Estimating Forest and Woodland Aboveground Biomass Using Active and Passive Remote Sensing

Wu, Zhuoting; Dye, Dennis G.; Vogel, John M.; Middleton, Barry R.

doi:10.14358/pers.82.4.271

Cited by 21 publications

(16 citation statements)

References 61 publications

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“…Messinger et al (2016) were able to produce above ground carbon estimates of amazon forests with error values as low as 0.05%. These numbers are comparable to other studies that have studied biomass estimation in eastern forests (Wu et al, 2016) and this is not a surprise that the accuracy of these estimates is greater than that of merchantable tree volumes. This measure was coarser than what was performed in this study and does not take in to account a great number of details necessary to evaluate useable board foot volumes.…”

Section: Discussionsupporting

confidence: 86%

A Work Flow and Evaluation of Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Liebermann¹

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A work flow and evaluation of using unmanned aerial systems for deriving forest stand characteristics in mixed hardwoods of West Virginia Henry Liebermann Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions and have added another approach to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board foot volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated segmentation was performed with Trimble eCognition Developer's multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and the high resolution imagery was able to provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted.

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Section: Discussionsupporting

confidence: 86%

A Work Flow and Evaluation of Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Liebermann¹

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“…Given the physical and financial resources involved in surveying, remote sensing is the only answer. While using a suite of other available methods in flat terrains with low biodiversity, products like Landsat-8 seem best suited for broader spatial-scale forest carbon products, while airborne lidar can be used for estimating fine-scale above-ground forest carbon mapping with low uncertainty 21 . Vegetation index-based calibrations of carbon in high biodiversity mountain forests will be subject to large errors.…”

Section: Resultsmentioning

confidence: 99%

Remote Sensing-Derived Spectral Vegetation Indices and Forest Carbon:Testing the Validity of Models in Mountainous Terrain Covered with High Biodiversity

Kumar

Ghose

2017

Current Science

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“…LiDAR has been recognized as a promising technology to characterize vegetation aboveground biomass (AGB) [13,14]. Wu et al [15] concluded that LiDAR-derived height and intensity metrics could map fine-scale AGB with low uncertainty at the plot-level. Numerous lidar-derived candidate metrics, in particular height metrics, exhibit good correlations with forest biomass [16].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

Tang

et al. 2020

Remote Sensing

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Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R2 = 0.96, RMSE = 1.27 kg m−2) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R2 = 0.97, RMSE = 1.33 kg m−2). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.

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Estimating Forest and Woodland Aboveground Biomass Using Active and Passive Remote Sensing

Cited by 21 publications

References 61 publications

A Work Flow and Evaluation of Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

A Work Flow and Evaluation of Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Remote Sensing-Derived Spectral Vegetation Indices and Forest Carbon:Testing the Validity of Models in Mountainous Terrain Covered with High Biodiversity

Estimation of Sugarcane Yield Using a Machine Learning Approach Based on UAV-LiDAR Data

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