Search

The need for objective methods to determine tree canopy cover (CC) across large numbers of stands has led to the development of techniques that utilise airborne laser scanning (ALS) data, which provides a reproducible and detailed representation of canopy geometry. We developed a method for determining CC area and evaluated the estimation accuracy for stands of different sizes, structure and composition. This method is based on tree crown geometries obtained from ALS data, and verified with field measurements using data for 3245 stands of the Białowieża Forest District in Poland. In relatively large stands (3–5 ha), the theoretical error of prediction decreased from 0.13 to 0.10 with increasing stand area. In stands larger than 10 ha, however, the error in estimating CC was less than ±0.10. Although every estimation method comes with its own assumptions and errors, the presented method eliminates the subjectivity in observer bias prevalent in traditional field-based ocular assessments and provides a more transparent and methodologically uniform approach for estimating CC in forests. [ABSTRACT FROM AUTHOR]

Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of canopy height obtained using airborne laser scanning (ALS). CHMs are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses.Here, we used high‐quality ALS data from nine sites in Australia, ranging from semi‐arid shrublands to 90‐m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure.We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to 5 m, or 40% of canopy height). Impacts of point cloud properties on CHM‐derived metrics varied, from robust inference for height percentiles, to considerable errors in above‐ground biomass estimates (~50 Mg ha−1, or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). However, we also found that two CHM algorithms—a variation on a 'spikefree' algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns—allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time.We show that CHM choice has a strong impact on forest structural characterisation that has previously been largely overlooked. To address this, we provide a sample workflow to create robust CHMs and best‐practice guidelines to minimise biases and uncertainty in downstream analyses. In doing so, our study paves the way for more rigorous large‐scale assessments of forest structure and dynamics from airborne laser scanning. [ABSTRACT FROM AUTHOR]

Background: Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat. Traditional ground-based sampling methods have been widely used and provide valuable information; however, they are time-consuming, labor-intensive, and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems. Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms. Our objectives were to (1) assess the accuracy of vegetation cover height in grasses using drones, (2) quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant (fall–winter) and growing seasons (spring–summer), and (3) evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite (Colinus virginianus) habitat in South Texas. We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration. Results: Estimated heights from drone imagery had a significant relationship with the field height measurements in September (r2 = 0.83; growing season) and February (r2 = 0.77; dormant season). Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022–2023 following grazing. Dormant season pasture had an increase in bare ground cover, and a shift from many large patches of tall herbaceous cover (40–120 cm) to few large patches of low herbaceous cover (5–30 cm) (p < 0.05). Conclusions: Drones provided high-resolution imagery that allowed us to assess the spatial and temporal changes of vertical herbaceous vegetation structure in a semi-arid rangeland subject to grazing. This study shows how drone imagery can be beneficial for wildlife conservation and management by providing insights into changes in fine-scale vegetation spatial and temporal heterogeneity from livestock grazing. [ABSTRACT FROM AUTHOR]

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson's correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction. [ABSTRACT FROM AUTHOR]

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers' income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization. [ABSTRACT FROM AUTHOR]

Abstract Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of canopy height obtained using airborne laser scanning (ALS). CHMs are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses. Here, we used high‐quality ALS data from nine sites in Australia, ranging from semi‐arid shrublands to 90‐m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure. We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to 5 m, or 40% of canopy height). Impacts of point cloud properties on CHM‐derived metrics varied, from robust inference for height percentiles, to considerable errors in above‐ground biomass estimates (~50 Mg ha−1, or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). However, we also found that two CHM algorithms—a variation on a ‘spikefree’ algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns—allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time. We show that CHM choice has a strong impact on forest structural characterisation that has previously been largely overlooked. To address this, we provide a sample workflow to create robust CHMs and best‐practice guidelines to minimise biases and uncertainty in downstream analyses. In doing so, our study paves the way for more rigorous large‐scale assessments of forest structure and dynamics from airborne laser scanning.

Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.

Abstract Background Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat. Traditional ground-based sampling methods have been widely used and provide valuable information; however, they are time-consuming, labor-intensive, and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems. Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms. Our objectives were to (1) assess the accuracy of vegetation cover height in grasses using drones, (2) quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant (fall–winter) and growing seasons (spring–summer), and (3) evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite (Colinus virginianus) habitat in South Texas. We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration. Results Estimated heights from drone imagery had a significant relationship with the field height measurements in September (r 2 = 0.83; growing season) and February (r 2 = 0.77; dormant season). Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022–2023 following grazing. Dormant season pasture had an increase in bare ground cover, and a shift from many large patches of tall herbaceous cover (40–120 cm) to few large patches of low herbaceous cover (5–30 cm) (p

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.

The forest floor is covered with tree canopy, and the forest canopy gap is a larger or smaller area of the floor that is not covered by the canopy of the dominant tree layer. The size of the gap affects tree species rejuvenation, as well as the vertical and horizontal structure of the forest. In the Pohorje area (Pahernik’s forest estate), which is characterized by forests where trees of different diameters and ages occur in small areas, we analyzed the possibilities of automatic gap detection based on data from the Laser Scanning of Slovenia (LSS) and the Cyclic Aerial Survey of Slovenia (CAS). We used Canopy Height Models (CHM) derived from both data sources. The overall detection accuracy even for the smallest gaps (smaller than 50 m2) was 94.4% based on the LSS data and 83.1% based on the CAS data. A larger number and area of gaps can be detected based on the LSS data, and the gaps derived from the LSS data are more disaggregated and elongated. The detection of gaps with CAS data and LSS data is more comparable with a minimum gap area of 100 m²; the accuracy of detecting such gaps is 85.4 and 94.4 % respectively. The results of the study show that in the absence of LSS data, the use of CAS data to detect forest canopy gaps on a large scale is useful when gaps with an area of more than 100 m² are detected.

To address poor crop extraction results in mountainous regions using single-feature data in previous research, this study employed a quadcopter to capture aerial orthophoto imagery and image-matching point cloud data from a pitaya cultivation site in the rugged mountainous terrain of southwestern China. The authors identified three critical features: the visible-band difference vegetation index (VDVI), excess green – excess red (ExG-ExR), and canopy height model (CHM) and then integrated them to build a multi-dimensional feature dataset, namely VDVI+CHM and ExG-ExR+CHM. Through a rule-based object-oriented technique, they conducted identification extraction specifically for pitayas plants. The study yielded impressive extraction accuracies, with VDVI, ExG-ExR, CHM segmentation, VDVI+CHM, and ExG-ExR+CHM achieving overall accuracies of 92.34%, 91.05%, 89.08%, 97.56%, and 96.86%, respectively. Furthermore, to validate the accuracy of the extraction results, a regression analysis was conducted to compare the actual canopy area of the pitayas plants determined through human-computer interaction with the extraction results. The root mean square error (RMSE) for VDVI+CHM and ExG-ExR+CHM were found to be 18 dm2 and 25 dm2, respectively, while the coefficient of determination (R2) was 0.81 and 0.67, respectively. Notably, the comparative analysis revealed that VDVI + CHM, which fused multi-dimensional features, exhibited the highest recognition accuracy, demonstrating that integrating multi-dimensional plant features effectively enhanced the accuracy of pitaya plant identification and extraction. By overcoming the limitations of single spectral or spatial structural features, this approach provides valuable insights into the identification and extraction of characteristic economic crops in mountainous regions. [ABSTRACT FROM AUTHOR]

Quantifying the vegetation aboveground biomass (AGB) is crucial for evaluating environment quality and estimating blue carbon in coastal wetlands. In this study, a UAV-LiDAR was first employed to quantify the canopy height model (CHM) of coastal Phragmites australis (common reed). Statistical correlations were explored between two multispectral remote sensing data (Sentinel-2 and JL-1) and reed biophysical parameters (CHM, density, and AGB) estimated from UAV-LiDAR data. Consequently, the reed AGB was separately estimated and mapped with UAV-LiDAR, Sentinel-2, and JL-1 data through the allometric equations (AEs). Results show that UAV-LiDAR-derived CHM at pixel size of 4 m agrees well with the observed stem height (R2 = 0.69). Reed height positively correlates with the basal diameter and negatively correlates with plant density. The optimal AGB inversion model was derived from Sentinel-2 data and JL-1 data with R2 = 0.58, RMSE = 216.86 g/m2 and R2 = 0.50, RMSE = 244.96 g/m2, respectively. This study illustrated that the synergy of UAV-LiDAR data and multispectral remote sensing images has great potential in coastal reed monitoring. [ABSTRACT FROM AUTHOR]

In uneven-aged forests, trees of different diameters, heights, and ages are located in a small area, which is due to the felling of individual trees or groups of trees, as well as small-scale natural disturbances. In this article, we present an objective method for classifying forest stands into even- and uneven-aged stands based on freely available low-resolution (with an average recording density of 5 points/m2) national lidar data. The canopy closure, dominant height, and canopy height diversity from the canopy height model and the voxels derived from lidar data were used to classify the forest stands. Both approaches for determining forest structural diversity (canopy height diversity—CHDCHM and CHDV) yielded similar results, namely two clusters of even- and uneven-aged stands, although the differences in vertical diversity between even- and uneven-aged stands were greater when using CHM. The first analysis, using CHM for the CHD assessment, estimated the uneven-aged forest area as 49.3%, whereas the second analysis using voxels estimated it as 34.3%. We concluded that in areas with low laser scanner density, CHM analysis is a more appropriate method for assessing forest stand height heterogeneity. The advantage of detecting uneven-aged structures with voxels is that we were able to detect shade-tolerant species of varying age classes beneath a dense canopy of mature, dominant trees. The CHDCHM values were estimated to be 1.83 and 1.86 for uneven-aged forests, whereas they were 1.57 and 1.58 for mature even-aged forests. The CHDV values were estimated as 1.50 and 1.62 for uneven-aged forests, while they were 1.33 and 1.48 for mature even-aged forests. The classification of stands based on lidar data was validated with data from measurements on permanent sample plots. Statistically significantly lower average values of the homogeneity index and higher values of the Shannon–Wiener index from field measurements confirm the success of the classification of stands based on lidar data as uneven-aged forests. [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]

Background and Objectives: Estimating forest tree structural attributes such as height and diameter at breast height (DBH) is crucial for understanding the structure and management of forest resources. One important method for estimating these parameters is the individual tree detection (ITD) method using appropriate remote sensing data, such as airborne LiDAR data. However, it should be noted that different ITD methods have various limitations and capabilities and react differently to changes in forest tree species and the vertical structure of the canopy. Methodology: This study presents a hybrid individual tree detection method that combines raster-based and point-based methods in a multi-scale framework to identify single trees from LiDAR data. In this method, tree crown scale levels are obtained from morphological filters in the canopy height model (CHM). Segmentation is then performed using a multi-scale method, and the results are merged. To better separate adjacent and understory trees, the point cloud inside the segments is analyzed using the probability density function, and tree crown segments are modified. After detecting single trees, DBH and height parameters were estimated using ground control data and extracted features from LiDAR data with machine learning algorithms, including random forest (RF), support vector machine (SVM), and cubist (CB), in the form of 10-fold nested cross- validation (10-fold NCV). The Boruta feature selection algorithm was used to identify the most important metrics based on the LiDAR point cloud, which played an effective role in improving the performance of machine learning algorithms. Due to limited access to LiDAR and ground data from Iran’s forests, this study uses the NEWFOR single tree detection benchmark dataset, collected from forests of the Alpine region with a combination of different tree species and vertical canopy structures. Results: Although understory trees cannot be extracted with the same accuracy as overstory trees, the results of this study showed that, on average, the developed multi-scale individual tree detection (MSITD) method detected 89% of the tree crowns in the highest height layer and the highest number of small overstory trees with a detection rate of 48% in the lowest height layer (2-5 meters). The analysis of the machine learning algorithms’ results in estimating forest structural attributes showed that, despite slight differences in performance, the SVM algorithm performed better than the RF and CB algorithms in estimating both height and DBH attributes. For the height attribute, the mean values of RMSE, rRMSE, and R² in the SVM algorithm were 1.75 m, 9%, and 0.85, respectively. For the DBH attribute, the values obtained for RMSE, rRMSE, and R² were 4.74 cm, 19%, and 0.78, respectively. Conclusion: The evaluation of the results showed that the methods presented in this study for identifying single trees and estimating forest tree structural attributes have high potential for practical applications. [ABSTRACT FROM AUTHOR]

Forests play a crucial role in storing much of the world's carbon (C). Accurately estimating C sequestration is essential for addressing and mitigating the impacts of global warming. While many studies have used machine learning models to estimate carbon storage (CS) in forests based on remote sensing data, this research further examines C sequestration (i.e., the annual carbon uptake by trees; CSE). The objectives of this study are two-fold: firstly, to identify the best models for estimating CSE and CS by testing various methods, and secondly, to examine the effect of climatic data and the canopy height model (CHM) on the estimation of CSE. To achieve the first objective, we will compare the performance of fourteen models, including twelve machine learning models, one deep learning model, and an ensemble model that combines the top four independent models. For the second objective, we study the effect of four input configurations: the first is a baseline configuration based solely on attributes extracted from satellite images (Sentinel-2) and geomorphology; the second combines satellite features with climatic data; the third uses a CHM derived from LiDAR instead of climatic data; and the fourth combines all available features: satellite images, climatic data, and CHM. The results show that adding climatic data does not improve the estimation of CSE and CS. However, adding CHM features significantly improves the models' performance for both targets. The implemented ensemble model demonstrated the best performance across all configurations.

ABSTRACTThe discrepancies in data across different phases and the unexplored optimal spatial resolution present challenges when using multi-temporal canopy height models to accurately discern actual forest growth. In this study, we evaluated the reliability of bi-temporal CHMs to characterize growth changes in a boreal natural forest over a four-year period. A maximum mosaic method was introduced to construct a CHM from various flight strips, aimed at minimizing data alignment errors. Subsequently, the canopy height and height changed derived from six different height percentile metrics and five spatial resolutions were evaluated. The results showed that higher resolution (e.g., < 2 m) and lower height metrics (e.g., 85th height percentile) consistently underestimated. Tree growth correlation with individual segmentation surpassed field-measurements at all resolutions and height metrics. for the optimal resolution and height metrics, the results suggest that using a 95th height percentile the 10 m scale effectively represents both canopy height ([Formula: see text] = 0.95, [Formula: see text] = 0.88 m, [Formula: see text] = 5.83%; [Formula: see text] = 0.96, [Formula: see text] = 1.11 m, [Formula: see text] = 6.91%) and height changes ([Formula: see text] = 0.59, [Formula: see text] = 0.86 m, [Formula: see text] = 18.38%). This study demonstrates the necessity of carefully evaluating data characteristics and resolutions when employing multi-temporal CHM for forest dynamics monitoring.

The forest floor is covered with tree canopy, and the forest canopy gap is a larger or smaller area of the floor that is not covered by the canopy of the dominant tree layer. The size of the gap affects tree species rejuvenation, as well as the vertical and horizontal structure of the forest. In the Pohorje area (Pahernik’s forest estate), which is characterized by forests where trees of different diameters and ages occur in small areas, we analyzed the possibilities of automatic gap detection based on data from the Laser Scanning of Slovenia (LSS) and the Cyclic Aerial Survey of Slovenia (CAS). We used Canopy Height Models (CHM) derived from both data sources. The overall detection accuracy even for the smallest gaps (smaller than 50 m²) was 94.4% based on the LSS data and 83.1% based on the CAS data. A larger number and area of gaps can be detected based on the LSS data, and the gaps derived from the LSS data are more disaggregated and elongated. The detection of gaps with CAS data and LSS data is more comparable with a minimum gap area of 100 m²; the accuracy of detecting such gaps is 85.4 and 94.4% respectively. The results of the study show that in the absence of LSS data, the use of CAS data to detect forest canopy gaps on a large scale is useful when gaps with an area of more than 100 m² are detected. [ABSTRACT FROM AUTHOR]

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]

As a remote sensing technique, unmanned aerial vehicles (UAVs) have great potential in several fields, such as monitoring vegetation in an urban area at low altitudes at a reasonable cost. In this study,we assessed the potential of individual tree detection by the structure of the motion algorithm (SfM) based on UAV images and derived point cloud. Urban broadleaved forests (Fateh garden) were photographed in the spring of 2018 with different structures, a mixed uneven-aged dense stand (MUDS), a mixed uneven-aged sparse stand (MUSS), and a pure even-aged dense stand (PDEs). The results of using the local maxima algorithm for the different structures showed a detection accuracy rate of 0.90, 0.54, and 0.32 for PDES, MUDS, and MUSS, respectively. Based on the results, the accuracy of tree detection is affected by the height of the trees (individuals with a height of fewer than 5 meters were not detected), and the species (Poplar trees were detected better than other species), as well as the searching window size. The fixed tree window size of 3×3 was the best window size, and the fixed smoothing window size was variable for each site. Using mean and Gaussian filters did not noticeably affect the results. In general, our study showed that the canopy height model (CHM) from UAV can detect trees with very high accuracy in urban forests with homogenous even-aged structures, while in uneven-aged stands, the accuracy of tree detection is medium to low. [ABSTRACT FROM AUTHOR]

As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze the sensitivity of the parameter setting to determine the optimal parameters, especially for the Eucalyptus spp. forest, which is one of the most important hardwood plantations in the world. In the study, four methods were employed to segment individual Eucalyptus spp. plantations from normalized point cloud data and canopy height model generated from the original UAV-LiDAR data. And the parameter sensitivity of each segmentation method was analyzed to obtain the optimal parameter setting according to the extraction accuracy. The performance of the segmentation result was assessed by three indices including detection rate, precision, and overall correctness. The results indicated that the watershed algorithm performed better than other methods as the highest overall correctness (F = 0.761) was generated from this method. And the segmentation methods based on the canopy height model performed better than those based on normalized point cloud data. The detection rate and overall correctness of low-density plots were better than high-density plots, while the precision was reversed. Forest structures and individual wood characteristics are important factors influencing the parameter sensitivity. The performance of segmentation was improved by optimizing the key parameters of the different algorithms. With optimal parameters, different segmentation methods can be used for different types of Eucalyptus plots to achieve a satisfying performance. This study can be applied to accurate measurement and monitoring of Eucalyptus plantation. [ABSTRACT FROM AUTHOR]

Forest stands, crucial for inventory, planning, and management, traditionally rely on time-consuming visual analysis by forest managers. To enhance efficiency, there is a growing need for automated methods that take into account essential forest attributes. In response, we propose a novel approach utilizing airborne Light Detection and Ranging (LiDAR) and hyperspectral data for automated forest stand delineation. Our approach initiates with over-segmentation of the Canopy Height Model (CHM), followed by attribute calculation for each segment using both CHM and hyperspectral data. Two rules are applied to merge homogeneous segments and eliminate others based on calculated attributes. The effectiveness of our method was validated using three types of reference forest stands with two indices: the explained variance (R2) and Intersection over Union (IoU). Results from our study demonstrated notable accuracy, with a R2 of 97.35% and 97.86% for mean tree height and mean diameter at breast height (DBH), respectively. The R2 for mean canopy height is 81.80%, outperforming manual delineation by 7.31% and multi-scale segmentation results by 2.13%. Furthermore, our approach achieved high IoU values, which indicates a strong spatial agreement with manually delineated forest stands and leading to fewer manual adjustments when applied directly to forest management. In conclusion, our forest stand delineation method enhances both internal consistency and spatial accuracy. This method contributes to improving practical performance and forest management efficiency.

In uneven-aged forests, trees of different diameters, heights, and ages are located in a small area, which is due to the felling of individual trees or groups of trees, as well as small-scale natural disturbances. In this article, we present an objective method for classifying forest stands into even- and uneven-aged stands based on freely available low-resolution (with an average recording density of 5 points/m2) national lidar data. The canopy closure, dominant height, and canopy height diversity from the canopy height model and the voxels derived from lidar data were used to classify the forest stands. Both approaches for determining forest structural diversity (canopy height diversity—CHDCHM and CHDV) yielded similar results, namely two clusters of even- and uneven-aged stands, although the differences in vertical diversity between even- and uneven-aged stands were greater when using CHM. The first analysis, using CHM for the CHD assessment, estimated the uneven-aged forest area as 49.3%, whereas the second analysis using voxels estimated it as 34.3%. We concluded that in areas with low laser scanner density, CHM analysis is a more appropriate method for assessing forest stand height heterogeneity. The advantage of detecting uneven-aged structures with voxels is that we were able to detect shade-tolerant species of varying age classes beneath a dense canopy of mature, dominant trees. The CHDCHM values were estimated to be 1.83 and 1.86 for uneven-aged forests, whereas they were 1.57 and 1.58 for mature even-aged forests. The CHDV values were estimated as 1.50 and 1.62 for uneven-aged forests, while they were 1.33 and 1.48 for mature even-aged forests. The classification of stands based on lidar data was validated with data from measurements on permanent sample plots. Statistically significantly lower average values of the homogeneity index and higher values of the Shannon–Wiener index from field measurements confirm the success of the classification of stands based on lidar data as uneven-aged forests.

Quantifying the vegetation aboveground biomass (AGB) is crucial for evaluating environment quality and estimating blue carbon in coastal wetlands. In this study, a UAV-LiDAR was first employed to quantify the canopy height model (CHM) of coastal Phragmites australis (common reed). Statistical correlations were explored between two multispectral remote sensing data (Sentinel-2 and JL-1) and reed biophysical parameters (CHM, density, and AGB) estimated from UAV-LiDAR data. Consequently, the reed AGB was separately estimated and mapped with UAV-LiDAR, Sentinel-2, and JL-1 data through the allometric equations (AEs). Results show that UAV-LiDAR-derived CHM at pixel size of 4 m agrees well with the observed stem height (R2 = 0.69). Reed height positively correlates with the basal diameter and negatively correlates with plant density. The optimal AGB inversion model was derived from Sentinel-2 data and JL-1 data with R2 = 0.58, RMSE = 216.86 g/m2 and R2 = 0.50, RMSE = 244.96 g/m2, respectively. This study illustrated that the synergy of UAV-LiDAR data and multispectral remote sensing images has great potential in coastal reed monitoring.

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 Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling. We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR). The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important. The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community.

The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops' yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions. [ABSTRACT FROM AUTHOR]

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.

Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. However, methods of data fusion of different factors still need to be explored in order to enhance the accuracy of their estimates. Therefore, the objective of this study was to investigate the combined metrics of different variables (spectral, structural and meteorological factors) for AGB estimation of wheat using UAS multispectral data. UAS images were captured on two selected growing dates at a typical reclaimed cropland in the North China Plain. The spectral response was determined using the highly correlated vegetation index (VI). A structural metric, the canopy height model (CHM), was produced using UAS-based multispectral images. The measure of growing degree days (GDD) was selected as a meteorological proxy. Subsequently, a structurally–meteorologically weighted canopy spectral response metric (SM-CSRM) was derived by the pixel-level fusion of CHM, GDD and VI. Both correlation coefficient analysis and simple function fitting were implemented to explore the highest correlation between the measured AGB and each proposed metric. The optimal regression model was built for AGB prediction using leave-one-out cross-validation. The results showed that the proposed SM-CSRM generally improved the correlation between wheat AGB and various VIs and can be used for estimating the wheat AGB. Specifically, the combination of MERIS terrestrial chlorophyll index (MTCI), vegetation-masked CHM (mCHM) and normalized GDD (nGDD) achieved an optimal accuracy (R2 = 0.8069, RMSE = 0.1667 kg/m2, nRMSE = 19.62%) through the polynomial regression method. This improved the nRMSE by 3.44% compared to the predictor using MTCI × mCHM. Moreover, the pixel-level fusion method slightly enhanced the nRMSE by ~0.3% for predicted accuracy compared to the feature-level fusion method. In conclusion, this paper demonstrated that an SM-CSRM using pixel-level fusion with canopy spectral, structural and meteorological factors can obtain a good level of accuracy for wheat biomass prediction. This finding could benefit the assessment of reclaimed cropland or the monitoring of crop growth and field management in precision agriculture. [ABSTRACT FROM AUTHOR]

The estimation of forestry parameters is essential to understanding the three-dimensional structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but few have paid attention to the effects of the canopy structure. Canopy density plays an important role since it provides information about the vertical distribution of dominant scatterers in the forest. In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in Germany by applying a canopy density index from high-resolution airborne laser scanning data. Two different methods defining the densest layer are introduced and compared with the position of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and +1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings highlight the importance of considering not only tree height but also canopy density when delineating SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and incorporating canopy density can improve the accuracy of SAR-derived forest height estimations. [ABSTRACT FROM AUTHOR]

Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling.We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR).The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important.The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community. [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]

Key message: Despite showing a cost-effective potential for quantifying vertical forest structure, the GEDI and ICESat-2 satellite LiDAR missions fall short of the data accuracy standards required by tree- and stand-level forest inventories. Tree and stand heights are key inventory variables in forestry, but measuring them manually is time-consuming for large forestlands. For that reason, researchers have traditionally used terrestrial and aerial remote sensing systems to retrieve forest height information. Recent developments in sensor technology have made it possible for spaceborne LiDAR systems to collect height data. However, there is still a knowledge gap regarding the utility and reliability of these data in varying forest structures. The present study aims to assess the accuracies of dominant stand heights retrieved by GEDI and ICESat-2 satellites. To that end, we used stand-type maps and field-measured inventory data from forest management plans as references. Additionally, we developed convolutional neural network (CNN) models to improve the data accuracy of raw LiDAR metrics. The results showed that GEDI generally underestimated dominant heights (RMSE = 3.06 m, %RMSE = 21.80%), whereas ICESat-2 overestimated them (RMSE = 4.02 m, %RMSE = 30.76%). Accuracy decreased further as the slope increased, particularly for ICESat-2 data. Nonetheless, using CNN models, we improved estimation accuracies to some extent (%RMSEs = 20.12% and 19.75% for GEDI and ICESat-2). In terms of forest structure, GEDI performed better in fully-covered stands than in sparsely-covered forests. This is attributable to the smaller height differences between canopy tops in dense forest conditions. ICESat-2, on the other hand, performed better in thin forests (DBH < 20 cm) than in large-girth and mature stands of Crimean pine. We conclude that GEDI and ICESat-2 missions, particularly in hilly landscapes, rarely achieve the standards needed in stand-level forest inventories when used alone. [ABSTRACT FROM AUTHOR]

A wide range of UAV systems used for forest research requires unified approaches to data collection. The research aims to determine the optimal parameters for UAV data collection to obtain accurate information about stands, considering the cost of resources for its collection. The process of collecting remote sensing data consisted of nine combinations divided into three levels of overlap and three levels of spatial resolution (survey altitude) and changing the degree of filtering of a dense point cloud during image processing. Individual tree detectingin the stand was performed using the R programming language and the ForestTools package. The results of the assessment of the dependence of the radius of tree crowns on their height were used to set the parameters of the variable filter function for finding local maxima for Scots pine stands. Errors in the identification of treetops were estimated using the F-score. The identified heights were compared with the field data of the ground survey. The proportion of classified digital elevation model DEM in the dense point cloud was reduced from the total area of the test site using images of 4.1 cm/pix spatial resolution (150 m survey altitude). The study presents the results of assessing the impact of spatial resolution of optical images collected from UAVs and their overlap on the results of measurements of stands parameters. It is determined that a photogrammetric survey with input images with a longitudinal overlap of less than 90% is not appropriate for the study of forest areas due to the impossibility of aligning all images. The results of the assessment of tree accounting in the stand showed that it is most appropriate to use images with a spatial resolution of up to 3.3 cm/pix (120 m survey altitude), otherwise, the proportion of missed treetops increases. Reducing the spatial resolution of remote sensing data leads to an increase in errors in determining the height of individual trees, and the average heights of the experimental plots had the same trend. Given the combination of the assessed factors, it is not recommended to use images with a spatial resolution of more than 3.3 cm/pix for forestry research due to increased errors in the individual tree detection and tree height determination. The results obtained can be used to select data collection parameters for research on Scots pine stands to assess their growing stock and phytomass

Leaf Area of Index (LAI) refers to half of the total leaf area of all crops per unit area. It is an important index to represent the photosynthetic capacity and biomass of crops. To obtain LAI conditions of summer maize in different growth stages quickly and accurately, further guiding field fertilization and irrigation. The Unmanned aerial vehicles (UAV) multispectral data, growing degree days, and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion. The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands. The correlation analysis was conducted to verify the accuracy of the multispectral data. To include many bands as possible, four vegetation indices which included R, G, B, and NIR bands were selected in this study to test the spectral accuracy. There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band. Through correlation analysis of LAI and the vegetation index, vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model. In addition, the Canopy Height Model (CHM) and Growing degree days (GDD) of summer maize were also calculated to build the LAI inversion model. The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model (GLR), Multivariate nonlinear regression model (MNR), and the partial least squares regression (PLSR) models. R² and RMSE were used to assess the accuracy of the model. The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64, which was significant. The Wide Dynamic Range Vegetation Index (WDRVI), Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Plant Biochemical Index (PBI), Optimized Soil-Adjusted Vegetation Index (OSAVI), CHM and GDD have a higher correlation with LAI. By comparing the models constructed by the three methods, it was found that the PLSR has the best inversion effect. It was based on OSAVI, GDD, RVI, PBI, CHM, NDVI, and WDRVI, with the training model’s R² being 0.8663, the testing model’s R² being 0.7102, RMSE was 1.1755. This study showed that the LAI inversion model based on UAV multispectral vegetation index, GDD, and CHM improves the accuracy of LAI inversion effectively. That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly, and this method can provide decision support for maize growth monitoring and field fertilization. [ABSTRACT FROM AUTHOR]

Fine-grained information on the level of individual trees constitute key components for forest observation enabling forest management practices tackling the effects of climate change and the loss of biodiversity in forest ecosystems. Such information on individual tree crowns (ITC's) can be derived from the application of ITC segmentation approaches, which utilize remotely sensed data. However, many ITC segmentation approaches require prior knowledge about forest characteristics, which is difficult to obtain for parameterization. This can be avoided by the adoption of data-driven, automated workflows based on convolutional neural networks (CNN). To contribute to the advancements of efficient ITC segmentation approaches, we present a novel ITC segmentation approach based on the YOLOv5 CNN. We analyzed the performance of this approach on a comprehensive international unmanned aerial laser scanning (UAV-LS) dataset (ForInstance), which covers a wide range of forest types. The ForInstance dataset consists of 4192 individually annotated trees in high-density point clouds with point densities ranging from 498 to 9529 points m-2 collected across 80 sites. The original dataset was split into 70% for training and validation and 30% for model performance assessment (test data). For the best performing model, we observed a F1-score of 0.74 for ITC segmentation and a tree detection rate (DET %) of 64% in the test data. This model outperformed an ITC segmentation approach, which requires prior knowledge about forest characteristics, by 41% and 33% for F1-score and DET %, respectively. Furthermore, we tested the effects of reduced point densities (498, 50 and 10 points per m-2) on ITC segmentation performance. The YOLO model exhibited promising F1-scores of 0.69 and 0.62 even at point densities of 50 and 10 points m-2, respectively, which were between 27% and 34% better than the ITC approach that requires prior knowledge.Furthermore, the areas of ITC segments resulting from the application of the best performing YOLO model were close to the reference areas (RMSE = 3.19 m-2), suggesting that the YOLO-derived ITC segments can be used to derive information on ITC level.

Canopy gap delineation is essential for achieving a better comprehension of forest structure. This study aims to (a) extract canopy gaps using UAV data and (b) compare the performance of different canopy gap extraction methods in a managed stand in the northeast of Iran. A canopy height model (CHM) was produced by subtracting LIDAR digital terrain model from the UAV digital surface model. CHM classification performs to extract gaps by thresholding CHM (fixed height and CHM slope and relative height thresholds) and object-based classification on the UAV CHM and orthophoto. Ground truth is produced in the point and polygon forms through field measurements and visual interpretation of the UAV orthophoto. The geometry of the canopy gaps (Area, perimeter, and shape complexity) was calculated. Finally, the point and polygon base accuracy of delineated gaps assess for each of the methods. Point accuracy assessment suggests that 60% CHM slope produces the highest overall accuracy and Kappa coefficient of 91.7% and 0.874, respectively. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using relative height and 60% CHM slope thresholds. The lowest mean errors of GSCI produced by 70% CHM slope (0.15). Moreover, object-based classification showed the lowest mean error of area (33.76 m2) and perimeter (16.80 m). In conclusion, while area accuracy is considered the best fit of the delineated gap's geometry is gained by the object-based classification.

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]

A wide range of UAV systems used for forest research requires unified approaches to data collection. The research aims to determine the optimal parameters for UAV data collection to obtain accurate information about stands, considering the cost of resources for its collection. The process of collecting remote sensing data consisted of nine combinations divided into three levels of overlap and three levels of spatial resolution (survey altitude) and changing the degree of filtering of a dense point cloud during image processing. Individual tree detectingin the stand was performed using the R programming language and the ForestTools package. The results of the assessment of the dependence of the radius of tree crowns on their height were used to set the parameters of the variable filter function for finding local maxima for Scots pine stands. Errors in the identification of treetops were estimated using the F-score. The identified heights were compared with the field data of the ground survey. The proportion of classified digital elevation model DEM in the dense point cloud was reduced from the total area of the test site using images of 4.1 cm/pix spatial resolution (150 m survey altitude). The study presents the results of assessing the impact of spatial resolution of optical images collected from UAVs and their overlap on the results of measurements of stands parameters. It is determined that a photogrammetric survey with input images with a longitudinal overlap of less than 90% is not appropriate for the study of forest areas due to the impossibility of aligning all images. The results of the assessment of tree accounting in the stand showed that it is most appropriate to use images with a spatial resolution of up to 3.3 cm/pix (120 m survey altitude), otherwise, the proportion of missed treetops increases. Reducing the spatial resolution of remote sensing data leads to an increase in errors in determining the height of individual trees, and the average heights of the experimental plots had the same trend. Given the combination of the assessed factors, it is not recommended to use images with a spatial resolution of more than 3.3 cm/pix for forestry research due to increased errors in the individual tree detection and tree height determination. The results obtained can be used to select data collection parameters for research on Scots pine stands to assess their growing stock and phytomass. [ABSTRACT FROM AUTHOR]

Forests play a crucial role in storing much of the world's carbon (C). Accurately estimating C sequestration is essential for addressing and mitigating the impacts of global warming. While many studies have used machine learning models to estimate carbon storage (CS) in forests based on remote sensing data, this research further examines C sequestration (i.e., the annual carbon uptake by trees; CSE). The objectives of this study are two-fold: firstly, to identify the best models for estimating CSE and CS by testing various methods, and secondly, to examine the effect of climatic data and the canopy height model (CHM) on the estimation of CSE. To achieve the first objective, we will compare the performance of fourteen models, including twelve machine learning models, one deep learning model, and an ensemble model that combines the top four independent models. For the second objective, we study the effect of four input configurations: the first is a baseline configuration based solely on attributes extracted from satellite images (Sentinel-2) and geomorphology; the second combines satellite features with climatic data; the third uses a CHM derived from LiDAR instead of climatic data; and the fourth combines all available features: satellite images, climatic data, and CHM. The results show that adding climatic data does not improve the estimation of CSE and CS. However, adding CHM features significantly improves the models' performance for both targets. The implemented ensemble model demonstrated the best performance across all configurations. [Display omitted] • New ensemble model outperforms 13 models in carbon sequestration & storage estimation. • Canopy height enhances carbon sequestration & storage estimation. • Climatic data doesn't improve the accuracy of estimation. [ABSTRACT FROM AUTHOR]

Aboveground biomass removal and canopy opening by selective logging modifies soil moisture in the main root zone, impacting soil aeration and various biogeochemical processes in tropical production forests. This study investigated the relationship between canopy damage and topsoil (10 cm) moisture in two logged forests in Malaysian Borneo, while simultaneously controlling for logging intensity, time elapsed since historical logging, and spatial autocorrelation. Volumetric soil water content (VSWC), canopy height model (CHM), leaf area index (LAI), and historical logging data were collected from 84 transects placed subjectively in 15 sites exhibiting varying canopies. We generated an index (PC1) quantifying the magnitude of canopy structural degradation from canopy structure metrics (CSM) combining CHM and LAI data within a 20-meter buffer for each transect. PC1 was analyzed for its impact on VSWC across logging periods, and contrasted with topography. Spatial autocorrelation of VSWC was examined regarding to canopy conditions. VSWC was significantly higher in all logged forests (over 0.4 m3 m−3) comparing to non-disturbed forests (0.27 m3 m−3). The immediate wetting could be a result of extracting mature individuals of late-successional species holding large biomass, while the persistent wet condition may be due to retarded canopy and biomass recovery. In the study area, canopy structure was a stronger predictor of soil moisture than topography. The high soil moisture underneath the most degraded canopies presented the largest spatial extent of autocorrelation. This study revealed soil wetting after selective logging in humid tropical forests, driven by reduced transpiration from biomass loss rather than increased evaporative demand resulting from canopy opening. The elevation in soil moisture could have disrupted biogeochemical processes in the below-ground system, which in turn impede forest succession and put stress on the overall vulnerability of disturbed tropical rainforests. • Soil moisture increased in logged forests, irrespective of logging intensity and time elapsed from logging. • Soil wetting is driven by weakened transpiration, which offsets the effect from enhanced soil evaporation. • The expansion of soil wetting potentially affects below-ground biogeochemical processes in surrounding remnant forests. [ABSTRACT FROM AUTHOR]

The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops’ yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions.

Abstract Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe.

BackgroundAccurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.ResultsOur results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m-2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R2 ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha-1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha-1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R2 ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha-1 [between 0.69-0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha-1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R2 was between 0.58-0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha-1 for the echo-based model, whereas for the CHM R2 was between 0.37-0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha-1.ConclusionsMetrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m-2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m-2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m-2.

Oil palms are currently in high demand, which tend to increase even higher as a source of alternative energy for humans, especially in Southeast Asian countries. This leads to the study that focuses on the height measurement, using an unmanned aerial vehicle (UAV), and age analysis of oil palm trees planted within the experimental plots in order to predict their yield. The methodology described in the paper provides using Canopy Height Model (CHM) for height measurement and prediction of the oil palm yield by multiple linear regression. The results indicated that the errors caused by overlapping age ranges were found in 3 out of 12 experimental plots. Furthermore, the primary factors influencing the oil palm yield prediction included the age (9 years and above) and canopy density (over 41% of the area), while the secondary factors supporting more accuracy included the total plot area, canopy area, and canopy height, with the coefficient of determination or R-squared at 0.98. In this study, we learned that the aforementioned factors could be concluded from the data collected by an UAV, which reduced the time for measuring the height of each tree manually, resulting in more accurate yield prediction. [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]

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]

Forest inventories require up-to-date data on dominant tree height and stand top height from forest sample plots. These data are used to characterize the vertical structure of forests, providing a baseline for volume and yield tables as well as many other biomass studies. Obtaining height information through ground measurement is laborious, costly, and time-consuming. The aim of this study is to estimate stand top heights of the Artvin-Hatila Valley's forests using freely available laser scanning (LiDAR) data from the ICESat-2 satellite for the first time in Turkey. For this purpose, the dominant tree heights, traditionally measured by digital hypsometer in 52 sample plots, were evaluated by stand types and compared with the ICESat-2 canopy data. Then, two data sets were modeled using the Convolutional Neural Network (CNN) and simple regression methods. The model accuracies were evaluated with correlation (Pearson's R), coefficient of determination (R2), and root mean squared error (RMSE) using ground-based data. The results showed that the CNN-based model performed better than the linear regression model in height estimation. Its R, R2, and RMSE values were .82, .68, and 4.2 m, respectively. As for stand types, broadleaves-dominated, mature, and fully covered stands seem more appropriate for top height modeling with spaceborne LiDAR data. Degraded, coniferous, and young stands, as well as non-forest areas, barely allow accurate top height estimations due to their complex canopy surfaces and small openings among trees. Given the promising results, we conclude that satellite-based LiDAR systems provide opportunities to forest professionals as a free auxiliary data source for operational forest management in Turkey. [ABSTRACT FROM AUTHOR]