scholarly journals Large Area Mapping of Boreal Growing Stock Volume on an Annual and Multi-Temporal Level Using PALSAR L-Band Backscatter Mosaics

Forests ◽  
2014 ◽  
Vol 5 (8) ◽  
pp. 1999-2015 ◽  
Author(s):  
Sebastian Wilhelm ◽  
Christian Hüttich ◽  
Mikhail Korets ◽  
Christiane Schmullius
Keyword(s):  
Large Area ◽  
Growing Stock ◽  
Multi Temporal ◽  
L Band ◽  
Stock Volume

scholarly journals Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 944
Author(s):  
Mihai A. Tanase ◽  
Ignacio Borlaf-Mena ◽  
Maurizio Santoro ◽  
Cristina Aponte ◽  
Gheorghe Marin ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Temporal Frequency ◽  
National Forest Inventory ◽  
Easy Access ◽  
Estimation Errors ◽  
Growing Stock ◽  
L Band ◽  
Stock Volume ◽  
Global Datasets ◽  
Relative Root

While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.

scholarly journals Polarimetric Parameters for Growing Stock Volume Estimation Using ALOS PALSAR L-Band Data over Siberian Forests

2013 ◽  
Vol 5 (11) ◽  
pp. 5725-5756 ◽  
Author(s):  
Tanvir Chowdhury ◽  
Christian Thiel ◽  
Christiane Schmullius ◽  
Martyna Stelmaszczuk-Górska
Keyword(s):  
Volume Estimation ◽  
Alos Palsar ◽  
Growing Stock ◽  
L Band ◽  
Stock Volume ◽  
Siberian Forests

Growing stock volume estimation from L-band ALOS PALSAR polarimetric coherence in Siberian forest

2014 ◽  
Vol 155 ◽  
pp. 129-144 ◽  
Author(s):  
Tanvir Ahmed Chowdhury ◽  
Christian Thiel ◽  
Christiane Schmullius
Keyword(s):  
Volume Estimation ◽  
Alos Palsar ◽  
Growing Stock ◽  
L Band ◽  
Stock Volume

scholarly journals Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 276 ◽  
Author(s):  
Haibo Zhang ◽  
Jianjun Zhu ◽  
Changcheng Wang ◽  
Hui Lin ◽  
Jiangping Long ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Correlation Coefficient ◽  
Terrain Correction ◽  
Central China ◽  
Synthetic Aperture ◽  
Single Variable ◽  
Variable Model ◽  
Growing Stock ◽  
L Band ◽  
Stock Volume

Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R2 value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m3/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m3/ha, and the mean value was 135.759 m3/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.

Growing stock volume from multi-temporal landsat imagery through google earth engine

2019 ◽  
Vol 83 ◽  
pp. 101913 ◽  
Author(s):  
Sergio Sánchez-Ruiz ◽  
Álvaro Moreno-Martínez ◽  
Emma Izquierdo-Verdiguier ◽  
Marta Chiesi ◽  
Fabio Maselli ◽  
...  
Keyword(s):  
Landsat Imagery ◽  
Google Earth ◽  
Growing Stock ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Stock Volume

scholarly journals Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data

2019 ◽  
Vol 11 (4) ◽  
pp. 384 ◽  
Author(s):  
Erkki Tomppo ◽  
Oleg Antropov ◽  
Jaan Praks
Keyword(s):  
Support Vector ◽  
Detection Accuracy ◽  
Climate Trends ◽  
Growing Stock ◽  
Forest Ecosystem Services ◽  
Multi Temporal ◽  
Snow Damage ◽  
Damage Mapping ◽  
Extent Of Damage ◽  
Stock Volume

Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.

Deep Neural Networks FOR forest Growing stock volume retrieval: a compartive analysis for L-band SAR data

2020 ◽  
Author(s):  
Mihai A. Tanase ◽  
Miguel A. Belenguer-Plomer ◽  
Gheorghe Marin ◽  
Ovidiu Badea
Keyword(s):  
Parametric Models ◽  
Limiting Factor ◽  
Learning Approaches ◽  
Backscatter Coefficient ◽  
Estimation Errors ◽  
Empirical Modelling ◽  
Growing Stock ◽  
L Band ◽  
Sar Data ◽  
Stock Volume

<p>The aim of this study was to evaluate the utility of deep learning (DL) approaches to estimate forest growing stock volume from L-band SAR data over areas characterized by diverse species composition. For comparison, parametric models were also used. When using one independent variable (i.e. HV backscatter coefficient) the lowest estimation errors were observed for the empirical model followed by Random Forests (RF). Increasing the number of independent variables resulted in marginally more accurate results for the machine learning approaches. However, for the studied area, DL approaches did not improve GSV retrieval when compared to RF or empirical modelling suggesting that L-band data sensitivity to GSV values is the main limiting factor.</p>

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

Sign in / Sign up

Export Citation Format

Share Document