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.

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>

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 Coniferous Plantations Growing Stock Volume Estimation Using Advanced Remote Sensing Algorithms and Various Fused Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3468
Author(s):  
Xinyu Li ◽  
Jiangping Long ◽  
Meng Zhang ◽  
Zhaohua Liu ◽  
Hui Lin
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Image Fusion ◽  
Estimation Accuracy ◽  
Mean Square ◽  
Estimation Performance ◽  
Growing Stock ◽  
Stock Volume ◽  
Relative Root ◽  
Sentinel 2

Spatial distribution prediction of growing stock volume (GSV) for supporting the sustainable management of forest ecosystems, is one of the most widespread applications of remote sensing. For this purpose, remote sensing data were used as predictor variables in combination with ground data obtained from field sample plots. However, with the increase in forest GSV values, the spectral reflectance of remote sensing imagery is often saturated or less sensitive to the GSV changes, making accurate estimation difficult. To improve this, we examined the GSV estimation performance and data saturation of four optical remote sensing image datasets (Landsat 8, Sentinel-2, ZiYuan-3, and GaoFen-2) in the subtropical region of Central South China. First, various feature variables were extracted and three optimization methods were used to select optimal feature variable combinations. Subsequently, k-nearest-neighbor (kNN), random forest regression, and categorical boosting algorithms were employed to build the GSV estimation models, and evaluate the GSV estimation accuracy and saturation. Second, Gram Schmidt (GS) and NNDiffuse pan sharpening (NND) methods were employed to fuse the optimal multispectral images and explore various image fusion schemes suitable for GSV estimation. We proposed an adaptive stacking (AdaStacking) model ensemble algorithm to further improve GSV estimation performance. The results indicated that Sentinel-2 had the highest GSV estimation accuracy exhibiting a minimum relative root mean square error of 20.06% and saturation of 434 m3/ha, followed by GaoFen-2 with a minimum relative root mean square error of 22.16% and a saturation of 409 m3/ha. Among the four fusion images, the NND-B2 image—obtained by fusing the GaoFen-2 green band and Sentinel-2 multispectral image with the NND method—had the best estimation accuracy. The estimated optimal RMSEs of NND-B2 were 24.4% and 16.5% lower than those of GaoFen-2 and Sentinel-2, respectively. Therefore, the fused image data based on GF-2 and Sentinel-2 can effectively couple the advantages of the two images and significantly improve the GSV estimation performance. Moreover, the proposed adaptive stacking model is more effective in GSV estimation than a single model. The GSV estimation saturation value of the AdaStacking model based on NND-B2 was 5.4% higher than that of the KNN-Maha model. The GSV distribution map estimated by AdaStacking model used the NND-B2 dataset corresponded accurately with the field observations. This study provides some insights into the optical image fusion scheme, feature selection, and adaptive modeling algorithm in GSV estimation for coniferous forest.

scholarly journals Fluctuations in gross volume increment estimated by the Lithuanian National Forest Inventory compared with annual variations in single tree increment

2019 ◽  
Vol 25 (2) ◽  
pp. 273-280
Author(s):  
Gintaras Kulbokas ◽  
Vaiva Jurevičienė ◽  
Andrius Kuliešis ◽  
Algirdas Augustaitis ◽  
Edmundas Petrauskas ◽  
...  
Keyword(s):  
Land Use ◽  
Volume Change ◽  
Forest Inventory ◽  
Basal Area ◽  
National Forest Inventory ◽  
Reference Level ◽  
National Forest ◽  
Growing Stock ◽  
Volume Increment ◽  
Stock Volume

There are significant inter-annual fluctuations of growing stock volume changes of living trees estimated by the Lithuanian National Forest Inventory (NFI). In the current study, we compared two sources of information on forest productivity: conventional NFI data and dendrochronological data based on tree cores collected in parallel with the measurements of the fourth Lithuanian NFI cycle during 2013–2017 on the same permanent plots (total number of cores was 4967). The main finding is that the dendrochronological basal area increment data confirmed the depression of gross stand volume increment around 2006–2007 (based on Lithuanian NFI measurements in 2008–2009), followed by a steep increase during 2008–2011 (NFI from 2010–2013). The findings explain the differences between projected growing stock volume change, which have been used for forest reference level estimation according to land use, land-use change and forestry sector regulation, and the one recently provided in National Greenhouse Gas Inventory Reports. Key words: Growing stock volume change, basal area increment, forest reference level, greenhouse gas reporting

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.

Multipurpose National Forest Inventory in Mongolia, 2014-2017 -A tool to support sustainable forest management

2019 ◽  
Vol 12 (3) ◽  
pp. 167-183 ◽  
Author(s):  
Dan Altrell
Keyword(s):  
Forest Management ◽  
Boreal Forest ◽  
Forest Inventory ◽  
Dead Wood ◽  
Sustainable Forest Management ◽  
National Forest Inventory ◽  
National Forest ◽  
Green Wood ◽  
Growing Stock ◽  
Stock Volume

Mongolia’s first Multipurpose National Forest Inventory, 2014-2017, was implemented by the Forest Research and Development Centre, in collaboration with international expertise and the country’s main forestry institutions, universities and research organisations.The long-term objective of the multipurpose NFI is to promote sustainable management of forestry resources in Mongolia, to enhance their social, economic and environmental functions.The NFI findings show that there are 11.3 million hectares of Boreal Forest in Mongolia. 9.5 million hectares are Stocked Boreal Forest Area, of which 69 percent is located outside of protected areas, 4 percent are designated for green-wood utilisation through forest enterprise concessions, and another 16 percent designated for fallen dead-wood collection through forest user group concessions. The non-protected stocked forests (i.e. production forest) have an average growing stock volume of 115 m3 per hectare, compared with an optimal growing stock volume of 237 m3 per hectare, and there is an additional 46.5 m3 of dead wood per hectare. The growing stock age distribution shows that 24 m3 per hectare are over 200 years (i.e. economically over-aged). The main tree species in stocked forest are Larix sibirica (81%), Pinus sibirica (7%), Betula platyphylla (6%) and Pinus sylvestris (5%), of which all, except for P. sibirica, are classified as legally harvestable tree species. Wild fire is the current main environmental factor decreasing the forest tree biomass.The NFI helped identifying priority areas for the forestry sector, and to guide the implementation of sustainable forest management at the local level. The main forest management challenges of Mongolia’s boreal forest will be to address that they are a) under-stocked (less than 50% of production potential), b) over-aged (31% of growing stock volume in stocked production forest is above optimal production age), and c) under-utilised (4% of forest area designated to green-wood utilisation). 

Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space

2014 ◽  
Vol 44 (10) ◽  
pp. 1156-1164 ◽  
Author(s):  
Anton Grafström ◽  
Svetlana Saarela ◽  
Liviu Theodor Ene
Keyword(s):  
Forest Inventory ◽  
Auxiliary Information ◽  
National Forest Inventory ◽  
Planning Stage ◽  
Entire Area ◽  
Growing Stock ◽  
Forest Inventories ◽  
Field Samples ◽  
A New Technique ◽  
Stock Volume

By using more sophisticated sampling designs in forest field inventories, it is possible to select more representative field samples. When full cover auxiliary information is available at the planning stage of a forest inventory, an efficient strategy for sampling is formed by making sure that the sample is well spread in the space spanned by the auxiliary variables. We show that by using such a sampling design, we can improve not only design-based estimation, but also estimation based on nearest neighbour techniques. A new technique to select well-spread probability samples, in multidimensional spaces, from larger populations is introduced. As an application, we illustrate how this strategy can be applied to a forest field inventory. We use an artificial dataset corresponding to a full cover forest remote sensing inventory of a 30 000 ha area of Kuortane, western Finland. The target variable (growing stock volume) has been generated for the entire area by a copula technique. The artificial population has been validated by utilizing the Finnish National Forest Inventory.

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