- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?
摘要： In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°–5° and 15°–20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.
关键词： understory vegetation,LiDAR,forest fuel,relative radiometric correction,software tool,processing tool
更新于2025-09-19 17:13:59
-
A Single-Tree Processing Framework Using Terrestrial Laser Scanning Data for Detecting Forest Regeneration
摘要： Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height > 130 cm in combination with a DBH < 12 cm and unestablished regeneration, consisting of trees with a height < 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration.
关键词： TLS,forestry,3D image segmentation,understory,tree detection,tree stems,DBH
更新于2025-09-11 14:15:04
-
Linking Phenological Indices from Digital Cameras in Idaho and Montana to MODIS NDVI
摘要： Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial based sensors. This study links greenness indices derived from digital images in a network of rangeland and forested sites in Montana and Idaho to 16-day normalized difference vegetation index (NDVI) from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Multiple digital cameras were placed along a transect at each site to increase the observational footprint and correlation with the coarser MODIS NDVI. Digital camera phenology indices were averaged across cameras on a site to derive phenological curves. The phenology curves, as well as green-up dates, and maximum growth dates, were highly correlated to the satellite derived MODIS composite NDVI 16-day data at homogeneous rangeland vegetation sites. Forested and mixed canopy sites had lower correlation and variable significance. This result suggests the use of MODIS NDVI in forested sites to evaluate understory phenology may not be suitable. This study demonstrates that data from digital camera networks with multiple cameras per site can be used to reliably estimate measures of vegetation phenology in rangelands and that those data are highly correlated to MODIS 16-day NDVI.
关键词： phenology,growing season,NDVI,digital photography,RGB camera,rangeland,understory productivity,phenocam,vegetation
更新于2025-09-10 09:29:36
-
Comprehensive Remote Sensing || Forest Background
摘要： Forest background (understory) is an important component of forest ecosystems typically supporting the majority of total ecosystem floristic diversity (Gilliam and Roberts, 2003; D’Amato et al., 2009). Forest background plays a central role in the dynamics and functioning of forest ecosystems by (i) influencing long-term successional patterns (Hart and Chen, 2006; Nyland et al., 2006); (ii) facilitating nutrient cycling and energy flow as ecosystem drivers (Chapin, 1983; Chastain et al., 2006); (iii) providing sources of food and habitat for wildlife species/animals (Tuanmu et al., 2010); and (iv) affecting forest fire danger and fire behavior in the event of fire occurrences (Pereira et al., 2004; Soares-Filho et al., 2012). Understanding forest understory vegetation ecology has important implications for both conservation and production-oriented forest management. Overall evidence is emerging that the type of forest background/understory vegetation present has important economic implications (Nilsson and Wardle, 2005).
关键词： phenology,reflectance,understory,LiDAR,remote sensing,Forest background
更新于2025-09-09 09:28:46