Thirty-five years after the accident, large forest areas in the Chernobyl Exclusion Zone still contain huge amounts of radionuclides released from the Chernobyl Nuclear Power Plant Unit 4 in April ...1986. An assessment of the radiological and radioecological consequences of persistent radioactive contamination and development of remediation strategies for Chernobyl forests imply acquiring comprehensive data on their contamination levels and dynamics of biomass inventories. The most accurate forest inventory data can be obtained in ground timber cruises. However, such cruises in radioactive contaminated forest ecosystems in the Chernobyl Exclusion Zone result in radiation exposures of the personnel involved, which means the need for development of the remote sensing methods. The purpose of this study is to analyze the applicability and limitations of the photogrammetric method for the remote large-scale monitoring of aboveground biomass inventories. Based on field measurements, we estimated the biomass inventories in 31 Scots pine stands including both artificial plantations and natural populations. The stands differed significantly in age (from a few years in natural populations to 115 years in the oldest plantation), productivity (from 0.4 to 19.8 kg m−2), mean height (from 4.1 to 36 m), and other parameters. Photogrammetric data were obtained from the same stands using unmanned aerial vehicle (UAV). These data were then processed using two approaches to derive the canopy height model (CHM) parameters which were tested for correlation with the aboveground biomass inventories. In the first approach, we found that the inventories correlated well with the mean value of CHM of the site (R2 = 0.79). In the second approach, the total aboveground biomass was approximated by a function of the average height of trees detected at the site and the total crown projection area (R2 = 0.78). Among other local parameters, the total crown projection area was identified as the major factor impacting the accuracy of the aboveground biomass inventory estimates from the UAV survey data in both approaches. In the dense stands with the high total crown projections areas (more than 0.90), the average relative deviations of the UAV-based aboveground biomass estimates from the results of the field measurements were close to 0, which means the adequate accuracy of the UAV surveys data for radioecological monitoring purposes. The relative deviations of the UAV-based estimates in both approaches increased in the stands consisting of separated groups of trees, which indicates potential limitation of the approaches and need for their further development.
•Applicability of aerial surveys for biomass estimate in Chernobyl forests was tested.•Canopy height models of the studied stands were derived from photogrammetric datasets.•Biomass inventories correlated well with the average values of canopy height models.•Biomass inventories also correlated with the parameters of individual trees.•Accuracy of biomass estimates was high in the dense forest stands.
The acquisition of high-resolution aboveground biomass (AGB) data cost-effectively and expeditiously represents a formidable challenge within the domain of current ecosystem surveillance. Plot-based ...inventory, the conventional approach for estimating and validating remote sensing data, is nonetheless costly and constrained in terms of spatial coverage. The expeditious advancements in unmanned-aerial-vehicle (UAV) technology furnish the potential to devise AGB equations that transcend traditional diameter-height-based equations alongside techniques for quantifying forest structural parameters through standard RGB aerial imagery. Since the canopy diameter (CD) and tree height (H) can be directly ascertained from UAV-derived datasets, biomass equations parameterized by CD and H may be more valuable. In the present investigation, we established AGB equations predicated on data procured from a UAV outfitted with a high-resolution RGB camera, specifically for planted sparsely Pinus sylvestris forest in central Inner Mongolia, China. Utilizing the aerial imagery, we generated the digital terrain model (DTM), digital surface model (DSM) and the digital orthophoto image (DOM). Then, the canopy height model (CHM) was obtained by subtracting DSM from DTM to extract the H and CD of individual trees. This methodology's CD (R2 = 0.85, RMSE = 0.203 m) and H (R2 = 0.77 and RMSE = 0.671 m) obtained closely mirrored the in-situ measurements. Six prospective AGB equations were constructed for the Pinus sylvestris forest, taking H and CD extracted from UAV aerial survey datasets as the dependent variables. The accuracy of the AGB estimation was appraised by employing extant allometric growth equations, which were parameterized using the ground-measured tree diameter at breast height (DBH) and H. The most efficacious biomass equation, predicated on H and CD data extracted from UAV aerial surveys, was delineated as W=2.3442CD∗H0.9057(R2 = 0.731, RMSE = 2.46 kg), thus presenting a convenient tool for estimating the AGB of sparse Pinus sylvestris forests in semi-arid locales.
•Aboveground biomass equation parameterized by UAV-derived parameter was constructed for Pinus sylvestris forest.•Equations based on UAV-derived parameters were evaluated by equations parameterized by ground-measured parameters.•Optimal biomass equation based on UAV-derived parameters is W=2.3442CD•H0.9057 (R2 = 0.731, RMSE = 2.46 kg).
Polarimetric SAR tomography (Pol-TomoSAR) can be used for global forest DTM and CHM mapping with high spatial and temporal resolution at low economic cost. However, the performance of DTM and CHM ...inversion in current Pol-TomoSAR methods is often compromised when the ground-to-volume ratio (GVR) is low, which usually happens in complicated terrain where large negative slope angles are commonly present, or in dense tropical forest where the ground visibility is low due to strong attenuation by the dense vegetation layer. In this work, a novel method for the separation of ground and volume scattering, aiming at robust and accurate DTM and CHM inversion in dense tropical forest and complicated terrain, is proposed. By fully exploiting multibaseline polarimetric SAR data, the proposed method can perform a more effective separation of ground and volume scattering. Subsequently, by applying the SAR tomography technology on the separated ground and volume scattering, the proposed method can retrieve accurate DTM and CHM information even at low GVR areas. Numerical experiments conducted on both simulated data and P-band airborne F-SAR data show that, compared to the most commonly used two-component algebraic synthesis method, the proposed one has a much better performance in terms of separation of ground and volume scattering. Furthermore, the inversed DTM and CHM present better agreement with LiDAR measurements, especially in large negative slope angle terrain where the GVR is rather low.
The precise and rapid estimation of leaf area index (LAI) is crucial for quantitative ecological monitoring and assessment, as it serves as an important ecological indicator of plant growth state and ...canopy structure. The acquisition, integration, and application of multiple remote sensing data from Unmanned Aerial Vehicle (UAV) platforms in quantitative ecological research is a current research hotspot. This study aims to explore the potential of combining morphological and spectral parameters using the symbolic regression (SR) algorithm with allometric models to improve the estimation of Leaf Area Index (LAI) at a regional scale of island ecosystem using UAV multi-source remote sensing data. The Canopy Height Model (CHM), Vegetation Density (VD), and Vegetation Indices (VIs), derived from UAV multi-platforms, were used as independent variables in the development of the retrieval models. Subsequently, regression equations were used to construct the Singlet, Duplet, and Triplet models. The precise assessments indicated that the triplet model demonstrates the highest accuracy, with a mean absolute error (MAE) of 9.48% and root mean square error (RMSE) of 0.2345. This improvement was observed compared to the singlet model (MAE = 19.55%, RMSE = 0.4091) and duplet model (MAE = 15.88%, RMSE = 0.3450), which can be attributed to incorporating morphological parameters to reduce the impact of tree height and leaf density. The analysis also showed that the estimated LAI accuracy is generally higher for shrubs and herbs compared to trees, and that lower plants with high vegetation coverage are better retrieved. The methodology advaneced in this study demonstrates significant potential and implications for ecological monitoring and assessment of island ecosystems, thereby expanding the research scope in the field of island ecology. Furthermore, it provides scientific evidence and technical support for decision-makers and scholars to propose and implement various measures for the conservation and restoration of island ecosystems.
•Proposed a methodology of improving LAI estimation combining morphological and spectral parameters with symbolic regression.•Triplet model achieved highest accuracy, outperforming singlet and duplet models.•Higher LAI accuracy for shrubs and herbs compared to trees.•Implications for island ecological monitoring and ecosystem conservation.
Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized canopy height model based methods often suffer from ...3-D information loss due to the interpolation operation. Therefore, this article proposes an individual tree segmentation method based on the marker-controlled watershed algorithm and 3-D spatial distribution analysis from airborne LiDAR point clouds. First, based on the potential tree apices derived from the local maxima filtering, the marker-controlled watershed segmentation algorithm is conducted to obtain the coarse point clusters. Then, within the principal component analysis defined local coordinate reference framework, a multidirectional 3-D spatial profile analysis is performed on each point cluster to refine the potential tree apex positions. Finally, the refined potential tree apex positions are used as a prior of K-means clustering to achieve the coarse-to-fine individual tree segmentation. Comparative experiments were conducted on the public NEWFOR dataset to evaluate the proposed method. Results indicate that the proposed method is efficient and robust for segmenting individual trees.
High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew ...a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86–87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015–2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool.
Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km2), offers a ...potential data source to calculate digital surface models (DSMs). We present an automated workflow to generate a nationwide DSM with a resolution of 1 1 m based on photogrammetric image matching. A canopy height model (CHM) is derived in combination with an existing digital terrain model (DTM). ADS40/ADS80 summer images from 2007 to 2012 were used for stereo matching, with ground sample distances (GSD) of 0.25 m in lowlands and 0.5 m in high mountain areas. Two different image matching strategies for DSM calculation were applied: one optimized for single features such as trees and for abrupt changes in elevation such as steep rocks, and another optimized for homogeneous areas such as meadows or glaciers. The country was divided into 165,500 blocks, which were matched independently using an automated workflow. The completeness of successfully matched points was high, 97.9%. To test the accuracy of the derived DSM, two reference data sets were used: (1) topographic survey points (n = 198) and (2) stereo measurements (n = 195,784) within the framework of the Swiss National Forest Inventory (NFI), in order to distinguish various land cover types. An overall median accuracy of 0.04 m with a normalized median absolute deviation (NMAD) of 0.32 m was found using the topographic survey points. The agreement between the stereo measurements and the values of the DSM revealed acceptable NMAD values between 1.76 and 3.94 m for forested areas. A good correlation (Pearson's r = 0.83) was found between terrestrially measured tree height (n = 3109) and the height derived from the CHM. Optimized image matching strategies, an automatic workflow and acceptable computation time mean that the presented approach is suitable for operational usage at the nationwide extent. The CHM will be used to reduce estimation errors of different forest characteristics in the Swiss NFI and has high potential for change detection assessments, since an aerial stereo imagery update is available every six years.
Light detection and ranging (LiDAR) remote sensing systems are deployed in various platforms including satellites, airplanes, and drones—which, in essence, determines the sampling characteristics of ...the underlying imaging system. Low-altitude LiDARs provide high photon count and high spatial resolution but only in very localized patches. Satellite LiDARs, on the other hand, provide measurements at a global scale but are limited by low photon count and their samples are sparsely apart along swath line trajectories that are far in between. This article describes a new class of satellite remote sensing LiDARs, aimed at overcoming the limitations of current satellite imaging systems. It exploits the principles of compressive sensing and machine learning (ML) to compressively sense Earth from hundreds of kilometers above Earth to then reconstruct the 3-D imagery with resolution and coverage, as if the data were collected from airborne platforms at just hundreds of meters in height. We introduce a novel representation of waveform altimetry profiles, coined hyperheight data cubes (HHDCs), which encompass rich information about the 3-D structure of a scene. Canopy height models (CHMs), digital terrain models (DTMs), and many other features of a scene that are embedded in HHDC are easily extracted with simple statistical quantiles. We introduce ML methods to reconstruct the compressive LiDAR measurements so as to attain high-resolution, dense coverage, and broad field-of-view per swath pass. ML training data are attained from NASA’s G-LiHT imaging missions. Simulations with various types of forests across the US illustrate the power of the new LiDAR imaging systems.
Accurate, cost-effective monitoring and management of young forests is important for future stand quality. There is a critical need for a rapid assessment tool for forest monitoring and management. ...This study uses a low-cost unmanned aerial vehicle (UAV) to complete a tree height and tree density assessment in a newly forested Chinese fir (
Cunninghamia lanceolata
(Lamb) Hook) planting (15 sample plots), Shunchang County, Fujian, China (1.12 ha). Images obtained from a Phantom4-Multispectral UAV were used to generate a digital surface model (DSM) with DJI Terra software (0.02 m spatial resolution). Based on the DSM, the individual trees were identified and the height of each corresponding tree was determined. The impacts of factors related to individual tree detection (ITD) and tree height accuracy were also analyzed. For the tree-level, the highest accuracy of ITD for Chinese fir was 98.93% (
F
-score = 98.93%). Remotely sensed individual tree heights produced an R
2
value of 0.89, RMSE value of 0.22 m when compared to a field survey. At the stand-level, tree height assessment yielded R
2
= 0.95, RMSE = 0.12 m, and tree density assessment yielded R
2
= 0.99, RMSE = 48 tree ha
−1
. The results highlight that UAVs can successfully monitor forest parameters and hold great potential as a supplement or substitute tool in field inventory.
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