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The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

The Ice, Cloud, and Land Elevation Satellite–2 (ICESat–2) carries the Advanced Topographic Laser Altimeter System (ATLAS), enabling global canopy height measurements from forest canopy height models (CHMs). Topographic slope is a crucial factor affecting the accuracy of canopy height estimates from ICESat–2 CHMs, but it has not been sufficiently studied. This paper aims to eliminate the influence of slope on canopy height estimates from ICESat–2 data and establishes a method for correcting forest canopy heights based on high spatial resolution digital orthophoto maps (DOM). The cross-track photons are corrected horizontally to eliminate the estimation error. Multi-resolution segmentation is used to segment tree crowns in the DOM, and the distance and relative position between the top of canopy (TOC) photons and the center point of the crown are calculated. TOC photon correction rules are established for different terrains, and the vertical error of the TOC photons is corrected. The results indicate that the vertical error increases exponentially with the slope. The cross-track photon correction and the TOC photon correction method eliminate the effect of slope on canopy height estimates. The cross-track photon correction method reduces the mean absolute error (MAE) and root mean square error (RMSE) of the canopy height estimates by 35.71% and 35.98%, respectively. The TOC photon correction approach further reduces the MAE and RMSE by 23% and 19.23%, respectively. The proposed method has significantly higher accuracy for forest canopy height estimation using ICESat–2 data than the traditional method. [ABSTRACT FROM AUTHOR]

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. [ABSTRACT FROM AUTHOR]

Using Montarice in central Adriatic Italy as a case study, this paper focuses on the extraction of the spectral (i.e., plant colour) and geometrical (i.e., plant height) components of a crop canopy from archived aerial photographs, treating both parameters as proxies for archaeological prospection. After the creation of orthophotographs and a canopy height model using image-based modelling, new archaeological information is extracted from this vegetation model by applying relief-enhancing visualisation techniques. Through interpretation of the resulting data, a combination of the co-registered spectral and geometrical vegetation dimensions clearly add new depth to interpretative mapping, which is typically based solely on colour differences in orthophotographs. [ABSTRACT FROM AUTHOR]

In the field of precision agriculture, weed detection models combined with selective spraying by ground or aerial robotics are seen as promising approaches for increasing yield harvests while simultaneously minimizing herbicide use. The available labeled training data are a major bottleneck for developing and applying supervised deep learning models, and more automated methods for labeled data generation are therefore needed. Our research aims to address this need by introducing a rule-based method for label data generation for perennial weeds. For this research, a dataset of a barley field was collected using an unmanned aerial vehicle (UAV) with a flight altitude of 10 m. A multispectral and a thermal camera were used for the data collection. The preprocessed dataset consists of multispectral and thermal orthomosaic images along with a canopy height model. The methodological part of this article introduces a proposed rule-based method for labeled data generation for perennial weeds based on the Normalized Difference Vegetation Index (NDVI), and this approach is further used to generate labels for the measured data. The preprocessed data combined with the generated labels was used to train U-net models. Three data combinations are used for the training and testing: multispectral, multispectral–thermal and multispectral–thermal–canopy–height–model. This approach was used to evaluate whether additional data improve model performance. To evaluate the models on ground truth labels, they are tested on a manually annotated test dataset, which consists of 10% of the whole dataset. The tested models achieved an F1 score of 0.82–0.83 on the test dataset. This finding demonstrates that the rule-based labeling method generates valid labels for the perennial weed detection task. Furthermore, our study shows that data fusion improved the model slightly. The data combination of multispectral–thermal–canopy–height–model as input resulted in the best-performing model, with an F1 score of 0.835. [ABSTRACT FROM AUTHOR]

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations. [ABSTRACT FROM AUTHOR]

Estimating consistent large-scale tropical forest height using remote sensing is essential for understanding forest-related carbon cycles. The Global Ecosystem Dynamics Investigation (GEDI) light detection and ranging (LiDAR) instrument employed on the International Space Station has collected unique vegetation structure data since April 2019. Our study shows the potential value of using remote-sensing (RS) data (i.e., optical Sentinel-2, radar Sentinel-1, and radar PALSAR-2) to extrapolate GEDI footprint-level forest canopy height model (CHM) measurements. We show that selected RS features can estimate vegetation heights with high precision by analyzing RS data, spaceborne GEDI LiDAR, and airborne LiDAR at four tropical forest sites in South America and Africa. We found that the GEDI relative height (RH) metric is the best at 98% (RH98), filtered by full-power shots with a sensitivity greater than 98%. We found that the optical Sentinel-2 indices are dominant with respect to radar from 77 possible features. We proposed the nine essential optical Sentinel-2 and the radar cross-polarization HV PALSAR-2 features in CHM estimation. Using only ten optimal indices for the regression problems can avoid unimportant features and reduce the computational effort. The predicted CHM was compared to the available airborne LiDAR data, resulting in an error of around 5 m. Finally, we tested cross-validation error values between South America and Africa, including around 40% from validation data in training to obtain a similar performance. We recommend that GEDI data be extracted from all continents to maintain consistent performance on a global scale. Combining GEDI and RS data is a promising method to advance our capability in mapping CHM values. [ABSTRACT FROM AUTHOR]

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA's current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson's r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA's operational forest inventory estimates. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI), specifically designed to measure vertical forest structures, has acquired, since April 2019, more than 35 billion waveforms of Earth's surface on a nearly global scale. GEDI is equipped with 3 identical 1064 nm lasers with a power of 10 mJ per shot, where 1 laser is split into 2 lasers, resulting in two 5 mJ coverage lasers and two 10 mJ full-power lasers. In this study, we evaluate the potential of GEDI's four lasers to penetrate through canopies and detect the ground, and their capabilities to detect the top of the canopies over a tropical forest (in French Guiana) characterized by a dense canopy cover and tall trees. The accurate detection of both of these surfaces is the first step in characterizing vertical forest structures. The SRTM Digital Elevation Model (DEM) is used as a reference point for elevations while a canopy height model (CHM), derived from airborne and spaceborne LiDAR data, is used as a reference for canopy heights. In addition, the ground and canopy-top elevations estimated from NASA's Land, Vegetation, and Ice Sensor (LVIS, 1064 nm full-waveform LiDAR, 5 mJ per shot, ~8 km altitude) are used as a benchmark for comparison with GEDI's lasers. Results indicate that GEDI's coverage and full-power lasers, even after the application of a preliminary filter that removes around 50% of acquisitions, tend to underestimate tree heights in densely vegetated, tall forests. Moreover, GEDI's coverage lasers also exhibited a lower level of performance in comparison to both the full-power lasers and LVIS. Overall, the average estimated maximum canopy heights (RH100) for a CHM greater than 30 m was 24.4 m with the coverage lasers, 32.1 m with the full-power lasers, and 36.7 m with LVIS. The analysis of shots with high-beam sensitivity (sensitivity ≥ 98%) showed that they tend to have a better probability of reaching the ground and have better detection of canopy tops for both GEDI laser types. Nonetheless, GEDI's coverage lasers still showed an underestimation of canopy heights with an average RH100 of 29.8 m, while for GEDI's full-power lasers and LVIS, the average RH100 was 35.2 m and 37.7 m, respectively. Finally, the assessment of the acquisition time on the detection of the ground return and the top of the canopies showed that, for the coverage lasers, solar noise could affect the detection of the ground return as acquisitions made during early mornings or late afternoons have more penetration than shots acquired between 8 a.m. and 4 p.m. The effect of acquisition time on the detection of the tops of canopies showed that solar noise slightly affected the coverage lasers. Regarding the full-power lasers, the acquisition time of the shots seem to affect neither the penetration of the lasers, nor the detection of the tops of canopies. [ABSTRACT FROM AUTHOR]

Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the success of forest management actions, for example, the establishment of natural regeneration. In this case study, we investigated the possibilities and perspectives of close-range photogrammetric approaches for modeling the amount of potential direct and diffuse solar radiation during the growing seasons (spring-summer), by comparing the performance of low-cost Unmanned Aerial Vehicle (UAV) RGB imagery vs. Hemispherical Photography (HP). Characterization of the solar environment based on hemispherical photography has already been widely used in botany and ecology for a few decades, while the UAV method is relatively new. Also, we compared the importance of several components of potential solar irradiation and their impact on the regeneration of Pinus sylvestris L. For this purpose, a circular fisheye objective was used to obtain hemispherical images to assess sky openness and direct/diffuse photosynthetically active flux density under canopy average for the growing season. Concerning the UAV, a Canopy Height Model (CHM) was constructed based on Structure from Motion (SfM) algorithms using Photoscan professional. Different layers such as potential direct and diffuse radiation, direct duration, etc., were extracted from CHM using ArcGIS 10.3.1 (Esri: California, CA, USA). A zonal statistics tool was used in order to extract the digital data in tree positions and, subsequently, the correlation between potential solar radiation layers and the number of seedlings was evaluated. The results of this study showed that there is a high relation between the two used approaches (HP and UAV) with R2 = 0.74. Finally, potential diffuse solar radiation derived from both methods had the highest significant relation (-8.06% bias) and highest impact in the modeling of pine regeneration. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) can be utilised to derive canopy height models (CHMs) for individual tree crown (ITC) delineation. In the case of forest areas subject to defoliation and dieback as a result of disease, increased irregularities across the canopy can add complications to the segmentation of ITCs. Research has yet to address this issue in order to suggest appropriate techniques to apply under conditions of forest stands that are infected by phytopathogens. This study aimed to find the best method of ITC delineation for larch canopies affected by defoliation as a result of a Phytophthora ramorum infection. Sample plots from two study sites in Wales, United Kingdom, were selected for ITC segmentation assessment across a range of infection levels and stand characteristics. The performance of two segmentation algorithms (marker-controlled watershed and region growing) were tested for a series of CHMs generated by a standard normalised digital surface model and a pit-free algorithm, across a range of spatial resolutions (0.15 m, 0.25 m and 0.5 m). The results show that the application of a pit-free CHM generation method produced improved segmentation accuracies in moderately and heavily infected larch forest, compared to the standard CHM. The success of ITC delineations was also influenced by CHM resolution. Across all plots the CHMs with a 0.25 m pixel size performed consistently well. However, lower and higher CHM resolutions also provided improved delineation accuracies in plots dominated by larger and smaller canopies respectively. The selected segmentation method also influenced the success of ITC delineations, with the marker-controlled watershed algorithm generating significantly more accurate results than the region growing algorithm (p < 0.10). The results demonstrate that ITCs in forest stands infected with Phytophthora ramorum can be successfully delineated from ALS when a pit-free algorithm is applied to CHM generation. [ABSTRACT FROM AUTHOR]

Vegetation provides important functions and services in urban areas, and vegetation heights divided into vertical and horizontal units can be used as indicators for its assessment. Conversely, detailed area-wide and updated height information is frequently missing for most urban areas. This study sought to assess three vegetation height classes from a globally available TanDEM-X digital elevation model (DEM, 12 × 12 m spatial resolution) for Berlin, Germany. Subsequently, height distribution and its accuracy across biotope classes were derived. For this, a TanDEM-X intermediate DEM, a LiDAR DTM, an UltraCamX vegetation layer, and a biotope map were included. The applied framework comprised techniques of data integration and raster algebra for: Deriving a height model for all of Berlin, masking non-vegetated areas, classifying two canopy height models (CHMs) for bushes/shrubs and trees, deriving vegetation heights for 12 biotope classes and assessing accuracies using validation CHMs. The findings highlighted the possibility of assessing vegetation heights for total vegetation, trees and bushes/shrubs with low and consistent offsets of mean heights (total CHM: -1.56 m; CHM for trees: -2.23 m; CHM bushes/shrubs: 0.60 m). Negative offsets are likely caused by X-band canopy penetrations. Between the biotope classes, large variations of height and area were identified (vegetation height/biotope and area/biotope: ~3.50-~16.00 m; 4.44%-96.53%). The framework and results offer a great asset for citywide and spatially explicit assessment of vegetation heights as an input for urban ecology studies, such as investigating habitat diversity based on the vegetation's heterogeneity. [ABSTRACT FROM AUTHOR]

Forest canopy height model (CHM) is useful for analyzing forest stocking and its spatiotemporal variations. However, high-resolution CHM with regional coverage is commonly unavailable due to the high cost of LiDAR data acquisition and computational cost associated with data processing. We present a CHM generation method using U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) LiDAR data for tree height measurement capabilities for entire state of Indiana, USA. The accuracy of height measurement was investigated in relation to LiDAR point density, inventory height, and the timing of data collection. A simple data exploratory analysis (DEA) was conducted to identify problematic input data. Our CHM model has high accuracy compared to field-based height measurement (R2 = 0.85) on plots with relatively accurate GPS locations. Our study provides an easy-to-follow workflow for 3DEP LiDAR based CHM generation in a parallel processing environment for a large geographic area. In addition, the resulting CHM can serve as critical baseline information for monitoring and management decisions, as well as the calculation of other key forest metrics such as biomass and carbon storage. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) allow efficient acquisition of forest data at very high resolution at relatively low cost, making it useful for multi-temporal assessment of detailed tree crowns and forest structure. Single-pass flight plans provide rapid surveys for key selected high-priority areas, but their accuracy is still unexplored. We compared aircraft-borne LiDAR with GatorEye UAV-borne LiDAR in the Apalachicola National Forest, USA. The single-pass approach produced digital terrain models (DTMs), with less than 1 m differences compared to the aircraft-derived DTM within a 145° field of view (FOV). Canopy height models (CHM) provided reliable information from the top layer of the forest, allowing reliable treetop detection up to wide angles; however, underestimations of tree heights were detected at 175 m from the flightline, with an error of 2.57 ± 1.57. Crown segmentation was reliable only within a 60° FOV, from which the shadowing effect made it unviable. Reasonable quality threshold values for LiDAR products were: 195 m (145° FOV) for DTMs, 95 m (110° FOV) for CHM, 160 to 180 m (~140° FOV) for ITD and tree heights, and 40 to 60 m (~60° FOV) for crown delineation. These findings also support the definition of mission parameters for standard grid-based flight plans under similar forest types and flight parameters. [ABSTRACT FROM AUTHOR]

Forest species classifications are becoming increasingly automated as advances are made in machine learning. Complex algorithms can reach high accuracies, but are not always suitable for small-scale classifications, which may benefit from simpler conventional methods. The goal of this classification was to identify contiguous stands of ponderosa pine (Pinus ponderosa Douglas ex Lawson) against a mix of forest and non-forest background in the southern Willamette Valley, Oregon. The study area is approximately 816,600 ha, considerably larger than most study areas used for presenting techniques for tree species classification. To achieve the objective, we used two classification procedures, one parametric and one non-parametric. For the parametric method, we selected the maximum likelihood (ML) algorithm, whereas for the non-parametric method we chose the random forest (RF) algorithm. To identify ponderosa pine, we used 1 m spatial resolution red-green-blue-infrared (RGBI) aerial images supplied by the U.S. National Agriculture Imagery Program (NAIP) and 1 m spatial resolution canopy height models (CHMs) provided by the Oregon Department of Geology and Mineral Industries (DOGAMI). We tested four data variations for each method: Aerial imagery, CHM-masked aerial imagery, aerial imagery with an additional CHM band, and CHM-masked aerial imagery with a CHM band. The parametric classifications of aerial imagery alone reached an average kappa coefficient of 0.29, which increased to 0.51 when masked with CHM data. The incorporation of CHM data as a fifth band resulted in a similar improvement in kappa (0.47), but the most effective parametric method was the incorporation of CHM data as both a fifth band and a post-classification mask, resulting in a kappa coefficient of 0.89. The non-parametric classification of aerial imagery achieved a mean validation kappa coefficient of 0.85 collectively and 0.90 individually, which only increased by approximately 0.01 or less when the CHM masks were applied. The addition of the CHM band increased the kappa value to 0.91 for both individual and collective tile classifications. The highest kappa of all methods was achieved through five-band non-parametric classification with the addition of the CHM band (0.94) for both collective and individual classifications. Our results suggest that parametric methods, when enhanced with a CHM mask, could be suitable for large-area, small-scale classifications based on RGBI imagery, but a non-parametric classification of fused spectral and height data will generally achieve the highest accuracy for large, unbalanced datasets. [ABSTRACT FROM AUTHOR]

In this study, we compared the accuracies of above-ground biomass (AGB) estimated by integrating ALOS (Advanced Land Observing Satellite) PALSAR (Phased-Array-Type L-Band Synthetic Aperture Radar) data and TanDEM-X-derived forest heights (TDX heights) at four scales from 1/4 to 25 ha in a hemi-boreal forest in Japan. The TDX heights developed in this study included nine canopy height models (CHMs) and three model-based forest heights (ModelHs); the nine CHMs were derived from the three digital surface models (DSMs) of (I) TDX 12 m DEM (digital elevation model) product, (II) TDX 90 m DEM product and (III) TDX 5 m DSM, which we developed from two TDX–TSX (TerraSAR-X) image pairs for reference, and the three digital terrain models (DTMs) of (i) an airborne Light Detection and Ranging (LiDAR)-based DTM (LiDAR DTM), (ii) a topography-based DTM and (iii) the Shuttle Radar Topography Mission (SRTM) DEM; the three ModelHs were developed from the two TDX-TSX image pairs used in (III) and the three DTMs (i to iii) with the Sinc inversion model. In total, 12 AGB estimation models were developed for comparison. In this study, we included the C-band SRTM DEM as one of the DTMs. According to Walker et al. (2007), the SRTM DEM serves as a DTM for most of the Earth's surface, except for the areas with extensive tree and/or shrub coverage, e.g., the boreal and Amazon regions. As our test site is located in a hemi-boreal zone with medium forest cover, we tested the ability of the SRTM DEM to serve as a DTM in our test site. This study especially aimed to analyze the capability of the two TDX DEM products (I and II) to estimate AGB in practice in the hemi-boreal region, and to examine how the different forest height creation methods (the simple DSM and DTM subtraction for the nine CHMs and the Sinc inversion model-based approach for the three ModelHs) and the different spatial resolutions of the three DSMs and three DTMs affected the AGB estimation results. We also conducted the slope-class analysis to see how the varying slopes influenced the AGB estimation accuracies. The results show that the combined use of the PALSAR data and the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM achieved the highest AGB estimation accuracies across the scales (R2 ranged from 0.82 to 0.97), but the CHMs derived from (I) TDX 12 m DEM and another two DTMs, (ii) and (iii), showed low R2 values at any scales. In contrast, the two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed high R2 values > 0.87 and 0.78, respectively, at the scales > 9.0 ha, but they yielded much lower R2 values at smaller scales. The three ModelHs gave the lowest R2 values across the scales (R2 ranged from 0.39 to 0.60). Analyzed by slope class at the 1.0 ha scale, however, all the 12 AGB estimation models yielded high R2 values > 0.66 at the lowest slope class (0° to 9.9°), including the three ModelHs (R2 ranged between 0.68 to 0.69). The two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed R2 values of 0.80 and 0.71, respectively, at the lowest slope class, while the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM showed high R2 values across the slope classes (R2 > 0.82). The results show that (I) TDX 12 m DEM had a high capability to estimate AGB, with a high accuracy across the scales and the slope classes in the form of CHM, but the use of (i) LiDAR DTM was required. On the other hand, (II) TDX 90 m DEM was able to achieve high AGB estimation accuracies not only with (i) LiDAR DTM, but also with (iii) SRTM DEM in the form of CHM, but it was limited to large scales > 9.0 ha; however, all the models developed in this study have the possibility to achieve higher AGB estimation accuracies at the 1.0 ha scale in flat terrains with slope < 10°. The analysis showed the strengths and limitations of each model, and it also indicates that the data creation methods, the spatial resolutions of datasets and topographic features affects the effective spatial scales for AGB mapping, and the optimal combinations of these features should be chosen to obtain high AGB estimation accuracies. [ABSTRACT FROM AUTHOR]

Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R2 = 0.89, RMSE = 91.48 g/m2, rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass. [ABSTRACT FROM AUTHOR]