Combining remote sensing imagery and forest age inventory for biomass mapping

Zheng, Guang; Chen, J.M.; Tian, Qingjiu; Ju, Weimin; Xia, X.Q.

doi:10.1016/j.jenvman.2006.07.015

Cited by 112 publications

(83 citation statements)

References 15 publications

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“…In this study, after the WV-2 image was processed, 24 SVIs were computed (Table 1) and utilized to predict the LAI of the six endangered tree species. These indices were selected based on previous studies that predicted forest biophysical traits like LAI and biomass [30,[77][78][79][80][81]. We investigated the use of all SVIs combined together (n = 24) for predicting the LAI of the endangered tree species in each forest stratum (i.e., intact and fragmented forests).…”

Section: Spectral Vegetation Indices (Svis)mentioning

confidence: 99%

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The groundbased methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R 2 Val = 0.75, RMSE Val = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species.

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Section: Spectral Vegetation Indices (Svis)mentioning

confidence: 99%

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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“…The remaining data were used to validate the accuracy of the climate map . The BEPS model has been used to simulate NPP of terrestrial ecosystems for many regions (Liu et al, 1997(Liu et al, , 2002Matsushita and Tamura, 2002;Feng et al, 2007) extensively evaluated at the site level in China (Sun et al, 2004;Zhou et al, 2007;Zheng et al, 2007;Chen et al, 2007). BEPS does not require information about forest age but it can simulate the variations of forest NPP with stand ages indirectly through remotely sensed LAI variation with age.…”

Section: Spatially Explicit Datamentioning

confidence: 99%

Relationships between net primary productivity and stand age for several forest types and their influence on China’s carbon balance

Wang

Li-hua

Chen

et al. 2011

Journal of Environmental Management

109

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“…Forest inventory information such as diameter at breast height (DBH), tree height (h), species, basal area and volume are critical to assessing the potential of wild fire hazard [1], obtaining and validating aboveground biomass, calculating forest ecosystem services and assessing carbon sequestration strategies for sustainable management [2][3][4]. Forest inventory has facilitated studies and research not only regarding the economic aspects of forest management, such as timber product sale or revenue earnings [5]; but also the ecological aspects including wildlife habitat [6,7], forest stability, ecosystem services [8,9] and natural biodiversity conservation [10].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest

2011

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Abstract:We present the point cloud slicing (PCS) algorithm, to post process point cloud data (PCD) from terrestrial laser scanning (TLS). We then test this tool for forest inventory application in urban heterogeneous forests. The methodology was based on a voxel data structure derived from TLS PCD. We retrieved biophysical tree parameters including diameter at breast height (DBH), tree height, basal area, and volume. Our results showed that TLS-based metrics explained 91.17% (RMSE = 9.1739 cm, p < 0.001) of the variation in DBH at individual tree level. Though the scanner generated a high-density PCD, only 57.27% (RMSE = 0.7543 m, p < 0.001) accuracy was achieved for predicting tree heights in these very heterogeneous stands. Furthermore, we developed a voxel-based TLS volume estimation method. Our results showed that PCD generated from TLS single location scans only captures 18% of the total tree volume due to an occlusion effect; yet there are significant relationships between the TLS data and field measured parameters for DBH and height, giving promise to the utility of a side scanning approach. Using our method, a terrestrial LiDAR-based inventory, also applicable to mobile-or vehicle-based laser scanning (MLS or VLS), was produced for future calibration of Aerial Laser Scanning (ALS) data and urban forest canopy assessments. OPEN ACCESSRemote Sens. 2012, 4 2

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Combining remote sensing imagery and forest age inventory for biomass mapping

Cited by 112 publications

References 15 publications

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Relationships between net primary productivity and stand age for several forest types and their influence on China’s carbon balance

Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest

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