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 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.

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

Retrieval of forest canopy height jointly using airborne LiDAR and ALOS PALSAR data

2019 ◽  
Vol 14 (02) ◽  
pp. 1
Author(s):  
Min Xu ◽  
Haibing Xiang ◽  
Hongquan Yun ◽  
Xiliang Ni ◽  
Wei Chen ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Alos Palsar

scholarly journals Mapping Physiognomic Types of Indigenous Forest using Space-Borne SAR, Optical Imagery and Air-borne LiDAR

2019 ◽  
Vol 11 (16) ◽  
pp. 1911
Author(s):  
John R. Dymond ◽  
Jan Zörner ◽  
James D. Shepherd ◽  
Susan K. Wiser ◽  
David Pairman ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Structural Information ◽  
European Space Agency ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Support Vector ◽  
Alos Palsar ◽  
Optical Imagery ◽  
Indigenous Forest

Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from the European Space Agency (ESA) Sentinel-1 and 2 missions, Advanced Land Orbiting Satellite (ALOS) PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest. A five-fold cross-validation (repeated 100 times) of ground data showed that the highest classification accuracy of 80.5% is achieved for bands 2, 3, 4, 8, 11, and 12 from Sentinel-2, the ratio of bands VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) from Sentinel-1, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on optical bands alone was 72.7% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.4%. The classification accuracy is sufficient for many management applications for indigenous forest, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management.

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 Physiognomic Types of Indigenous Forest in New Zealand Using Space-Borne SAR, Optical Imagery and Air-Borne LiDAR

2019 ◽  
Author(s):  
John R. Dymond ◽  
Jan Zörner ◽  
James D. Shepherd ◽  
Susan K. Wiser ◽  
David Pairman ◽  
...  
Keyword(s):  
New Zealand ◽  
Classification Accuracy ◽  
Structural Information ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Support Vector ◽  
Alos Palsar ◽  
Optical Imagery ◽  
Indigenous Forest

Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from ESA’s Sentinel-1 and 2 missions, ALOS PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest in New Zealand. A five-fold cross-validation of ground data showed that the highest classification accuracy of 80.9% is achieved for bands 2, 3, 4, 5, 8, 11, and 12 from Sentinel-2, the ratio of bands VH and VV from Sentinel-1, HH from PALSAR, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on the optical bands alone was 73.1% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.8%. The classification accuracy is sufficient for many management applications for indigenous forest in New Zealand, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management. National application of the method will be possible in several years, once national LiDAR coverage is achieved, and a national canopy height model is available.

scholarly journals STANDWISE CHANGE DETECTION FOR GROWING STOCK USING REPEAT-PASS ALOS PALSAR / PALSAR-2 DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 841-845
Author(s):  
M.–G. Hong ◽  
C. Kim
Keyword(s):  
Gradual Increase ◽  
Test Site ◽  
Topographic Effects ◽  
Mountainous Areas ◽  
Backscattering Coefficient ◽  
Alos Palsar ◽  
Stand Growth ◽  
Growing Stock ◽  
Growth Changes ◽  
Stock Volume

This study demonstrates the possibility of detecting the changes of growing stocks in mountainous forest stands derived from ALOS PALSAR and PALSAR-2 images. The ALOS PALSAR were obtained over the Kwangneung Experiment Forest (KEF, Korea) during the period of nineteen and a half months from the April 26, 2009 to December 12, 2010, whereas the PALSAR-2 data were acquired on the April 7, 2015. The KEF test site comprises 58 stands, which cover approximately 1,000ha and have steep slope topography. Owing to topographic effects of SAR data in mountainous areas, the DEM-assisted topographic normalized backscattering coefficient γ<sup>0</sup> was applied to the evaluation of the relationships between the ALOS PALSAR / PALSAR-2 HV backscatter and the field inventory–based stand stock volume. The results indicate that: 1) the γ<sup>0</sup> values for the volume obtained from ALOS PALSAR data on December 12, 2010 show a gradual increase higher than those computed from the data on April 26, 2009, here the γ<sup>0</sup> value increases in accordance with an increase in the volume: 2) the γ<sup>0</sup> values determined from the PALSAR-2 data increase with the same inventory-based volume, when compared with those computed from both ALOS PALSAR data. They also increase substantially as the values of the volume rise, with the exception of the volume interval from 130 m<sup>3</sup> ha<sup>−1</sup> to 160 m<sup>3</sup> ha<sup>−1</sup>. This is understandable because the volume of the aforementioned interval has been reduced through clearing. Consequently, the γ<sup>0</sup>–based relationship between PALSAR-2 HV backscatter and growing stock can lead to detecting the stand growth changes in the KEF of Korea.

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