool for the Indonesian government to reduce carbon dioxide (CO2) emission by 26% by 2030. The United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation has encouraged the Indonesian government to establish a forest monitoring system by estimating forest carbon stock using a combination of forest inventory and remote sensing. This study is aimed at assessing the potential of vegetation indices derived from Sentinel-2 for estimating aboveground biomass (AGB) of private forests. We used 45 sample plots and 7 vegetation indices to evaluate the ability of Sentinel-2 in estimating AGB on private forests. Normalised difference index (NDI) 45 exhibited a strong correlation with AGB compared to other indices (r = 0.89; R2 = 0.79). Stepwise linear regression fitted for establishing the model between field AGB and vegetation indices (R2 = 0.81). We also found that AGB in the study area based on spatial analysis was 72.54 Mg/ha. A root mean square error (RMSE) value from predicted and observed AGB was 27 Mg/ha. The AGB value in the study area is higher than the AGB value from some of forest types, and it indicates that private forests are good for biomass storage. Overall, vegetation indices from Sentinel-2 multispectral imagery can provide a good result in terms of reporting the AGB on private forests.

scholarly journals Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2534
Author(s):  
Aliny A. Dos Reis ◽  
João P. S. Werner ◽  
Bruna C. Silva ◽  
Gleyce K. D. A. Figueiredo ◽  
João F. G. Antunes ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Canopy Height ◽  
Gradient Boosting ◽  
Remotely Sensed Data ◽  
Spatiotemporal Resolution ◽  
Spectral Bands ◽  
Extreme Gradient Boosting ◽  
Intensively Managed ◽  
New Generation

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. 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. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

scholarly journals Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

2019 ◽  
Vol 11 (7) ◽  
pp. 799 ◽  
Author(s):  
Rachel Lugassi ◽  
Eli Zaady ◽  
Naftaly Goldshleger ◽  
Maxim Shoshany ◽  
Alexandra Chudnovsky
Keyword(s):  
Crude Protein ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Neutral Detergent Fiber ◽  
Ecological Quality ◽  
Spatial And Temporal Patterns ◽  
Validation Data ◽  
Calibration And Validation ◽  
Sentinel 2

Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.

scholarly journals BEECH TREE DENSITY ESTIMATION USING SENTINEL-2 DATA (CASE STUDY: KHYROUD FOREST)

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 891-895
Author(s):  
G. Ronoud ◽  
A. A. Darvish Sefat ◽  
P. Fatehi
Keyword(s):  
Density Estimation ◽  
Vegetation Indices ◽  
Model Performance ◽  
Field Measurements ◽  
Tree Density ◽  
Spectral Bands ◽  
Tasseled Cap Transformation ◽  
Beech Tree ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Abstract. Obtaining information about forest attributes is essential for planning, monitoring, and management of forests. Due to the time and cost consuming of Tree Density (TD) using field measurements especially in the vast and remote areas, remote sensing techniques have gained more attention in scientific community. Khyroud forest, a part of Hyrcanian forest of Iran, with a high species biodiversity and growing volume stock plays an important role in carbon storage. The aim of this study was to assess the capability of Sentinel-2 data for estimating the tree density in the Khyroud forest. 65 square sample plots with an area of 2025 m2 were measured. In each sample plot, trees with diameter at the breast height (DBH) higher than 7.5-cm were recorded. The quality of Sentinel-2 data in terms of geometric correction and cloud effect were investigated. Different processing approaches such as vegetation indices and Tasseled Cap transformation on spectral bands in combination with an empirical approach were implemented. Also, some of biophysical variables were computed. To assess the model performance, the data were randomly divided into parts, 70% of sample plots were used for modelling and 30% for validation. The results showed that the SVR algorithm (linear kernel) with a relative RMSE of 23.09% and a R2 of 0.526 gained the highest performance for tree density estimation.

scholarly journals Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yaron Ogen ◽  
Michael Denk ◽  
Cornelia Glaesser ◽  
Holger Eichstaedt ◽  
Rene Kahnt ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Field Measurements ◽  
Crushed Rock ◽  
Qualitative And Quantitative Analysis ◽  
Ambient Conditions ◽  
K Nearest Neighbor ◽  
Spectral Variability ◽  
Rock Types

Reflectance spectroscopy is a nondestructive, rapid, and easy-to-use technique which can be used to assess the composition of rocks qualitatively or quantitatively. Although it is a powerful tool, it has its limitations especially when it comes to measurements of rocks with a phaneritic texture. The external variability is reflected only in spectroscopy and not in the chemical-mineralogical measurements that are performed on crushed rock in certified laboratories. Hence, the spectral variability of the surface of an uncrushed rock will, in most cases, be higher than the internal chemical-mineralogical variability, which may impair statistical models built on field measurements. For this reason, studying ore-bearing rocks and evaluating their spectral variability in different scales is an important procedure to better understand the factors that may influence the qualitative and quantitative analysis of the rocks. The objectives are to quantify the spectral variability of three types of altered granodiorite using well-established statistical methods with an upscaling approach. With this approach, the samples were measured in the laboratory under supervised ambient conditions and in the field under semisupervised conditions. This study further aims to conclude which statistical method provides the best practical and accurate classification for use in future studies. Our results showed that all statistical methods enable the separation of the rock types, although two types of rocks have exhibited almost identical spectra. Furthermore, the statistical methods that supplied the most significant results for classification purposes were principal component analysis combined with k-nearest neighbor with a classification accuracy for laboratory and field measurements of 68.1% and 100%, respectively.

scholarly journals Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal

2019 ◽  
Vol 11 (1) ◽  
pp. 77 ◽  
Author(s):  
José Antonio Navarro ◽  
Nur Algeet ◽  
Alfredo Fernández-Landa ◽  
Jessica Esteban ◽  
Pablo Rodríguez-Noriega ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Auxiliary Information ◽  
Field Measurements ◽  
Cost Effective ◽  
Point Clouds ◽  
Support Vector ◽  
Monitoring Methods ◽  
Individual Tree ◽  
Entire Study ◽  
Sentinel 2

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurements of AGB may be a challenge because of their remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund Oceanium project that monitors 10,000 ha of mangrove plantations. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric support vector regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2, and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m, respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in young mangrove plantations.

Sign in / Sign up

Export Citation Format

Share Document