High-resolution Airborne LiDAR Research Articles

Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR

The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope (R 2 < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series

Earth observation satellites offer vast opportunities for quantifying landscapes and regional land cover composition and changes. The integration of artificial intelligence in remote sensing is essential for monitoring significant land cover types like forests, demanding a substantial volume of labeled data for effective AI model development and validation. The Wald5Dplus project introduces a distinctive open benchmark dataset for mid-European forests, labeling Sentinel-1/2 time series using data from airborne laser scanning and multi-spectral imagery. The freely accessible satellite images are fused in polarimetric, spectral, and temporal domains, resulting in analysis-ready data cubes with 512 channels per year on a 10 m UTM grid. The dataset encompasses labels, including tree count, crown area, tree types (deciduous, coniferous, dead), mean crown volume, base height, tree height, and forested area proportion per pixel. The labels are based on an individual tree characterization from high-resolution airborne LiDAR data using a specialized segmentation algorithm. Covering three test sites (Bavarian Forest National Park, Steigerwald, and Kranzberg Forest) and encompassing around six million trees, it generates over two million labeled samples. Comprehensive validation, including metrics like mean absolute error, median deviation, and standard deviation, in the random forest regression confirms the high quality of this dataset, which is made freely available.

Feasibility of monitoring floodplain on-farm water storages by integrating airborne and satellite LiDAR altimetry with optical remote sensing

Small water storages (≤ 500 ha surface area) enhance water supply for agricultural, human and livestock consumption. Their oftentimes large numbers and wide geographic spread, plus inaccurate or absent in situ metering, make their inclusion in water resource management difficult. This research assessed the capabilities of satellite optical remote sensing and LiDAR altimetry to characterise small water storages used for irrigation, so they can be included in water resources assessments. Landsat and Sentinel-2 water maps were integrated with airborne and satellite LiDAR altimetry to assess: (i) locations; (ii) elevation-area-volume relations; (iii) dates of construction/decommissioning; (iv) actual evaporation losses; and (v) storage volume changes of floodplain on-farm water storages (FoFWS) used primarily for cotton irrigation in the semi-arid Barwon-Darling region (142,173 km2), Australia. These remotely sensed characteristics would enable the representation of FoFWS in river system models and in regional water management plans, thus enhancing socioeconomic and environmental outcomes. Results from Landsat water maps showcased the growth in FoFWS surface area. In January 1988 there were 17 FoFWS with a total maximum surface area of 101.89 ha. By December 2021, the then 105 FoFWS maximum surface area was 5587.68 ha; a 55-times increase. For the first time in a regional context, mean annual (2000/01–2020/21) standing water evaporation losses were estimated for all FoFWS. These accounted to 52.47 MCM/year, which is a substantial ∼27% of the estimated long-term mean regional irrigation diversion limit (189 MCM/year). Elevation measurements from satellite LiDAR altimetry data, specifically GEDI and ICESat-2, were evaluated to assess if water volumes in FoFWS can be estimated through elevation-area-volume relations from a 1 m digital elevation model derived from airborne LiDAR data. Although GEDI's spatially variable footprint and ICESat-2's 91-day repeat cycle render them insufficient to monitor changes in water volumes in FoFWS, both satellite LiDAR altimeters provided accurate water level observations, with root mean squared differences (RMSD) of 0.38 m for GEDI and 0.12 m for ICESat-2, when compared to in situ water level measurements. ICESat-2's 0.7 m along-track sampling interval also detected changes of FoFWS structural features, such as increases in wall heights in recent years. This study demonstrated the value of integrating high-resolution airborne LiDAR data with satellite altimetry (LiDAR and/or radar interferometry), along with satellite optical water maps and rates of actual evaporation, to monitor FoFWS and other similar individually small yet collectively large reservoirs. Where similar data are available globally, then accurate information about small and dispersed water storages across poorly metered irrigated agricultural landscapes can be retrieved. Doing so will improve water resource management in the Barwon-Darling region, and in similar semi-arid and arid areas globally where irrigated agriculture is present.

Investigating paleoseismicity using high-resolution airborne LiDAR data - A case study of the Huangxianggou fault in the northeastern Tibetan plateau

Fault-offset landforms have long been recognized as holding important information about paleoseismic slip. Constructing an along-strike fault slip distribution could help reveal a fault's long-term rupture patterns and facilitate a more precise assessment of its future behavior. In this study, we documented the paleoseismic history of the Huangxianggou Fault, an oblique sinistral-thrust segment of the West Qingling Fault in the northeastern region of the Tibetan Plateau, using LiDAR-derived high-resolution DEM (0.1 m/pixel) and measurement of offset landforms. A total of 46 well-preserved and well-shaped landforms were chosen for horizontal and vertical displacement measurements, and the dense measurement data allow us to generate the cumulative offset probability distribution curves of horizontal and vertical displacements. Statistical analysis revealed at least seven surface-rupturing events, each with similar coseismic horizontal displacements of ∼7.0 m and vertical displacements of ∼0.7 m, in agreement with previous paleoseismic trench results. Using empirical relationships between moment magnitude and horizontal displacement, a plausible moment magnitude range of Mw7.3–7.7 was determined for these paleoearthquakes. Furthermore, by evaluating rupture parameters and fault slip rate, we determined a recurrence interval of 2.7–3.4 kyr for significant surface-rupturing events on this fault. The elapsed time since the last earthquake closely approaches this interval, suggesting a potential seismic risk for the Huangxianggou Fault. Our results emphasizes the importance of high-resolution topographic data in paleoseismic investigations, enabling the identification of numerous offset landforms and precise displacement measurements across faults. Paleoearthquake counts derived from horizontal and vertical displacement analyses were found to be consistent, highlighting the significance of vertical displacements in quantifying paleoearthquakes for strike-slip faults with a thrust component.

Mapping stone walls in Northeastern USA using deep learning and LiDAR data

ABSTRACT Stone walls are widespread and iconic landforms found throughout forested terrain in the Northeastern USA that were built during the 17th to early 20th centuries to delineate property boundaries and the edges of agricultural fields and pastures. As linear, or broadly curved, features that are typically >0.5 m high, 1–2 m wide, and >4–8 m long, stone walls are highly visible in LiDAR data, and mapping them is of broad interest to the cultural heritage sector as well as to researchers specifically focused on historic landscape reconstruction. However, existing mapping attempts have commonly relied on field surveys and manual digitization, which is time-consuming, especially when trying to complete mapping at broader scales. In response to this limitation, this study: (1) presents a novel framework to automate stone wall mapping using Deep Convolutional Neural Networks (DCNN) models (U-Net and ResUnet) and high-resolution airborne LiDAR, (2) evaluates model performance in two test sites against field verified stone walls, (3) investigates the factors that can influence model performance in terms of the quality of LiDAR data (e.g. ground point spacing), and (4) suggests post-processing for town-level mapping of stone walls (~120 km2). Both models performed well with respect to the Matthews Correlation Coefficient (MCC) score. U-Net scenario 3 achieved an MCC score of 0.87 at test site 1, while ResUnet scenario 3 (S3) had an MCC score of 0.80 at test site 2. In town-level test site 3, ResUnet S3 achieved the best F1 score of 82% after post-processing. This study demonstrates the potential of automated mapping of anthropogenic features using our models.

SUPPORT VECTOR MACHINE FOR LANDSLIDE ACTIVITY IDENTIFICATION BASED ON VEGETATION ANOMALIES INDICATOR

Landslide activity identification is critical for landslide inventory mapping. A detailed landslide inventory map is highly required for various purposes such as landslide susceptibility, hazard, and risk assessments. This paper proposes a novel approach based on vegetation anomalies indicator (VAI) and applying machine learning method namely support vector machine (SVM) to identify status of natural-terrain landslides. First, high resolution airborne LiDAR data and satellite imagery were used to derive landslide-related VAIs, including tree height irregularities, canopy gap, density of different layer of vegetation, vegetation type, vegetation indices, root strength index (RSI), and distribution of water-loving trees. Then, SVM is utilized with different setting of parameter using grid search optimization. SVM Radial Basis Function (RBF) recorded the best optimal pair value with 0.062 and 0.092 misclassification rate for deep seated and shallow translational landslide, respectively. For landslide activity classification, SVM RBF recorded the best accuracy value for both deep seated and shallow translational landslides with 86.0 and 71.3, respectively. Overall, VAIs have great potential in tackling the landslide activity identification problem especially in tropical vegetated area.

Identification of windthrow-endangered infrastructure combining LiDAR-based tree extraction methods using GIS

Windthrows induced by strong winds pose a major threat to both transport infrastructure and road users. Therefore, an exposure analysis of trees along the federal trunk road network was carried out and exemplarily applied for the federal state of North Rhine-Westphalia, Germany. The aim of this project was the development of a GIS-based method on the basis of freely accessible high-resolution airborne LiDAR data as well as RGBI orthoimage data that identifies and parameterizes single trees. For the determination of a suitable method, several models with different settings and parameters were calculated, validated, and iteratively adjusted to gain the optimal settings for tree identification. The identification of trees was finally realized by applying both a minimum-curvature technique for single trees as well as a local maximum approach for dense vegetation, especially for trees with small crown diameters. False classification results corresponding to nonvegetation areas have been corrected by the use of the normalized difference vegetation index. With our method, we accomplish detection accuracies from 65% to 75% in very heterogeneous environments and 75% to 100% in more specific settings. All tree candidates with potential to affect the road infrastructure and road users were retrieved and joined to the respective road sections to deliver a fast and effective method for analyzation and visualization of vulnerable parts of the trunk road network due to tumbling trees.

Generating Landslide Susceptibility Map using Airborne Lidar Derived Parameters and Geological Mapping Factors for Canada Hill, Miri, Sarawak

Landslide Susceptibility Mapping using GIS software and remote sensing data have been conducted in several location involving geological and geomorphological sensitive at Canada Hill, Miri. The previous researcher has conducted quantitative analyses using different statistical methods with different parameters in the same study area. The mapping of landslides using high-resolution Airborne LiDAR data is a valuable effort. All of this play important role, in the analysis and development of landslide susceptibility map. High-resolution Airborne LiDAR data has the ability to penetrate thick forest cover and produce Digital Terrain Model. Using Digital Terrain Model, the landslide parameter can be generated and extracted. The main objective of this study was to produce landslide susceptibility map using the Probability Frequency Ratio Model method. This study involved the delineating of causative factors from Digital Terrain Model generated by Airborne LiDAR data as well as the data collected from the field. Apart from parameters derived from LiDAR, parameters from filed and site investigation were included into the mapping process. This study was different from the previous studies in the same area in terms of various analytical approaches and samples used. The results of the landslide susceptibility map were verified via randomly selected landslides samples using two different methods. The landslide susceptibility map produced is more refined and is able to predict more effectively compared to the existing map. The landslide susceptibility map produced in this study could be used for land use planning and management by decision makers and land use planners.

An Efficient Multi-stage Object-Based Classification to Extract Urban Building Footprints from HR Satellite Images

Urban building information can be effectively extracted by applying object-based image segmentation and multi-stage thresholding on High Resolution (HR) remote sensing satellite imageries. This study provides the results obtained using this method on the images of Indian remote sensing satellite, CARTOSAT-2S launched by the Indian Space Research Organization (ISRO). In this study, a method is developed to extract urban building footprints from the HR remote sensing satellite images. The first step of the process consists of generating highly dense per pixel Digital Surface Model (DSM) by using semi global matching algorithm on HR satellite stereo images and applying robust ground filtering to generate Digital Terrain Model (DTM). In the second step, multi-stage object-based approach is adopted to extract building bases using the PAN sharpened image, normalized Digital Surface Model (nDSM) derived from DSM and DTM, and Normalised Difference Vegetation Index (NDVI). The results are compared with the manual method of drawing building footprints by cartographers. An average precision of 0.930, recall of 0.917, and f-score of 0.922 are obtained. The results are found to be in a match with the method using the high resolution Airborne LiDAR DSM by providing a solution for large areas, low cost and low time.

Assessing Reef Island Sensitivity Based on LiDAR-Derived Morphometric Indicators

Reef islands are some of the most highly sensitive landforms to the impacts of future environmental change. Previous assessments of island morphodynamics primarily relied on historical aerial and satellite imagery. These approaches limit analysis to two-dimensional parameters, with no ability to assess long-term changes to island volume or elevation. Here, we use high-resolution airborne LiDAR data to assess three-dimensional reef island features for 22 islands along the north-western coast of Australia. Our primary objective was to utilize two regional LiDAR datasets to identify characteristics indicative of island sensitivity and future vulnerability. Results show reef platform area to be an accurate predictor of island area and volume suggesting larger island volumes may reflect (1) increased carbonate production and supply from the reef platform and/or (2) enhanced shoreline protection by larger reef platforms. Locations of foredune scarping (an erosional signature) and island orientations were aligned to the regional wind and wave climate. Reef island characteristics (island area, volume, elevation, scarping, and platform area) were used to rank islands according to sensitivity, using a new Island Sensitivity Characteristics Index (ISCi) where low ISCi indicates stable islands (large areas and volumes, high elevations, and fewer scarped areas) and high ISCi indicates unstable islands (small areas and volumes, low elevations, and more scarped areas). Comparison of two LiDAR surveys from 2016 and 2018 validates the use of 3D morphometrics as important (direct) measurements of island landform change, and can complement the use of 2D parameters (e.g., area) moving forward. Results demonstrate that ongoing use of airborne LiDAR and other 3D technology for monitoring coral reef islands at regional scales will enable more accurate quantification of their sensitivity to future impacts of global environmental change.

Morphological Characteristics of Tidal Creeks in the Central Coastal Region of Jiangsu, China, Using LiDAR

Tidal creeks are an important component of the intertidal zone and are essential for maintaining the balance between sedimentary processes and the hydrodynamic environment. A quantitative analysis of the morphological characteristics of tidal creeks is essential for understanding their processes of evolution and to evaluate the stability of tidal flats. This study describes the morphological characteristics of tidal creeks using a high-resolution airborne LiDAR DEM. The parameters include the number, order, length, width, depth, and width/depth ratio. The results show that the number and degree of development tidal creeks along the central coast of Jiangsu are higher than those in the radial sandbanks, and the mean width and length increase with the increasing tidal creek order. The number, length, and depth of tidal creeks in the salt marsh zone with well-developed vegetation are higher than those areas with little vegetation cover. The number of tidal creeks in the mid-upper intertidal zone is the largest, while the length and width of tidal creeks in the lower intertidal zone are the greatest. The differences in these characteristics are mainly related to the vegetation distribution, tidal flat width, and hydrodynamic conditions. Our findings potentially provide guidelines for coastal management and the evaluation of tidal flat stability.

Tree height explains mortality risk during an intense drought

Forest mortality is accelerating due to climate change and the largest trees may be at the greatest risk, threatening critical ecological, economic, and social benefits. Here, we combine high-resolution airborne LiDAR and optical data to track tree-level mortality rates for ~2 million trees in California over 8 years, showing that tree height is the strongest predictor of mortality during extreme drought. Large trees die at twice the rate of small trees and environmental gradients of temperature, water, and competition control the intensity of the height-mortality relationship. These findings suggest that future persistent drought may cause widespread mortality of the largest trees on Earth.

Tracing the last remnants of the Scandinavian Ice Sheet: Ice-dammed lakes and a catastrophic outburst flood in northern Sweden

We present geomorphological evidence of large, previously undocumented, early Holocene ice-dammed lakes in the Scandinavian Mountains of northwestern Sweden. The lakes extents indicate that the last remnants of the Scandinavian Ice Sheet were located east of the mountain range. Some early pioneering works have presented similar reconstructions, whereas more recently published reconstructions place the last ice remnants in the high mountains of Sarek.Using high-resolution airborne LiDAR data we have mapped a large number of hitherto undocumented shorelines in some of the main valleys within the northern Scandinavian mountain range. Our results indicate that a larger system of ice-dammed lakes existed in this region than previously thought. The lakes were dammed between the main water divide to the west and the retreating ice sheet margin to the east. The shorelines dip towards the northwest with gradients ranging from 0.5 to 0.4 m/km, from the oldest to the youngest. Further, we have compiled Lateglacial and Holocene shoreline data along the Norwegian coast and from within the Baltic Sea basin and reconstructed the isostatic uplift along a 1400 km long northwest-southeast transect from the Norwegian Sea to Lake Ladoga. By comparing the measured ice-dammed lake shoreline gradients to the dated marine shorelines, we infer that the lakes may have existed for several centuries following 10.2 cal ka BP. We also describe large deposits and extensive erosive features, which demonstrate that a catastrophic glacial lake outburst flood (GLOF) took place eastward along the Pite River Valley. Based on cross-cutting relations to raised shorelines developed in the early Holocene Ancylus Lake (Baltic Sea basin) we conclude that the flood and thus the final phase of deglaciation took place within the time interval 10.3–9.9 cal ka BP.

Integrated Object-Based Image Analysis for semi-automated geological lineament detection in southwest England

Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection.

An approach to estimating forest biomass change over a coniferous forest landscape based on tree-level analysis from repeated lidar surveys

ABSTRACTForests represent a significant opportunity for carbon sequestration, but quantifying biomass change at the landscape scale and larger remains a challenge. Here we develop an approach based on repeated tree-level analysis using high-resolution airborne lidar (around 8 pulses/m2). The study area was 53 km2 of actively managed coniferous forestland in the Coast Range Mountains in western Oregon. The study interval was 2006–2012. Tree heights and crown areas were determined from the lidar data using point cloud clustering. Biomass per tree was estimated with allometry. Tree-level data (N = 14,709) from local USDA Forest Service Forest Inventory and Analysis plots provided the basis for the allometry. Estimated biomass change over the 6-year interval averaged −1.3 kg m−2 year−1, with the average gain in undisturbed areas of 1.0 kg m−2 year−1. Full harvest occurred on 3% of the area per year. For surviving trees, the mean change in height was 0.5 m year−1 (SD = 0.3) and the mean change in biomass was 45.3 kg year−1 (SD = 6.7). The maximum bin-average increase in biomass per tree (57.3 kg year−1) was observed in trees of intermediate height (35–40 m). In addition to high spatially resolved tracking of forest biomass change, potential applications of repeated tree-level surveys include analysis of mortality. In this relatively productive forest landscape, an interval of 6 years between lidar acquisitions was adequate to resolve significant changes in tree height and area-wide biomass.

The effect of user-defined parameters on DTM accuracy—development of a hybrid model

User-defined parameters (point density, interpolation, and pixel size) have a great influence on the accuracy of digital terrain model (DTM). Therefore, the optimal interpolation method (IM) and appropriate pixel size should be used to create a continuous surface. Pixel size or spatial resolution tends to be a compromise between the number of samples and the size of the study area, whereby input dataset is often devaluated. In this paper, the authors propose a new methodological approach to DTM production (hybrid DTM—HDTM) from an aero-photogrammetry dataset. Two different approaches to DTM production are presented: the usual (UDTM) and the hybrid (HDTM). HDTM is based on restructuring and refining the input dataset by generating contour lines, determining the optimal interpolation method and selecting the appropriate spatial resolution. The goal is to develop a qualitative DTM while minimalizing the devaluation of input data and error propagation. The accuracy of different IMs for UDTM was examined in a comparative analysis of six statistical parameters by applying cross-validation, with the focus on the root mean square error (RMSE) parameter. Four different methods of selecting optimal spatial resolution were tested for the same model. The UDTM and HDTM generated were compared by interior, exterior, and visual accuracy assessment and by the performance success of specific hydrological parameters: (1) flow accumulation—by applying a developed DNA concept and (2) watershed—by calculating volume and 3D surface. The reference value for exterior accuracy assessment was high-resolution airborne LiDAR DTM (LDTM). It was found that ordinary kriging (OK) was the best IM (RMSE, 1.9893 m). The spatial resolution of UDTM was calculated by combining two variants of the point pattern analysis method and was found to be 19 m. On the other hand, the complexity of terrain method was used to define the spatial resolution of HDTM (3 m). HDTM achieved better results than UDTM in all aspects of accuracy assessment. The exterior accuracy of HDTM was better by 1.483 m (RMSE). Finally, the results of the applied DNA concept showed that a stream generated from HDTM had a 2.587 m lower horizontal root mean square error (HRMSE) than the stream generated from UDTM.

Investigating the Influences of Tree Coverage and Road Density on Property Crime

With the development of Geographic Information Systems (GIS), crime mapping has become an effective approach for investigating the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment reveals possible strategies for reducing crime in a neighborhood. The relationship between vegetation density and crime has long been under debate. The convenience of a road network is another important factor that can influence a criminal’s selection of locations. This research is conducted to investigate the correlations between tree coverage and property crime, and road density and property crime in the City of Vancouver. High spatial resolution airborne LiDAR data and road network data collected in 2013 were used to extract tree covered areas for cross-sectional analysis. The independent variables were inserted into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their relationships to property crime rates. The results of the cross-sectional analysis provide statistical evidence that there are negative correlations between property crime rates and both tree coverage and road density, with the stronger correlations occurring around Downtown Vancouver.

Rapid mapping application of vegetated terraces based on high resolution airborne LiDAR

The aim of this work is to define a methodology for terraced areas survey and rapid mapping in a complex environment, like Ligurian (Northwestern Italy) one, where a remarkable percentage of its surface is estimated as terraced and where the canopy coverage makes their recognition very hard. Methodology steps are the definition of LiDAR survey parameters, morphometric filtering and GIS processing for final mapping. Each phase is oriented to provide a reliable terrace mapping, also practicable in canopy-covered areas due to a particular attention to land cover influence. The work considers a case study (Rupinaro basin) close to Cinque Terre, with a mixed land cover (terraces, forest and urbanized areas). The methodology provided encouraging results detecting 448 ha of terraces, 95% of them located under canopy cover. This finding pointed out that terraces mapping cannot rely only on photo-interpretation, as canopies will hamper their detection. Mapping of these areas, frequently characterized by abandonment, is crucial while identifying potential trigger factors for slope instabilities. This case study highlighted the importance of a carefully planned production chain, that should start from LiDAR survey parameter choice, providing the best input for analysis algorithm and providing the correct identification of terraces.

Employing airborne lidar and archaeological testing to determine the role of small depressions in water management at the ancient Maya site of Yaxnohcah, Campeche, Mexico

High-resolution airborne lidar has been employed in the Maya lowlands to examine landscape modifications, detect architectural features, and expedite and expand upon traditional settlement surveys. Another potentially beneficial—and to-date underutilized—application of lidar is in the analysis of water management features such as small reservoirs and household storage tanks. The urban center of Yaxnohcah, located within the Central Karstic Uplands of the Yucatan Peninsula, provides an ideal test case for studying how the residents of this important Maya community managed their seasonally scarce water resources at the household scale. We employ an integrative approach combining lidar-based GIS analysis of 24km2 of the site area, ground verification, and excavation data from five small depressions to determine their function and the role they may have played in water management activities. Our research shows that some, but not all, small depressions proximate to residential structures functioned as either natural or human-made storage tanks and were likely an adaptive component of expanding Middle Preclassic to Classic period urbanization at the site. Thus, while lidar has revolutionized the identification of topographical features and hydrologic patterns in the landscape, a combination of ground verification and archaeological testing remains necessary to confirm and evaluate these features as potential water reservoirs.

Snow depth from ICESat laser altimetry — A test study in southern Norway

Direct snow depth measurements are sparse, especially in remote areas. In this study, we assess the potential of ICESat laser altimetry for providing snow depths for its operational period 2003–2009 on the example of the Scandinavian Mountains in southern Norway. Snow cover during ICESat campaigns typically results in positive elevation differences (dh) between ICESat GLAH14 elevations and reference elevations from Digital Elevation Models (DEMs). Three DEMs are used: the Norwegian national DEM for the entire study area, and the SRTM DEM and a high-resolution airborne lidar DEM for a spatial subset on the Hardangervidda mountain plateau. To account for uncertainty in elevation data, ICESat samples are grouped into spatial subsets, elevation bands, and over time (e.g. all winter campaigns together). We find that ICESat has the potential to provide regional-scale snow depths for the years 2003–2009 for its winter (March) and late spring (June) campaigns. ICESat-derived snow depth time series for different elevation bands agree well with measured (RMSE 0.47m) and modelled (RMSE 0.61m) snow depths in the study area. Annual differences in snow amounts and the increase of snow depths with elevation and coastal proximity over the study area are correctly reproduced. Uncertainties in reference elevations exceed ICESat elevation uncertainty and good control over errors and biases in reference DEMs turn out essential. Spatially varying vertical offsets between ICESat and the reference DEMs make it necessary to bias-correct March/June snow depths with autumn dh per spatial unit or elevation band. Best results are achieved when samples are summarised per season over the entire observation period. After correction of local DEM biases, the spatial pattern of ICESat 2003–2009 March dh matches spatially distributed modelled snow depths in southern Norway with decimeter-scale accuracy. In the western part of Hardangervidda, ICESat-based March snow depths agree better with measurements (RMSE ≤0.15m for all DEMs) than modelled snow depths do (RMSE 0.61m). In eastern Hardangervidda, the coarse resolution SRTM DEM (RMSE 0.41m) performs better than the 10m Norwegian DEM (RMSE 0.64m) which is based on a less consistent mosaic of elevation data. Using the high-resolution lidar DEM, even single footprints show good agreement (R2 0.59, RMSE 0.94m) with measured snow depths from the same year. Snow depth estimates could be further improved by using full waveform ICESat data or elevation measurements from ICESat-2 once this satellite is operational. Good quality reference DEMs may still be acquired in the future even in areas where no such data exists today.