Tree diversity assessment and above ground forests biomass estimation using SAR remote sensing: A case study of higher altitude vegetation of North-East Himalayas, India

Kumar, Amit; Kishore, Bodi Surya Pratap Chandra; Deka, Jyotishman; Bharali, Sanjeeb; Singha, Lal Bihari; Tripathi, Om Prakash; Khan, M. L.

doi:10.1016/j.pce.2019.03.007

Cited by 31 publications

(11 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Synthetic aperture radar (SAR) has been used for forest monitoring for several years. SAR satellites have been proven to discriminate forest/vegetation [21,22], not only to map forest vegetation [23][24][25] but also to characterize the vertical forest structure [26] to estimate forest aboveground biomass [27][28][29] and many other variables.…”

Section: Introductionmentioning

confidence: 99%

GEDI Elevation Accuracy Assessment: A Case Study of Southwest Spain

Quiros

Polo

Fragoso-Campon

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Information about forest structures is becoming crucial to Earth's global carbon cycle, forest habitats and biodiversity. The Global Ecosystem Dynamics Investigation (GEDI) provides 25-m diameter footprints of the surface for 3D structure measurements. The main goal of this study is to compare 12 031 footprints of GEDI data with other airborne and spaceborne Digital Elevation Models (DEMs) for Southwest Spain. Ground elevation differences (ELM) are analyzed by comparing GEDI measurements with ALS LiDAR-and TanDEM-X-derived DEMs. The vertical structure (RH100) is compared to the ALS LiDAR measurement. Ten zones are analyzed, considering different degrees of coverage and slopes. We achieved an RMSE of 6.13 m for the ELM when comparing GEDI and LiDAR data and an RMSE of 7.14 m when comparing GEDI and TanDEM-X data. For some of the studied areas, these values were considerably smaller, with RMSE values even lower than 1 m. For the RH100 metric, an RMSE of 3.56 m was achieved when comparing GEDI and LiDAR data, but again with a minimum value of 2.09 m for one zone. The results show a clear relation to coverage and slope, especially for the latter. This work also evaluates the positional uncertainty of GEDI footprints, shifting them ±10 and ±5 m along and across the track of the satellite orbit and their intermediate angular positions. The outcomes reveal a strong tendency to obtain better results in the ELM when setting the footprint to 270° and displacing it within 10 m of its positional uncertainty in comparison with the LiDAR and TanDEM-X data.

show abstract

Section: Introductionmentioning

confidence: 99%

GEDI Elevation Accuracy Assessment: A Case Study of Southwest Spain

Quiros

Polo

Fragoso-Campon

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Based on the findings of similar studies, a more plausible explanation is that the Sentinel-1 satellite radar images have a short wavelength and consequently low canopy penetration [70]. For example, Kumar et al [71] found an insignificant correlation between VH polarization acquired by Sentinel-1A and above-ground biomass estimated in the field. Conversely, a significant correlation between VH polarization acquired by ALOS PALSAR and biomass was found by the authors due to the fact that the L-Band, compared to the C-band, of Sentinel 1-A is more sensitive to backscatter values and has a greater penetration of the canopy.…”

Section: Discussionmentioning

confidence: 98%

Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data

et al. 2021

View full text Add to dashboard Cite

Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1-h fuel load using standard fuel parameters or site-specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi-source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1-h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1-h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel-1), optical (Sentinel-2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson’s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1-h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1-h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management.

show abstract

“…Sentinel-1 (S1) has a C-band (5.405 GHz) and spatial and temporal resolution of 5 Â 20-m and 12 days, respectively (Sentinel-1_Team 2013). S1 operates with two polarization channels of VV and VH and has been used for AGB estimation (Kumar et al 2019;Navarro et al 2019;Berninger et al 2018;Ghosh and Behera 2018;Huang et al 2018;Laurin et al 2018;Periasamy 2018;Omar et al 2017). It is essential to mention that the long-wavelength SAR data are more sensitive to AGB (Ouchi 2013).…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran

Ronoud

Fatehi

Darvishsefat

et al. 2021

Canadian Journal of Remote Sensing

View full text Add to dashboard Cite

In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R 2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation. RÉSUMÉDans cette etude, la capacit e de Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1) et leur combinaison ont et e etudi ees pour estimer la biomasse a erienne (AGB). Un peuplement pur de Fagus Orientalis situ e dans la forêt hyrcanienne d'Iran a et e choisi comme zone d' etude. Le rendement d'une approche param etrique, c'est-a-dire, le mod ele de r egression lin eaire multiple (MLR) et les approches non param etriques, c'est-a-dire, k-Nearest Neighbor (k-NN), Random Forest (RF) et Support Vector Regression (SVR), ont et e evalu es pour les estimations de la biomasse. Nos r esultats indiquent que parmi les mesures S2, l'indice biophysique de la canop ee FAPAR et l'indice NDVI bas e sur la bande red-edge (NIR-b8a) ont les coefficients de corr elation les plus elev es (r) soit 0,420 et 0,417 respectivement. Les r esultats de l'estimation de l'AGB montrent qu'une combinaison des donn ees S2 et S1 utilisant l'algorithme k-NN donne la meilleure pr ecision (R2 de 0,57 et rRMSE de 14,68%). Le meilleur rRMSE en utilisant les ensembles de donn ees L8, S2 et S1 etait de 18,95%, 16,99% et 19,17% en utilisant respectivement les algorithmes k-NN, k-NN et MLR. La combinaison des ensembles de donn ees L8 et S1 a egalement am elior e le rRMSE de 0,96% et 1,18% par rapport aux donn ees L8 et S1 s epar ement. En conclusion, la combinaison des donn ees optiques (L8 ou S2) avec les donn ees SAR (S1) am eliore l'estimation de l'AGB de la forêt de feuillus hyrcanienne.

show abstract

Tree diversity assessment and above ground forests biomass estimation using SAR remote sensing: A case study of higher altitude vegetation of North-East Himalayas, India

Cited by 31 publications

References 56 publications

GEDI Elevation Accuracy Assessment: A Case Study of Southwest Spain

GEDI Elevation Accuracy Assessment: A Case Study of Southwest Spain

Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data

Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran

Contact Info

Product

Resources

About