Integrating Data from Discrete Return Airborne LiDAR and Optical Sensors to Enhance the Accuracy of Forest Description: A Review

Xu, Cong; Morgenroth, Justin; Manley, Bruce

doi:10.1007/s40725-015-0019-3

Cited by 24 publications

(14 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forest inventory measurements derived from ALS data are often extrapolated to regional or national scales using covariates obtained from optical or radar satellite observations [28][29][30]. This multi-sensor upscaling approach facilitates the wall-to-wall retrieval of forest inventory biometrics through the integration of wide-area satellite observations, high-resolution ALS data and field measurements acquired from a representative sample of forests [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

et al. 2020

View full text Add to dashboard Cite

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.

show abstract

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Li et al (2013) used a hierarchical object-based classification method to classify urban vegetation with high-resolution aerial photography alone [71] and obtained a classification accuracy of 90.5%, which indicates that the object-based classification method was effective for vegetation mapping, including for trees and grasses. Xu et al (2015) reviewed the application of fused airborne LiDAR and optical sensors in forest description [72]. The review demonstrated that the fusion of multiple remote sensing data could improve the performance of forest areas extraction (about 20% accuracy improvement).…”

Section: Performance Of Different Classification Methods For Trees and Grasses Extractionmentioning

confidence: 99%

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

et al. 2020

View full text Add to dashboard Cite

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

show abstract

“…Fusion of multispectral images such as Landsat ETM+ and SAR has two major advantages: (1) enhancement of spectral information; and (2) reducing the problem of cloud cover because SAR is less affected by cloud cover [119]. LiDAR was also integrated with Landsat ETM+ images in order to improve mapping of vertical and longitudinal characteristics of different land cover types [120][121][122]. Generally, when correct algorithms are applied during image fusion of Landsat images with other remote sensing data, improvements are expected on the results of land cover classification [117,123,124].…”

Section: Landsat Image Fusions In Land Cover Classificationmentioning

confidence: 99%

Developments in Landsat Land Cover Classification Methods: A Review

2017

Self Cite

View full text Add to dashboard Cite

Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

show abstract

Integrating Data from Discrete Return Airborne LiDAR and Optical Sensors to Enhance the Accuracy of Forest Description: A Review

Cited by 24 publications

References 96 publications

Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

Developments in Landsat Land Cover Classification Methods: A Review

Contact Info

Product

Resources

About