Lidar-derived structural-complexity data across four experimental forests

This study evaluates the potential of low-altitude airborne laser scanning (ALS) and terrestrial laser scanning (TLS) for characterizing structural complexity in Southern Finland. Unlike species diversity, structural complexity reflects realized niche occupancy by describing how vegetation utilizes light, water, and space, providing key insights into ecosystem functioning. We analyzed 99 circular sample plots (r = 20 m) scanned with helicopter-borne ALS at 80 m altitude and TLS data from nine scan locations per plot. Structural complexity metrics were derived at both grid level (variability in canopy height models and voxel occupancy) and object level (variability in individual tree attributes). High-density ALS effectively captured vertical and horizontal complexity through object-level analysis, showing close agreement with TLS. However, differences in measurement geometry affected volumetric complexity, with ALS and TLS characterizing tree architecture and vegetation occupancy differently. Object-level approaches captured a broader range of horizontal and vertical complexity, while grid-level approaches better captured volumetric variability, facilitating the identification of forest stand properties and biodiversity hotspots. The strongest agreement between ALS and TLS occurred for variation in tree height (R² = 0.66, Spearman = 0.80), while lowest agreement was found for fractal dimensions of tree architecture (R² = 0.04, Spearman = 0.25).

Airborne laser scanning (ALS) has demonstrated utility for forestry applications and has renewed interest in other forms of remotely sensed data, especially those that capture three-dimensional (3-D) forest characteristics. One such data source results from the advanced processing of high spatial resolution digital stereo imagery (DSI) to generate 3-D point clouds. From the derived point cloud, a digital surface model and forest vertical information with similarities to ALS can be generated. A key consideration is that when developing forestry related products such as a canopy height model (CHM), a high spatial resolution digital terrain model (DTM), typically from ALS, is required to normalize DSI elevations to heights above ground. In this paper we report on our investigations into the use of DSI-derived vertical information for capturing variations in forest structure and compare these results to those acquired using ALS. An ALS-derived DTM was used to provide the spatially detailed ground surface elevations to normalize DSI-derived heights. Similar metrics were calculated from the vertical information provided by both DSI and ALS. Comparisons revealed that ALS metrics provided a more detailed characterization of the canopy surface including canopy openings. Both DSI and ALS metrics had similar levels of correlation with forest structural attributes (e.g., height, volume, and biomass). DSI-based models predicted height, diameter, basal area, stem volume, and biomass with root mean square (RMS) accuracies of 11.2%, 21.7%, 23.6%, 24.5%, and 23.7%, respectively. The respective accuracies for the ALS-based predictions were 7.8%, 19.1%, 17.8%, 17.9%, and 17.5%. Change detection between ALS-derived CHM (time 1) and DSI-derived CHM (time 2) provided change estimates that demonstrated good agreement (r = 0.71) with two-date, ALS only, change outputs. For the single-layered, even-aged stands under investigation in this study, the DSI-derived vertical information is an appropriate and cost-effective data source for estimating and updating forest information. The accuracy of DSI information is based on a capability to measure the height of the upper canopy envelope with performance analogous to ALS. Forest attributes that are well captured and subsequently modeled from height metrics are best suited to estimation from DSI metrics, whereas ALS is more suitable for capturing stand density. Further investigation is required to better understand the performance of DSI-derived height products in more complex forest environments. Furthermore, the difference in variance captured between ALS and DSI-derived CHM also needs to be better understood in the context of change detection and inventory update considerations.

Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests

Investigation of lineament extraction: Analysis and comparison of digital elevation models in the Ait Semgane region, Morocco

Qualities of software design heavily affect on qualities of software ultimately developed. One of the claimed advantages of object-oriented paradigm is the ease of maintenance. Assessing maintainability in design phase will help software designers to decide that the design of software should be altered or not. This will help reducing cost of software maintenance in later phases. This research presents a controlled experiment carried out to investigate whether structural complexity and aesthetic metrics can be indicators of class and sequence diagrams maintainability and to establish maintainability models from both metric sets. Maintainability models are constructed by using three techniques: Discriminant analysis, Decision tree and MLP neural network in order to find the best one among these models. The preliminary result shows that the maintainability models constructed by applying MLP neural network are the best models in terms of the model accuracy. Moreover, this research constructs an automated tool for predicting maintainability using the maintainability models and also proposes a new set of structural complexity metrics for measuring maintainability from class diagrams.

Solar radiation significantly affects many processes on Earth. In situ measurements are demanding and require a dense network of sensors. A suitable alternative solution could be the modelling of potential solar radiation based on a digital elevation model (DEM) in geographic information systems. The key issue of this study is to determine the influence of the terrain parameters and the spatial resolution of a DEM on the calculation of potential solar radiation. The area of study is the Tatra Mountains (the highest mountains of the Carpathians). The DEM determined from light detection and ranging (LiDAR) was used. To determine the influence of the terrain, the following terrain parameters were applied: slope; aspect, represented by northness and eastness; elevation; and topographical position index using six different circular neighbourhoods (10 m, 30 m, 50 m, 100 m, 500 m, and 1000 m). The results indicate a moderate correlation (0.32–0.46) between the solar radiation calculation errors and the absolute values of the topographic position indices with small neighbourhoods (10 m–100 m). To show the impact of the spatial resolution, the calculation was performed based on four different DEM resolutions, namely 5 m, 10 m, 30 m, and 90 m. Mutual differences in potential solar radiation were quantified concerning the topographic position index. The result is also a model of potential annual solar radiation in the Tatra Mountains, calculated at a resolution of 5 m or 2 m.

A comparison of multiple methods for mapping groundwater levels in the Mu Us Sandy Land, China

Active remotely sensed data can be used to perform a variety of forestry tasks including stand characterization, inventory, and management of forest and fire behavior modeling. The present work investigates the potential of Airborne Laser Scanning (ALS) derived methods applied in the deciduous forest by processing an individual tree detection (ITD) based on canopy Height model (CHM) and tree segmentation of larger-area point clouds. Different algorithms are tested and their performances are evaluated to show which of them can provide the most adequate number of trees compared with the ground truth. Tree scale information is used in order to determine stand age. The forest height, structure, and density are specified by applying individual tree Detection (ITD) to calculate some forest attributes such as stem volume, forest uniformity, and biomass estimation. The major aim of this post is to examine the state of the forest to monitor it in real-time. We assume that utilizing the LM algorithm, which was originally built for ITD from LiDAR data, trees should be automatically distinguished from the ALS-derived CHM with reasonable accuracy. As a result, the present research work studies the fixed treetop window size (FWS), fixed smoothing window size (SWS), and variable window (VW) effect on ITD performance (RMSE=3.4% and R=0.88). It is obvious, from the obtained results that smaller window sizes result in more trees. In fact, the smallest trees obscured by the largest trees containing the highest points in the neighborhood are often ignored by large windows. Crown delineation is also explored to extract the height of the trees, radius crown and, 3D coordinates and to compare them to those detected by a Low Bluetooth sensor “iBeacon.”.

Gunung Sewu Karst, located in the Gunung Kidul Regency within the Special Region of Yogyakarta, features a distinctive karst topography consisting of dolines, hills and caves. Despite its aesthetic appearance, the area often experiences water scarcity due to the lack of surface water. Dolines, basin-like depressions formed during the karstification process, play a crucial role in biodiversity conservation, ecology, hydrology and land-use management planning. This study aims to map and identify the spatial distribution and morphometric characteristics of dolines in the Purwosari subdistrict, which have important implications for water resource management and drought mitigation in karst regions. The findings are intended to guide spatial planning and environmental mitigation strategies. Data for the study were derived from a digital elevation model (DEM), more scalable and accessible elevation data, to generate the Topographic Position Index (TPI) and Topographic Wetness Index for analysing doline spatial patterns, their terrain shapes, their types (dry or watery), and morphometry. The results show that dolines are associated with negative TPI values (58%) and low TWI values (53.48%), with most landforms classified below the plain level (27.91%). A significant proportion of dolines (79.06%) contain water, with the classification accuracy between TPI and TWI being 65.11%. Morphologically, most dolines are U-shaped, with oval-shaped dolines being the most common (41.86% of observations). The findings reveal that dolines in the Purwosari Subdistrict have significant underground water storage potential, making them crucial for mitigating regional drought risks.

Forest soils play a critical role in the global methane (CH4) budget, but the magnitude of CH4 fluxes varies significantly across a landscape, spatially and temporally. In complex landscapes, soil hydrology is strongly influenced by variations in topography and vegetation, which affect soil CH4 fluxes (FCH4). Consequently, accurately scaling FCH4 to the landscape level is a significant challenge. This study aimed to develop a methodology for scaling seasonal FCH4 across a topographically complex landscape in a cold temperate mountain forest.This study was conducted in the upper watershed of the Yura River in the Ashiu Experimental Forest (area 40 ha and elevation 600-850 m). The landscape was classified into upland, wetland, and river, comprising approximately 94%, 1%, and 5% of the total study area, respectively. 52 collars were installed in upland areas covering different topographic positions and vegetation types, and FCH4 were measured nine times from April to November. Additionally, 11 collars were installed in small riparian wetlands and measured twice during the wet-to-dry summer transition. Then, we used measured FCH4 together with topographic attributes i.e., slope, aspect, profile curvature (PRC), vertical distance to the channel network (VDCN), topographic position index (TPI), and topographic wetness index (TWI) from remotely sensed data (digital elevation model) and vegetation type (broadleaf, coniferous, and mixed) to develop a machine-learning model (quantile regression forest) for predicting upland seasonal FCH4 at 5 m resolution with uncertainty across the landscape level. A simple average was used to estimate the wetland fluxes.Seven predictor variables were used to model upland FCH4 for each season; the selected predictors and model accuracy varied with seasons. The model accuracy was high in early autumn (R2 = 0.67) and low in early wet summer (R2 = 0.28). TPI was consistently selected in all seasons, while TWI was chosen for most seasons except two, where VDCN was selected instead. VDCN and PRC were occasionally selected with TWI and TPI. Vegetation type was not selected for any of the seasons. Across the landscape, predicted upland median seasonal FCH4 ranged from -0.35 to -0.60 g CH4 hr-1 ha-1 in spring, -0.41 to -1.25 g CH4 hr-1 ha-1 in summer, and -0.50 to -0.89 g CH4 hr-1 ha-1 in autumn. This seasonal variation in upland predicted median FCH4 was well explained by the antecedent precipitation index (R2 = 0.71, p < 0.01) calculated over 20 days. When scaled at the landscape level, the average CH4 uptake by upland soils was -25.1 (uncertainty -35.8 to -16.2) g CH4 hr-1. In the wet summer, small wetland patches offset 8% of the upland CH4 uptake (-15.6 g CH4 hr-1 upland, 1.2 g CH4 hr-1 wetland), and the following dry summer, they offset only 2% because both the upland CH4 uptake increased and the wetland emission decreased (-32.6 g CH4 hr⁻¹ upland, 0.5 g CH4 hr⁻¹ wetland). This study highlighted the efficiency of remote sensing and machine learning approaches to extrapolate field measurements to the landscape level and allowed us to visualize spatial patterns of fluxes over time.

Error evaluation is essential for the improvement and application of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 06 daily precipitation estimates, including early-, late-, and final-run products (IMERG-DE, IMERG-DL, and IMERG-DF, respectively), especially for summer precipitation in complex topographical areas. However, many existing works mainly focus on comparing the error statistical metrics of precipitation estimates, but few further analyze the internal relationships between these error statistics and geographical features. Therefore, taking Sichuan Province of China as a case study of the complex topographic and mountainous area, we adopt statistical metrics, error decomposition schemes, systematic and random error separation models, and regression methods to analyze the relationships between the spatial distribution of IMERG summer precipitation error metrics and geographical features. These features include longitude, latitude, distance from Sichuan Basin edge (DFBE), digital elevation model (DEM), normalized difference vegetation index (NDVI), slope, aspect, and topographic position index (TPI). The results show that: (1) DEM and DFBE are the two most important geographical features affecting the spatial distribution of error metrics, while both aspect and TPI have negligible effects on these metrics; (2) the variations in DEM, DFBE, and latitude have the negative relationships with error metrics; (3) longitude and DFBE do not have a direct impact on the errors, but indirectly affect the precipitation errors through the changing DEM; (4) slope shows a strong negative correlation with hit bias, and its increase significantly amplifies the sensitivity of systematic errors of hit bias from IMERG-DE and DL; and (5) the high detection probability and small missed precipitation error of the three IMERG estimates are virtually unaffected by changes in geographical features.

Interferometry Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique for studying the earth’s surface topography and deformations; it is used to generate high-quality Digital Elevation Models (DEMs). DEMs are a crucial and primary input to various topographical quantification and modelling applications. The quality of input DEMs can be further improved using fusion methods, which combine multi-sensor or multi-temporal datasets intelligently to retrieve the best information from the input data. This research study is based on developing a Neural Network-based fusion approach for improving InSAR-based DEMs in plain and hilly terrain parts of India. The study areas comprise relatively plain terrain from Ghaziabad and hilly terrain of Dehradun and their surrounding regions. The training dataset consists of DEM elevations and derived topographic attributes like slope, aspect, topographic position index (TPI), terrain ruggedness index (TRI), and vector roughness measure (VRM) in different land use land cover classes of the study areas. The spaceborne altimetry ICESat-2 ATL08 photon data are used as a reference elevation. A Feed Forward Neural Network with a backpropagation algorithm is trained based on the prepared training samples. The trained model produces fused DEMs by learning the relationship between the input and target samples; this is used to predict elevations for the test areas. The accuracy of results from the models is assessed with TanDEM-X 90 m DEM. The fused DEMs show significant improvement in terms of RMSE (Root Mean Square Error) over the input DEMs with an improvement factor of 94.65% in plain areas and 82.62% in hilly areas. The study concludes that the ANN with its universal approximation property can significantly improve the fused DEM.

The level of spatial co-registration between airborne laser scanning (ALS) and ground data can determine the goodness of the statistical inference used in forest inventories. The importance of positioning methods in the field can increase, depending on the structural complexity of forests. An area-based approach was followed to conduct forest inventory over seven National Forest Inventory (NFI) forest strata in Spain. The benefit of improving the co-registration goodness was assessed through model transferability using low- and high-accuracy positioning methods. Through the inoptimality losses approach, we evaluated the value of good co-registered data, while assessing the influence of forest structural complexity. When using good co-registered data in the 4th NFI, the mean tree height (HTmean), stand basal area (G) and growing stock volume (V) models were 2.6%, 10.6% and 14.7% (in terms of root mean squared error, RMSE %), lower than when using the coordinates from the 3rd NFI. Transferring models built under poor co-registration conditions using more precise data improved the models, on average, 0.3%, 6.0% and 8.8%, while the worsening effect of using low-accuracy data with models built in optimal conditions reached 4.0%, 16.1% and 16.2%. The value of enhanced data co-registration varied between forests. The usability of current NFI data under modern forest inventory approaches can be restricted when combining with ALS data. As this research showed, investing in improving co-registration goodness over a set of samples in NFI projects enhanced model performance, depending on the type of forest and on the assessed forest attributes.

<p>Accurate characterization of land use and land cover (LULC) is important in a rapidly changing environment such as the Indonesian tropics. Over the past 30 years, native tropical forests have been cleared and replaced by fast-growing cash-crops, such as oil palm and rubber plantations. This change in land use dramatically alters the vegetation structure of the entire region. Vegetation structural complexity is highly variable in tropical forests, and provides habitat to a large number of native species. In addition, vegetation structure has an impact on micro-climate and the exchange of greenhouse gases (GHG), water and energy. Measuring vegetation structure in the field can be costly and time consuming, particularly in remote, inaccessible areas of tropical forest. In contrast, Airborne Laser Scanning (ALS) can provide very detailed three-dimensional information on forest structure without the need to reach remote areas in the field. Here, we aim to study the potential of ALS-derived measures of structural complexity as ecological indicators to highlight differences in forest structure across a gradient of LULC in Sumatra, Indonesia. We analysed the structural complexity of four main LULC types relevant to the region: tropical secondary forests, rubber agroforests, oil palm plantations and shrublands. Several structural metrics have been extracted from ALS data over 136 circular 0.1 ha plots (34 plots per LULC type): top height, height percentiles, rumple index, leaf area index (LAI), effective number of layers (ENL), vegetation cover, number of gaps. Results from a Principal Component Analysis (PCA) indicated number of gaps to be a major driver associated with the occurrence of oil palm plantations, while higher values of key structural metrics, such as top height, LAI and ENL were strongly linked with the presence of secondary tropical forest plots. Furthermore, a clear separation in metrics behaviour between forest and oil palm plots was evident from the pairwise comparison of these metrics, with rubber and shrubland plots behaving similarly to either forests or oil palm plantings according to different metrics. Our results show clear distinctions in several structural attributes among different LULC, which indicate the need for careful considerations regarding the impact of land-use change on ecosystem functioning, biodiversity and climate.</p>

This paper concerns the study of the seafloor through digital seabed elevation models, using object based image analysis methods. The goal of this research was the automated extraction of geomorphological features from the seabed in regions presenting intense volcanic activity. The study area is located around the submarine volcano of the Kolοumbo (in the submarine area northeast of the Santorini island, Greece). For this purpose, a Digital Elevation Model (DEM) of the seabed with a spatial resolution of 50m was used. Derivatives of the DEM, such us Slope, Topographic Position Index (TPI) and Terrain Ruggedness Index (TRI) were created in the open source software "QGIS 2.4". The implementation of the object based image analysis approach was performed in eCognition 8.7 software. Nine segmentation and classification levels were created in order to produce the final level segmentation "level 5", where the final geomorphological features were classified. The results of the method were evaluated using classification stability measures and qualitative and quantitative comparison of the results with existing map.