Digital Aerial Photogrammetry Research Articles

Integrating personal laser scanning with uncrewed aerial vehicle-based photogrammetry or laser scanning for accurate stem volume estimation in temperate coniferous forests

ABSTRACT An extensive number of trees are surveyed for logging purposes, necessitating efficient and precise logging survey methods. Integrating over- and under-canopy remote sensing, such as personal laser scanning (PLS) with uncrewed aerial vehicle-based laser scanning (ULS) or digital aerial photogrammetry (DAP), is a promising method. In this study, we aimed to estimate stem volume in a 16.89-ha coniferous forest using these techniques at the individual, plot, and stand levels. The root mean square error (RMSE) and relative RMSE (rRMSE) when compared to conventional field surveys of PLS-based volume estimations were 0.14 m3 (21.6%) before and 0.08 m3 (12.3%) after bias correction. When integrating PLS with ULS and DAP, the RMSE and rRMSE values were 0.16 m3 (26.7%) and 0.16 m3 (27.0%), respectively. The F-score for the individual tree detection analysis by ULS and DAP were 0.94 and 0.96, respectively. The RMSE of the estimated volume in the plots was 0.08 m3 for both ULS and DAP. The estimated volumes of timber produced in the stand using the utilization rate overestimated the actual volume by 30.6%, 13.0%, and 17.0%, for PLS, ULS, and DAP, respectively. While the individual stem volume estimation accuracy of ULS and DAP was lower than that of the field survey, at the plot level the results were comparable, with ULS and DAP showing results significantly closer to the actual results than those of PLS at the stand level. This study highlights the potential for integrating PLS with uncrewed aerial vehicle methods for estimating stem and log volumes at the plot and stand levels.

Massive sediment pulses triggered by a multi-stage 130 000 m3 alpine cliff fall (Hochvogel, DE–AT)

Abstract. Massive sediment pulses in catchments are a key alpine multi-risk component. Substantial sediment redistribution in alpine catchments frequently causes flooding, river erosion, and landsliding and affects infrastructure such as dam reservoirs as well as aquatic ecosystems and water quality. While systematic rock slope failure inventories have been collected in several countries, the subsequent cascading sediment redistribution is virtually unaccessed. For the first time, this contribution reports the massive sediment redistribution triggered by the multi-stage failure of more than 130 000 m3 from the Hochvogel dolomite peak during the summer of 2016. We applied change detection techniques to seven 3D-coregistered high-resolution true orthophotos and digital surface models (DSMs) obtained through digital aerial photogrammetry later optimized for precise volume calculation in steep terrain. The analysis of seismic information from surrounding stations revealed the temporal evolution of the cliff fall. We identified the proportional contribution of > 600 rockfall events (> 1 m3) from four rock slope catchments with different slope aspects and their volume estimates. In a sediment cascade approach, we evaluated erosion, transport, and deposition from the rock face to the upper channelized erosive debris flow channel, then to the widened dispersive debris flow channel, and finally to the outlet into the braided sediment-supercharged Jochbach river. We observe the decadal flux of more than 400 000 m3 of sediment, characterized by massive sediment waves that (i) exhibit reaction times of 0–4 years in response to a cliff fall sediment input and relaxation times beyond 10 years. The sediment waves (ii) manifest with faster response times of 0–2 years in the upper catchment and over 2 years in the lower catchments. The entire catchment (iii) undergoes a rapid shift from sedimentary (102–103 mm a−1) to massive erosive regimes (102 mm a−1) within single years, and the massive sediment redistribution (iv) shows limited dependency on rainfall frequency and intensity. This study provides generic information on spatial and temporal patterns of massive sediment pulses in highly sediment-charged alpine catchments.

Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring

Detailed maps of vegetation composition are vital for restoration planning, implementation, and monitoring, particularly at early stages of succession. This is usually accomplished through ground surveys, which can be costly and impractical depending on extent and accessibility, or conducted at too broad a spatial scale. In this study, we propose a methodology for mapping regenerating vegetation composition at 2 × 2 m2 spatial resolution, using very high spatial resolution (<1 m) remote sensing imagery obtained from remotely piloted aerial systems (RPAS) in conjunction with digital aerial photogrammetry (DAP) techniques for reconstructing vegetation structure. We applied logistic regression on multispectral orthomosaics, clusters of vegetation structure, and local illumination estimates to develop presence-absence models for eight key plant groups at various taxonomic levels as well as six plant functional types (conifer tree seedlings, grasses, tall- and low-growing forbs, shrubs, and mosses). Our results show higher accuracies for plant functional types (mean F-score = 0.67) compared to lower taxonomic levels (0.57). Notably, shrubs (F-score = 0.79), low-growing forbs (0.70), and mosses (0.69) exhibited the highest accuracies, while grasses (0.46), the aster family (Asteraceae spp; 0.48), and spruce seedlings (Picea spp; 0.54) demonstrated lower accuracies. Vegetation structure variables were identified as the most influential in the models, with mean NIRv ranking highest among spectral variables. High average ranks of spectral variation metrics (e.g., standard deviation of NIRv) implied the influence of environmental determinants such as plant co-occurrences and micro-habitat conditions, which drive spectral variation. Discrete composition maps were produced for three restoration sites and analogous wildfire-disturbed sites. Plant compositions found at one site pair exhibited similarity (Bray-Curtis = 0.28), however, certain key plant groups covered larger extents of the restoration site than anticipated. Willows (Salix spp; 25.4% vs. 9.3%), which are typically planted for soil stabilization and obstruction, and clovers (Trifolium spp; 11.1% vs. 3.6%), which represent non-native agronomic vegetation, were prominent. The developed methodology facilitates the generation of detailed plant composition maps, aiding evaluations of vegetation patterns that are difficult to discern visually or through conventional field sampling. This approach can effectively help assess restoration goals and guide adaptive management strategies, especially when incorporating the expertise of restoration ecologists in understanding how different vegetation types affect habitat quality.

Towards a high-resolution modelling scheme for local-scale urban flood risk assessment based on digital aerial photogrammetry

With the rapid development of cities and the impact of climate change, cities located near rivers are facing an increasingly serious flood threat. Urban flood risk prediction management is pressing. The peak discharge and duration are important factors in urban flood management as well as the important characteristics of flood hygrograph. Mostly, hydrological model is used to obtain the upstream flood hygrograph to drive inundation model. However, lack of information on reservoirs, barrage structures and land use make it difficult to construct high-precision hydrological models, especially in upstream cities where data is lacking. In this study, therefore, we propose an approach to urban flood management based on measured flood data from urban hydrological stations in close proximity to derive flood hydrography for different return periods, and establish a high-precision urban-scale river flood risk management method by combining with Unmanned Aerial Vehicle (UAV) survey data. This method can allow information on river construction and underlying surface change to be introduced into the flood hygrograph implicitly, thus avoiding the difficulty of establishing hydrological and hydrodynamic coupling of the whole basin. The applicability and accuracy of the methodology are explored in this article with reference to Chenxi City in the upper reaches of Yuanshui River. Comparison the flood process of the 10-year with the actual flood in 2016 indicates that the extent of inundation is strongly dependent on the instantaneous discharge, with the peak flow largely determining the maximum inundation extent and risk level. This illustrates the feasibility of risk assessment of urban flooding based on flood hygrograph with different period levels derived from measured flood processes at adjacent sites. Subsequently, the different return periods scenario is simulated and analyzed. This approach provides technical guidance for flood risk assessment in areas where data is lacking (e.g., upstream mountainous cities).

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 1: Methodology and comparison in vineyards

Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions..

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 2: Comparison for different crops and training systems

The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping.

Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference

Due to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such as tree height, volume, basal area, and aboveground biomass (AGB) in various forest types. Model-based inference is found to be efficient for the estimation of forest attributes using auxiliary RS data, and this study focused on testing model-based estimations of AGB in the treeline ecotone using an area-based approach. Shrubs (Salix spp., Betula nana) and trees (Betula pubescens ssp. czerepanovii, Sorbus aucuparia, Populus tremula, Pinus sylvestris, Picea abies) with heights up to about five meters constituted the AGB components. The study was carried out in a treeline ecotone in Hol, southern Norway, using field plots and point cloud data obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). The field data were acquired for two different strata: tall and short vegetation. Two separate models for predicting the AGB were constructed for each stratum based on metrics calculated from ALS and DAP point clouds, respectively. From the stratified predictions, mean AGB was estimated for the entire study area. Despite the prediction models showing a weak fit, as indicated by their R2-values, the 95% CIs were relatively narrow, indicating adequate precision of the AGB estimates. No significant difference was found between the mean AGB estimates for the ALS and DAP models for either of the strata. Our results imply that RS data from ALS and DAP can be used for the estimation of AGB in treeline ecotones.

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca).

Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2 = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2 = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.

UAV-acquired imagery with photogrammetry provides accurate measures of mudflat elevation gradients and microtopography for investigating microphytobenthos patterning

Intertidal mudflats are highly productive ecosystems where elevation gradients and complex microtopography drive the growth of benthic microalgae (microphytobenthos; MPB) that form the basis of estuarine foodwebs and are crucial for nutrient cycling, shoreline stabilization, and the persistence of marine and coastal species. Mudflat ecosystems are threatened by human activity and natural stressors and thus need to be mapped and monitored. Unoccupied aerial vehicle (UAV) technologies and digital aerial photogrammetry (DAP) have been successfully implemented to study mudflat environments. However, standardizing the UAV flight parameters needed for optimal DAP performance on mudflats remains outstanding. Here, we systematically determined the optimal flight parameters for collection and photogrammetric processing of UAV-acquired data on mudflats by (1) testing across-track overlap (50, 70, and 90%) and flight elevation (73 m and 110 m) parameters, assessing the accuracy of DAP results against reference data from a mobile laser scanner (MLS), and (2) comparing semi-variograms of digital surface models (DSMs) from two UAV flight elevations. We found that all combinations of UAV flight parameters yielded accurate DAP products; flight elevation had a marginal effect on image alignment and had no effect on accuracy, while across-track overlap had no effect on image alignment of DSM of difference (DoD) values. All UAV and MLS point clouds were aligned with and accuracy of < 0.016 m and absolute values of mean DoDs were all sub-millimeter, ranging from 0.0001 ± 0.0322 to 0.0083 ± 0.0270 m. We conclude that conducting UAV surveys at 110 m elevation with 50% across-track image overlap is sufficient for high-accuracy DAP in mudflats. Finally, we tested the utility of such fine-scale topographic data for ecological applications by comparing elevation and topographic position indices (TPI) of DAP-derived DSMs to MPB abundance, measured as chlorophyll a (chl-a), calculated from UAV-acquired NDVI data. We found that elevation and TPI account for 1.6–17% of the variation in chl-a concentration, and that these relationships depend on distance from shore and mudflat morphology. Our findings contribute to standardizing the application of UAV technologies in mudflats and demonstrate the potential of UAV-acquired data for modeling the relationship between microtopography and MPB on ecologically important mudflats.

A Comparative Assessment of Multi-Source Generation of Digital Elevation Models for Fluvial Landscapes Characterization and Monitoring

Imaging and measuring the Earth’s relief with sensors mounted upon unmanned aerial vehicles is an increasingly frequently used and promising method of remote sensing. In the context of fluvial geomorphology and its applications, e.g., landform mapping or flood modelling, the reliable representation of the land surface on digital elevation models is crucial. The main objective of the study was to assess and compare the accuracy of state-of-the-art remote sensing technologies in generating DEMs for riverscape characterization and fluvial monitoring applications. In particular, we were interested in DAP and LiDAR techniques comparison, and UAV applicability. We carried out field surveys, i.e., GNSS-RTK measurements, UAV and aircraft flights, on islands and sandbars within a nature reserve on a braided section of the Vistula River downstream from the city of Warsaw, Poland. We then processed the data into DSMs and DTMs based on four sources: ULS (laser scanning from UAV), UAV-DAP (digital aerial photogrammetry), ALS (airborne laser scanning), and satellite Pléiades imagery processed with DAP. The magnitudes of errors are represented by the cross-reference of values generated on DEMs with GNSS-RTK measurements. Results are presented for exposed sediment bars, riverine islands covered by low vegetation and shrubs, or covered by riparian forest. While the average absolute height error of the laser scanning DTMs oscillates around 8–11 cm for most surfaces, photogrammetric DTMs from UAV and satellite data gave errors averaging more than 30 cm. Airborne and UAV LiDAR measurements brought almost the perfect match. We showed that the UAV-based LiDAR sensors prove to be useful for geomorphological mapping, especially for geomorphic analysis of the river channel at a large scale, because they reach similar accuracies to ALS and better than DAP-based image processing.

Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing

Canopy height (CH) is an important trait for crop breeding and production. The rapid development of 3D sensing technologies shed new light on high-throughput height measurement. However, a systematic comparison of the accuracy and heritability of different 3D sensing technologies is seriously lacking. Moreover, it is questionable whether the field-measured height is as reliable as believed. This study uncovered these issues by comparing traditional height measurement with four advanced 3D sensing technologies, including terrestrial laser scanning (TLS), backpack laser scanning (BLS), gantry laser scanning (GLS), and digital aerial photogrammetry (DAP). A total of 1920 plots covering 120 varieties were selected for comparison. Cross-comparisons of different data sources were performed to evaluate their performances in CH estimation concerning different CH, leaf area index (LAI), and growth stage (GS) groups. Results showed that 1) All 3D sensing data sources had high correlations with field measurement (r > 0.82), while the correlations between different 3D sensing data sources were even better (r > 0.87). 2) The prediction accuracy between different data sources decreased in subgroups of CH, LAI, and GS. 3) Canopy height showed high heritability from all datasets, and 3D sensing datasets had even higher heritability (H2 = 0.79–0.89) than FM (field measurement) (H2 = 0.77). Finally, outliers of different datasets are analyzed. The results provide novel insights into different methods for canopy height measurement that may ensure the high-quality application of this important trait.

A digital and non-destructive integrated methodology for heritage modelling and deterioration mapping. The case study of the Moorish Castle in Sintra

The development of non-destructive, non-contact and autonomous unmanned techniques is extremely important for the preservation of heritage integrity. This paper presents a digital integrated methodology combining Terrestrial Laser Scanning (TLS), Aerial Digital Photogrammetry (ADP) and Infrared Thermography (IRT), which accelerate data collection and facilitate the creation of point clouds, the deterioration mapping and the state of conservation monitoring of historic structures. Both Digital Photogrammetry and Infrared Thermography were coupled with unmanned aerial vehicle (UAV). The proposed methodology was applied to the Moorish Castle in Sintra, Portugal. Manual Infrared Thermography, complemented with other traditional non-destructive techniques, were also applied in a pilot area for comparison purposes. Resultsdemonstrate the high potential of the integration of TLS, ADP and IRT techniques for heritage modelling digitalization, inspection and deterioration mapping with a positive impact on conservation and restoration projects.

How to Systematically Evaluate the Greenspace Exposure of Residential Communities? A 3-D Novel Perspective Using UAV Photogrammetry

The quantity and quality of green space (GS) exposure play an important role in urban residents’ physical and psychological health. However, the current framework for assessing GS quality is primarily based on 2-D remote sensing data and 2.5-D street-view images. Few studies have comprehensively evaluated residential community GSs from an overall 3-D perspective. This study proposes a novel systematic framework for evaluating the quantity and quality of residential GSs based on the generation of a high-resolution 3-D point cloud using Unmanned Aerial Vehicle (UAV)-digital aerial photogrammetry (DAP). Nine indices were proposed: green volume ratio, floor green volume index, green groups diversity index, vegetation diversity index, greenspace fragmentation, average vegetation colour distance, vegetation colour diversity, activity areas ratio, and green cohesion index of activity site. These metrics were calculated using the classified point clouds from four typical Chinese residential communities with different residential greenery types and population densities. The results showed that our method could quantitatively identify the differences in residential GS exposure within urban residential communities. For example, a residential community with a large plant distribution and rich greenery variations had higher greenspace volume ratio and vegetation diversity index values. Our findings suggest that this novel framework, employing cost-effective UAV-DAP, can clearly describe different GS attributes and characteristics, aiding decision-makers and urban planners in comprehensively implementing GS interventions to improve the residents’ quality of life.

Superior Clone Selection in a Eucalyptus Trial Using Forest Phenotyping Technology via UAV-Based DAP Point Clouds and Multispectral Images

The quantitative, accurate and efficient acquisition of tree phenotypes is the basis for forest “gene-phenotype-environment” studies. It also offers significant support for clarifying the genetic control mechanisms of tree traits. The application of unmanned aerial vehicle (UAV) remote sensing technology to the collection of phenotypic traits at an individual tree level quantitatively analyses tree phenology and directionally evaluates tree growth, as well as accelerating the process of forest genetics and breeding. In this study, with the help of high-resolution, high-overlap, multispectral images obtained by an UAV, combined with digital elevation models (DEMs) extracted from point clouds acquired by a backpack LiDAR, a high-throughput tree structure and spectral phenotypic traits extraction and a genetic selection were conducted in a trial of Eucalyptus clones in the State-owned Dongmen Forest Farm in the Guangxi Zhuang Autonomous Region. Firstly, we validated the accuracy of extracting the phenotypic parameters of individual tree growth based on aerial stereo photogrammetry point clouds. Secondly, on this basis, the repeatability of the tree growth traits and vegetation indices (VIs), the genetic correlation coefficients between the traits were calculated. Finally, the eucalypt clones were ranked by integrating a selection index of traits, and the superior genotypes were selected and their genetic gain predicted. The results showed a high accuracy of the tree height (H) extracted from the digital aerial photogrammetry (DAP) point cloud based on UAV images (R2 = 0.91, and RMSE = 0.56 m), and the accuracy of estimating the diameter at breast height (DBH) was R2 = 0.71, and RMSE = 0.75 cm. All the extracted traits were significantly different within the tree species and among the clones. Except for the crown width (CW), the clonal repeatability (Rc) of the traits were all above 0.9, and the individual repeatability values (Ri) were all above 0.5. The genetic correlation coefficient between the tree growth traits and VIs fluctuated from 0.3 to 0.5, while the best clones were EA14-15, EA14-09, EC184, and EC183 when the selection proportion was 10%. The purpose of this study was to construct a technical framework for phenotypic traits extraction and genetic analysis of trees based on unmanned aerial stereo photography point clouds and high-resolution multispectral images, while also exploring the application potential of this approach in the selective breeding of eucalypt clones.

Mapping of the Successional Stage of a Secondary Forest Using Point Clouds Derived from UAV Photogrammetry

The definition of strategies for forest restoration projects depends on information of the successional stage of the area to be restored. Usually, classification of the successional stage is carried out in the field using forest inventory campaigns. However, these campaigns are costly, time-consuming, and limited in terms of spatial coverage. Currently, forest inventories are being improved using 3D data obtained from remote sensing. The objective of this work was to estimate several parameters of interest for the classification of the successional stages of secondary vegetation areas using 3D digital aerial photogrammetry (DAP) data obtained from unmanned aerial vehicles (UAVs). A cost analysis was also carried out considering the costs of equipment and data collection, processing, and analysis. The study was carried out in southeastern Brazil in areas covered by secondary Atlantic Forest. Regression models were fit to estimate total height (h), diameter at breast height (dbh), and basal area (ba) of trees in 40 field inventory plots (0.09 ha each). The models were fit using traditional metrics based on heights derived from DAP and a portable laser scanner (PLS). The prediction models based on DAP data yielded a performance similar to models fit with LiDAR, with values of R² ranging from 88.3% to 94.0% and RMSE between 11.1% and 28.5%. Successional stage maps produced by DAP were compatible with the successional classes estimated in the 40 field plots. The results show that UAV photogrammetry metrics can be used to estimate h, dbh, and ba of secondary vegetation with an accuracy similar to that obtained from LiDAR. In addition to presenting the lowest cost, the estimates derived from DAP allowed for the classification of successional stages in the analyzed secondary forest areas.

Structure and fracture characterization of the Jizan group: Implications for subsurface CO2 basalt mineralization

The coastal region of southwest Saudi Arabia contains a thick sequence of Late Oligocene basalts in the Jizan Group, which accumulated along the continental rift that preceded the opening of the Red Sea. These basalts are targeted for the disposal of CO2 emitted from industrial sources by subsurface carbon mineralization processes. The disposal potential of the Jizan Group basalts depends on having adequate permeability along fracture networks capable of conducting injected fluids away from the wellbores. The basalts in the Jizan Group generally lack primary permeability due to hydrothermal alteration, but are cross-cut by a dense network of fractures. In this paper, we describe and interpret the structural geology of the area based on field and geophysical data, and characterize the fracture development in the Jizan Group. The Jizan Group in the area comprises a bimodal suite of 30–21 Ma volcanic and volcaniclastic rocks and lacustrine sediments that accumulated in a continental rift valley similar to the East African rift. It consists predominantly of basaltic lavas that were fed by dense swarms of sheeted basalt dikes intruded parallel to the rift axis. Structurally the area is composed of half grabens bounded from the west by antithetic normal faults, and from the east by a megaflexure. Fractures in the Jizan Group were characterized by ground and aerial digital photogrammetry of outcrops. Mean P21 fracture intensities from 12 scattered meter scale outcrops are in the range 5–54 m−1, which demonstrates that the Jizan Group is highly fractured. Fracture directions are multimodal. The dominant fracture trend is 140–160 N, which is parallel to the sheeted dike swarms and normal faults, and therefore parallel to the paleo-rift axis. Additional conjugate and orthogonal fracture sets are also recognized. The presence of pervasive fracture-based permeability in the Jizan Group will facilitate the injection and mineral carbonation of carbon dioxide in the mafic volcanic rocks in this region.

Polychrome majolica of Apulian domes: history, technique, pathology and conservation

The paper is focused on the historical-architectural evolution and material-technical qualification of polychrome majolica domes that were built between the 17th and 19th centuries in Apulian religious buildings. In particular, the cultural context in which this solution spread throughout the Mediterranean area is identified. Moreover, the case history of construction and decorative techniques that are distinctive of the regional territory is discussed in terms of underlying structure, installation of elements and surface colours. The study is then detailed on the Church of Santa Maria della Vetrana in Castellana Grotte (BA), for which the morpho-typological survey and mapping of the decay state are presented, based on the use of terrestrial and aerial digital photogrammetry. Finally, based on the identification of the main pathologies from both direct and indirect alteration factors, the most appropriate conservation and maintenance interventions are outlined, with specific focus on repair and integration of the majolica tiles, according to principles of high compatibility and low intrusiveness for a solution meeting artisan tradition and technical practice.

Polychrome majolica of Apulian domes: history, technique, pathology and conservation

The paper is focused on the historical-architectural evolution and material-technical qualification of polychrome majolica domes that were built between the 17th and 19th centuries in Apulian religious buildings. In particular, the cultural context in which this solution spread throughout the Mediterranean area is identified. Moreover, the case history of construction and decorative techniques that are distinctive of the regional territory is discussed in terms of underlying structure, installation of elements and surface colours. The study is then detailed on the Church of Santa Maria della Vetrana in Castellana Grotte (BA), for which the morpho-typological survey and mapping of the decay state are presented, based on the use of terrestrial and aerial digital photogrammetry. Finally, based on the identification of the main pathologies from both direct and indirect alteration factors, the most appropriate conservation and maintenance interventions are outlined, with specific focus on repair and integration of the majolica tiles, according to principles of high compatibility and low intrusiveness for a solution meeting artisan tradition and technical practice.

Mapping with height and spectral remote sensing implies that environment and forest structure jointly constrain tree community composition in temperate coniferous forests of eastern Washington, United States

Maps of species composition are important for assessing a wide range of ecosystem functions in forested landscapes, including processes shaping community structure at broader (e.g., climate) and finer (e.g., disturbance) scales. Incorporating recently available remotely sensed datasets has the potential to improve species composition mapping by providing information to help predict species presence and relative abundance. Using USDA Forest Service Forest Inventory and Analysis plot data and the gradient nearest neighbor imputation modeling approach in eastern Washington, USA, we developed tree species composition and structure maps based on climate, topography, and two sources of remote sensing: height from digital aerial photogrammetry (DAP) of pushbroom aerial photography and Sentinel-2 multispectral satellite imagery. We tested the accuracy of these maps based on their capacity to predict species occurrence and proportional basal area for 10 coniferous tree species. In this study region, climate, topography, and location explained much of the species occurrence patterns, while both DAP and Sentinel-2 data were also important in predicting species proportional basal area. Overall accuracies for the best species occurrence model were 68–92% and R2 for the proportional basal area was 0.08–0.55. Comparisons of model accuracy with and without remote sensing indicated that adding some combination of DAP metrics and/or Sentinel-2 imagery increased R2 for the proportional basal area by 0.25–0.45, but had minor and sometimes negative effects on model skill and accuracy for species occurrence. Thus, species ranges appear most strongly constrained by environmental gradients, but abundance depends on forest structure, which is often determined by both environment and disturbance history. For example, proportional basal area responses to moisture limitation and canopy height varied by species, likely contributing to regional patterns of species dominance. However, local-scale examples indicated that remotely sensed forest structures representing recent disturbance patterns likely impacted tree community composition. Overall, our results suggest that characterizing geospatial patterns in tree communities across large landscapes may require not only environmental factors like climate and topography, but also information on forest structure provided by remote sensing.

Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR

Forest land plays a vital role in global climate, ecosystems, farming and human living environments. Therefore, forest biomass estimation methods are necessary to monitor changes in the forest structure and function, which are key data in natural resources research. Although accurate forest biomass measurements are important in forest inventory and assessment, high-density measurements that involve airborne light detection and ranging (LiDAR) at a low flight height in large mountainous areas are expensive. The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to leverage their complementary advantages. In this study, we utilized digital aerial photogrammetry (DAP), which has the unique advantages of speed, high spatial resolution, and low cost, to compensate for the deficiency of forestry inventory using UAS-based LiDAR that requires terrain-following flight for high-resolution data acquisition. Combined with the sparse LiDAR points acquired by using a high-altitude and high-speed UAS for terrain extraction, dense normalized DAP point clouds can be obtained to produce an accurate and high-resolution canopy height model (CHM). Based on the CHM and spectral attributes obtained from multispectral images, we estimated and mapped the AGB of the region of interest with considerable cost efficiency. It is proved that sparse LiDAR point cloud could serve as important sources for terrain estimation. The accuracy of the AGB estimates could be improved by 9% and 5% in terms of the RMSE and R2, respectively, by adding spectral metrics to point cloud structural metrics. Compared with results obtained using only spectral metrics, the results obtained using the combination of spectral and point cloud metrics were better by 37% in terms of the RMSE and 55% in R2. Our study supports the development of predictive models for large-scale wall-to-wall AGB mapping by leveraging the complementarity between DAP and LiDAR measurements. This work also reveals the potential of utilizing a UAS-based digital camera and LiDAR synergistically in a plateau mountainous forest area. In this future, we will further investigate different establishing strategies for combining different sensors in a synergistic system.