Terrestrial laser scanning in forest inventories

Liang, Xinlian; Kankare, Ville; Hyyppä, Juha; Wang, Yunsheng; Kukko, Antero; Haggrén, Henrik; Yu, Xiaowei; Kaartinen, Harri; Jaakkola, Anttoni; Guan, Fengying; Holopainen, Markus; Vastaranta, Mikko

doi:10.1016/j.isprsjprs.2016.01.006

Cited by 552 publications

(460 citation statements)

References 90 publications

Supporting

Mentioning

431

Contrasting

Unclassified

Order By: Relevance

“…It uses a SICK LMS151 LiDAR and records the first and last returns at 905 nm over a 360 • horizontal and 270 • vertical scan in less than 35 s. It has a 0.25 • angular resolution and a 0.86 • beam divergence and a maximum range of 40 m. The CBL was placed on a tripod and oriented with a level so that the resulting x, y point cloud was in the horizontal plane. Four CBL scans were taken from opposing sides of each plot to minimize occlusion [45,46]. For each scan, the CBL was placed 1.6 m from the side of the plot at a height of 1.6 m. This ensured that the CBL was always above the grass (heights 50 to 70 cm) and meant that the grass was between 1.84 m and 2.80 m away from the CBL.…”

Section: Remotely Sensed Data Measurementmentioning

confidence: 99%

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

et al. 2017

View full text Add to dashboard Cite

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGB grass r 2 = 0.42, AGB total r 2 = 0.32) than the TLS (AGB grass r 2 = 0.46, AGB total r 2 = 0.57) or SfM (AGB grass r 2 = 0.54, AGB total r 2 = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems.

show abstract

Section: Remotely Sensed Data Measurementmentioning

confidence: 99%

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In order to derive meaningful harmonized stand-level metrics of forest stand for such a purpose, Tomppo et al [43] suggested a sample plot of 0.5 hectares would be appropriate. As a result, a terrestrial LiDAR technique [13,[43][44][45][46][47] would be appropriate for collecting accurate ground data that minimize measurement uncertainty for stand-level AGC modeling. Additionally, it may be possible to use low resolution airborne LiDAR data or high-resolution satellite SAR images such as POLSAR [48] and TanDEM-X [49] to obtain parameters of forest canopy as the predictors of stand-level AGC models.…”

Section: Recommendations For Future Workmentioning

confidence: 99%

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

2016

View full text Add to dashboard Cite

Abstract:This study developed an IPCC (Intergovernmental Panel on Climate Change) compliant method for the estimation of above-ground carbon (AGC) in forest stands using remote sensing technology. A multi-level morphological active contour (MMAC) algorithm was employed to obtain tree-level metrics (tree height (LH), crown radius (LCR), competition index (LCI), and stem diameter (LDBH)) from an airborne LiDAR-derived canopy height model. Seven biomass-based AGC models and 13 volume-based AGC models were developed using a training dataset and validated using a separate validation dataset. Four accuracy measures, mean absolute error (MAE), root-mean-square error (RMSE), percentage RMSE (PRMSE), and root-mean-square percentage error (RMSPE) were calculated for each of the 20 models. These measures were transformed into a new index, accuracy improvement percentage (AIP), for post hoc testing of model performance in estimating forest stand AGC stock. Results showed that the tree-level AGC models explained 84% to 91% of the variance in tree-level AGC within the training dataset. Prediction errors (RMSEs) for these models ranged between 15 ton/ha and 210 ton/ha in mature forest stands, which is equal to an error percentage in the range 6% to 86%. At the stand-level, several models achieved accurate and reliable predictions of AGC stock. Some models achieved 90% to 95% accuracy, which was equal to or superior to the R-squared of the tree-level AGC models. The first recommended model was a biomass-based model using the metrics LDBH, LH, and LCI and the others were volume-based models using LH, LCI, and LCR and LDBH and LH. One metric, LCI, played a critical role in upgrading model performance when banded together with LH and LCR or LDBH and LCR. We conclude by proposing an IPCC-compatible method that is suitable for calculating tree-level AGC and predicting AGC stock of forest stands from airborne LiDAR data.

show abstract

“…In this context, field measurements are also needed to validate biophysical attributes of the vegetation such as volume, carbon stocks, tree height, etc. However, traditional field sampling methods are limited in their application to tropical mountainous regions, and the accuracy of inventory data is constrained by the quality and quantity of the field samples [8]. Fortunately, remote sensing methods have evolved sufficiently to support forest inventories in large landscapes [9], which allows rapid, automatic, and periodical estimates of many forest inventory attributes [8] and can also help to improve the precision of forest area estimates [10].…”

Section: Introductionmentioning

confidence: 99%

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Kou

Liang

Wei

et al. 2017

Forests

View full text Add to dashboard Cite

Updated extent, area, and spatial distribution of tropical evergreen forests from inventory data provides valuable knowledge for research of the carbon cycle, biodiversity, and ecosystem services in tropical regions. However, acquiring these data in mountainous regions requires labor-intensive, often cost-prohibitive field protocols. Here, we report about validated methods to rapidly identify the spatial distribution of tropical forests, and obtain accurate extent estimates using phenology-based procedures that integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. Firstly, an analysis of temporal profiles of annual time-series MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) was developed to identify the key phenology phase for extraction of tropical evergreen forests in five typical lands cover types. Secondly, identification signatures of tropical evergreen forests were selected and their related thresholds were calculated based on Landsat NDVI, EVI, and LSWI extracted from ground true samples of different land cover types during the key phenology phase. Finally, a map of tropical evergreen forests was created by a pixel-based thresholding. The developed methods were tested in Xishuangbanna, China, and the results show: (1) Integration of Landsat and MODIS images performs well in extracting evergreen forests in tropical complex mountainous regions. The overall accuracy of the resulting map of the case study was 92%; (2) Annual time series of high-temporal-resolution remote sensing images (MODIS) can effectively be used for identification of the key phenology phase (between Julian Date 20 and 120) to extract tropical evergreen forested areas through analysis of NDVI, EVI, and LSWI of different land cover types; (3) NDVI and LSWI are two effective metrics (NDVI ≥ 0.670 and 0.447 ≥ LSWI ≥ 0.222) to depict evergreen forests from other land cover types during the key phenology phase in tropical complex mountainous regions. This method can make full use of the Landsat and MODIS archives as well as their advantages for tropical evergreen forests geospatial inventories, and is simple and easy to use. This method is suggested for use with other similar regions.

show abstract

Terrestrial laser scanning in forest inventories

Cited by 552 publications

References 90 publications

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Contact Info

Product

Resources

About