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 Mapping growing stock volume and biomass carbon storage of larch plantations in Northeast China with L-band ALOS PALSAR backscatter mosaics

2018 ◽  
Vol 39 (22) ◽  
pp. 7978-7997 ◽  
Author(s):  
Tian Gao ◽  
J. J. Zhu ◽  
Q. L. Yan ◽  
S. Q. Deng ◽  
X. Zheng ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Northeast China ◽  
Biomass Carbon ◽  
Alos Palsar ◽  
Growing Stock ◽  
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 Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 279 ◽  
Author(s):  
Ernest William Mauya ◽  
Joni Koskinen ◽  
Katri Tegel ◽  
Jarno Hämäläinen ◽  
Tuomo Kauranne ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Small Scale ◽  
Remotely Sensed Data ◽  
Weighted Mean ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

Multitemporal jers repeat-pass coherence for growing-stock volume estimation of siberian forest

2003 ◽  
Vol 41 (7) ◽  
pp. 1561-1570 ◽  
Author(s):  
L.E.B. Eriksson ◽  
M. Santoro ◽  
A. Wiesmann ◽  
C.C. Schmullius
Keyword(s):  
Volume Estimation ◽  
Growing Stock ◽  
Stock Volume

scholarly journals Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

2012 ◽  
Vol 4 (11) ◽  
pp. 3320-3345 ◽  
Author(s):  
Oliver Cartus ◽  
Josef Kellndorfer ◽  
Markus Rombach ◽  
Wayne Walker
Keyword(s):  
Airborne Lidar ◽  
Canopy Height ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume

scholarly journals The Accuracy of Standwise Forest Inventory in Mature Stands

2014 ◽  
Vol 32 (1) ◽  
pp. 2-8
Author(s):  
Ainārs Grīnvalds
Keyword(s):  
Forest Inventory ◽  
Alnus Glutinosa ◽  
Volume Estimation ◽  
Individual Species ◽  
Mixed Stands ◽  
Black Alder ◽  
Growing Stock ◽  
Study Results ◽  
Stock Volume ◽  
Mature Stands

Abstract Traditionally forest resources are estimated in each compartment or stand with ocular standwise forest inventory. However, this inventory technique has shortages with measurement accuracy. In the study the accuracy of the standwise forest inventory was estimated by comparing the growing stock volume of the standwise inventory with the accurate (instrumental) re-measurements. Comparison was done with 4515 mature stands of pine (Pinus sylvestris L.), spruce (Picea abies (L.) Karst.), birch (Betula spp.), aspen (Populus tremula L.) and black alder (Alnus glutinosa L.). The stands’ measurements by callipers or by harvesters (recalculated to growing stock volume) were used for accurate re-measurements. The study results show that the volume of standwise forest inventory have relative bias of 17.6% (volume is underestimated by 17.6%) and relative root mean square error 27.5 % for the whole data. Spruce stands are more accurately measured and black alder stands - inaccurately. The accuracy of pine, birch and mixed stands was similar to overall trends. Stands with volume 200 - 300 m3 ha-1 are more accurately measured and stands with the volume less than 200 m3 ha-1 - most inaccurately. The accuracy of stands with the volume more than 300 m3 ha-1, decreases by increasing the volume of stands. The volume estimation of individual species has different trends in standwise forest inventory. The volume of pine and birch is overestimated and the volume of spruce, aspen and black alder is underestimated.

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