Relative Topographic Position Research Articles

Spatial configuration of patches with different ecological maturity: Implications for service supply at the landscape level

Traditional agrarian landscapes have been managed over centuries to provide complementary ecosystem services (provision and regulation) in a sustainable manner. The spatial arrangement of patches in these landscapes seems to connect ecosystems of different maturity that complement each other functionally, through exchanges of matter and energy, optimizing provisioning services supply while minimizing management effort (e.g., water and fertilizers supply). In this study we explored the implications that the spatial pattern of patches with different degrees of maturity (grasslands, scrublands, and oak groves) may have on service provision within an agrarian multifunctional landscape. To assess the ecological maturity of the evaluated patches, we sampled biotic and abiotic variables related to compositional and structural complexity of the plant community, as well as soil characteristics. Our results show that less mature ecosystems (grasslands) adjacent to the most mature ones (oak groves) had a higher structural complexity of the plant community than those adjacent to ecosystems with intermediate maturity (scrublands), which could be associated to a higher resource flow from oak groves. Furthermore, the relative topographic position of oak groves and scrublands influenced the ecological maturity of grasslands. Grasslands topographically located below oak groves and scrublands had more herbaceous biomass and fertile soils than grasslands located above them, which suggests that resource flow is accelerated by gravitational forces. This indicates that grassland patches can have higher human exploitation rates when located below the more mature patches, which can increase agricultural provisioning services (e.g., biomass extraction). Overall, our findings suggest that agrarian provisioning services can be improved by spatially arranging the patches that provide such services (e.g., grasslands) in the landscape, as well as those patches responsible for ecosystem regulating services, such as water flow regulation and material accumulation (e.g., forests).

How tree species, tree size, and topographical location influenced tree transpiration in northern boreal forests during the historic 2018 drought.

Trees in northern latitude ecosystems are projected to experience increasing drought stress as a result of rising air temperatures and changes in precipitation patterns in northern latitude ecosystems. However, most drought-related studies on high-latitude boreal forests (>50°N) have been conducted in North America, with few studies quantifying the response in European and Eurasian boreal forests. Here, we tested how daily whole-tree transpiration (Q, Litersday-1 ) and Q normalized for mean daytime vapor pressure deficit (QDZ , Liters day-1 kPa-1 ) were affected by the historic 2018 drought in Europe. More specifically, we examined how tree species, size, and topographic position affected drought response in high-latitude mature boreal forest trees. We monitored 30 Pinus sylvestris (pine) and 30 Picea abies (spruce) trees distributed across a topographic gradient in northern Sweden. In general, pine showed a greater QDZ control compared to spruce during periods of severe drought (standardized precipitation-evapotranspiration index: SPEI<-1.5), suggesting that the latter are more sensitive to drought. Overall, QDZ reductions (using non-drought QDZ as reference) were less pronounced in larger trees during severe drought, but there was a species-specific pattern: QDZ reductions were greater in pine trees at high elevations and greater in spruce trees at lower elevations. Despite lower QDZ during severe drought, drought spells were interspersed with small precipitation events and overcast conditions, and QDZ returned to pre-drought conditions relatively quickly. This study highlights unique species-specific responses to drought, which are additionally driven by a codependent interaction among tree size, relative topographic position, and unique regional climate conditions.

Habitat mapping of remote coasts: Evaluating the usefulness of lightweight unmanned aerial vehicles for conservation and monitoring

Unmanned aerial vehicles (UAVs) have the potential to be an important tool providing low-cost but sufficiently precise mapping products to support environmental management. In this study, we present possible applications of UAVs to map and monitor three representative coastal tropical habitats: mangroves, rocky shores and coral reefs. We conducted UAVs surveys in a Marine Protected Area (MPA) of the tropical eastern Pacific region to investigate the suitability and usefulness of using this tool in a remote area for a variety of management and monitoring purposes. For mangrove ecosystems, we evaluated the potential of UAV-derived data to estimate canopy cover. On an intertidal rocky shore, we evaluated the potential of UAVs to obtain a detailed relative topographic position index that can be used to correlate the distribution patterns of resident and transient fauna. Finally, we compared the standard diver-based coral reef mapping approach used at the MPA with the use of a map produced with the UAV. Our results suggest that the use of UAVs by conservation practitioners in MPAs with diverse habitats, such as in the tropics, is likely to improve the knowledge of the MPAs environments and provide highly detailed information for monitoring helping to understand the nursery function of these inter-connected tropical habitats, at a reduced cost. This tool, therefore, has the potential to support conservation measures in a more effective way.

Modelling End-of-Season Soil Salinity in Irrigated Agriculture Through Multi-temporal Optical Remote Sensing, Environmental Parameters, and In Situ Information

Accurate information of soil salinity levels enables for remediation actions in long-term operating irrigation systems with malfunctioning drainage and shallow groundwater (GW), as they are widespread throughout the Aral Sea Basin (ASB). Multi-temporal Landsat 5 data combined with GW levels and potentials, elevation and relative topographic position, and soil (clay content) parameters, were used for modelling bulk electromagnetic induction (EMI) at the end of the irrigation season. Random forest (RF) regressionwas applied to predict in situ observations of 2008–2011 which originated from a cotton research station in Uzbekistan. Validation, i.e. median statistics from 100 RF runs with a holdout of each 20% of the samples, revealed that mono-temporal (R2: 0.1–0.18, RMSE: 16.7–24.9 mSm−1) underperformed multi-temporal RS data (R2: 0.29–0.45; RMSE: 15.1–20.9 mSm−1). Combinations of multi-temporal RS data with environmental parameters achieved highest accuracies (R2: 0.36–0.50, RMSE: 13.2–19.9 mSm−1). Beside RS data recorded at the initial peaks of the major irrigation phases, terrain and GW parameters turned out to be important variables for the model. RF preferred neither raw data nor spectral indices known to be suitable for detecting soil salinity. Unexplained variance components result from missing environmental variables, but also from processes not considered in the data. A calibration of the EMI for electrical conductivity and the standard soil salinity classification returned an overall accuracy of 76–83% for the period 2008–2011. The presented indirect approach together with the in situ calibration of the EMI data can support an accurate mapping of soil salinity at the end of the season, at least in the type of irrigation systems found in the ASB.

Evaluating metrics of local topographic position for multiscale geomorphometric analysis

The field of geomorphometry has increasingly moved towards the use of multiscale analytical techniques, due to the availability of fine-resolution digital elevation models (DEMs) and the inherent scale-dependency of many DEM-derived attributes such as local topographic position (LTP). LTP is useful for landform and soils mapping and numerous other environmental applications. Multiple LTP metrics have been proposed and applied in the literature; however, elevation percentile (EP) is notable for its robustness to elevation error and applicability to non-Gaussian local elevation distributions, both of which are common characteristics of DEM data sets. Multiscale LTP analysis involves the estimation of spatial patterns using a range of neighborhood sizes, traditionally achieved by applying spatial filtering techniques with varying kernel sizes. While EP can be demonstrated to provide accurate estimates of LTP, the computationally intensive method of its calculation makes it unsuited to multiscale LTP analysis, particularly at large neighborhood sizes or with fine-resolution DEMs. This research assessed the suitability of three LTP metrics for multiscale terrain characterization by quantifying their computational efficiency and by comparing their ability to approximate EP spatial patterns under varying topographic conditions. The tested LTP metrics included: deviation from mean elevation (DEV), percent elevation range (PER), and the novel relative topographic position (RTP) index. The results demonstrated that DEV, calculated using the integral image technique, offers fast and scale-invariant computation. DEV spatial patterns were strongly correlated with EP (r2 range of 0.699 to 0.967) under all tested topographic conditions. RTP was also a strong predictor of EP (r2 range of 0.594 to 0.917). PER was the weakest predictor of EP (r2 range of 0.031 to 0.801) without offering a substantial improvement in computational efficiency over RTP. PER was therefore determined to be unsuitable for most multiscale applications. It was concluded that the scale-invariant property offered by the integral image used by the DEV method counters the minor losses in robustness compared to EP, making DEV the optimal LTP metric for multiscale applications.

Evaluation of a spatially adaptive approach for land surface classification from digital elevation models

ABSTRACTClassification of land surface to landforms is fundamental to interpretation of various environmental processes. The heterogeneous landform descriptions and classification approaches, in combination with the scale dependence of digital elevation models (DEMs) and their products, defy the development of an interoperable and transferable automated landform classification approach. A theoretical framework has proposed that land surface should be regionalised to morphologic meaningful objects, delimited by discontinuities (i.e. slope breaks and inflections) and subsequently classified with morphometric and contextual criteria. However, an automated methodology meeting these conditions is still lacking. This study is an attempt to automate this framework through the investigation of a modified version of a spatially adaptive pattern-based approach and its potential to produce morphologic meaningful objects of various shapes and sizes, present at the given DEM resolution. These objects were classified to 15 landform element classes based on semantic descriptions, including criteria of morphometry, relative topographic position and topological relations. Results were visually analysed by draping them over DEMs and contours and quantitatively assessed with fuzzy classification tools. The modified pattern-based approach was proven to be efficient for delineation of morphologic meaningful objects in DEMs. The classification approach was transferable to various landscapes and DEM resolutions, given that it uses spatially flexible fuzzy criteria.

Semi-automated object-based landform classification modelling in a part of the Deccan Plateau of central India

ABSTRACTLandform mapping holds significance in governing boundary conditions for the underlying processes operative in the fields of natural resource management, yet the automation in recognizing landform occurrence remains difficult. Geospatial object-based image analysis (GEOBIA) technique has evolved as a promising tool for addressing the issue. Majority of the GEOBIA-based landform classification studies document generic approach. The present study undertaken in Katol Tehsil of Nagpur District, a part of Deccan Plateau of central India aims at knowledge-based modelling through a multi-scale mapping workflow comprising multi-resolution segmentation (input raster dataset of IRS-P6 LISS-IV image and Cartosat-1 digital terrain model), knowledge-based classification, and accuracy assessment against a reference landform map. Contour- and drainage-based relative topographic position zone is derived in a novel attempt. Finally, knowledge-based rules are framed using the primary terrain parameters of elevation, slope, profile curvature, and drainage for deriving final output. The results of landform classification indicate the dominance of erosive landform over depositional one; maximum area of 6244 ha being under pediment. An accuracy assessment exercise is carried out in a watershed occurring in the study area, which shows very good statistical agreements between modelled and reference landforms including partial detection. The key constraint of this knowledge-based modelling is its limited adaptability to only localized conditions. However, such kind of object-based and knowledge-based analyses have immense potential with the increasing availability of finer resolution remote-sensing data products that demand the alternative paths of deriving objects that are made up of several pixels.

Object-based classification of global undersea topography and geomorphological features from the SRTM30_PLUS data

The analysis of undersea topography and geomorphological features provides necessary information to related disciplines and many applications. The development of an automated knowledge-based classification approach of undersea topography and geomorphological features is challenging due to their multi-scale nature. The aim of the study is to develop and evaluate an automated knowledge-based OBIA approach to: i) decompose the global undersea topography to multi-scale regions of distinct morphometric properties, and ii) assign the derived regions to characteristic geomorphological features. First, the global undersea topography was decomposed through the SRTM30_PLUS bathymetry data to the so-called morphometric objects of discrete morphometric properties and spatial scales defined by data-driven methods (local variance graphs and nested means) and multi-scale analysis. The derived morphometric objects were combined with additional relative topographic position information computed with a self-adaptive pattern recognition method (geomorphons), and auxiliary data and were assigned to characteristic undersea geomorphological feature classes through a knowledge base, developed from standard definitions. The decomposition of the SRTM30_PLUS data to morphometric objects was considered successful for the requirements of maximizing intra-object and inter-object heterogeneity, based on the near zero values of the Moran's I and the low values of the weighted variance index. The knowledge-based classification approach was tested for its transferability in six case studies of various tectonic settings and achieved the efficient extraction of 11 undersea geomorphological feature classes. The classification results for the six case studies were compared with the digital global seafloor geomorphic features map (GSFM). The 11 undersea feature classes and their producer's accuracies in respect to the GSFM relevant areas were Basin (95%), Continental Shelf (94.9%), Trough (88.4%), Plateau (78.9%), Continental Slope (76.4%), Trench (71.2%), Abyssal Hill (62.9%), Abyssal Plain (62.4%), Ridge (49.8%), Seamount (48.8%) and Continental Rise (25.4%). The knowledge-based OBIA classification approach was considered transferable since the percentages of spatial and thematic agreement between the most of the classified undersea feature classes and the GSFM exhibited low deviations across the six case studies.

Topographic and Bioclimatic Determinants of the Occurrence of Forest and Grassland in Tropical Montane Forest-Grassland Mosaics of the Western Ghats, India.

The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

An integral image approach to performing multi-scale topographic position analysis

Digital elevation model (DEM) derived measures of terrain ruggedness and relative topographic position are useful parameters for automated landform classification and are widely applied in soils, vegetation, and habitat mapping. These elevation residual attributes are inherently scale dependent because they are defined in the context of a local neighborhood. Several previous studies have focused on assessing the multi-scale properties of elevation residuals based on varying roving window sizes, DEM grid resolution resampling, and hierarchical object-based methods. The computationally intensive nature of large-window DEM filtering has previously prevented the application of the varying roving window size approach from being applied to study the scaling properties of the terrain ruggedness and topographic position at broader regional scales.This paper explores the use of an integral image based approach to measuring the common relative topographic position metric deviation from mean elevation (DEV). The approach was applied to a large DEM of an extensive and heterogeneous region in eastern North America. Compared with traditional image filtering techniques, the integral image approach was extremely efficient for calculating DEV, enabling a fine-resolution multi-scale analysis of elevation residuals. A method is described to allow for the measurement of DEV at optimal scales for each grid cell within wide spatial scale ranges. A novel technique is also developed for visualizing the scaling characteristics of topographic position using color composite imagery.

Analyzing Spatial Autocorrelation for the Hypsometric Integral to Discriminate Neotectonics and Lithologies Using DEMs and GIS

This investigation documents the sensitivity of the hypsometric integral (HI) and its relationship to neotectonics and lithology. We used digital elevation models (DEMs) of 30 and 90 m spatial resolution from the Shuttle Radar Topographic Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to calculate HI values in the Hindu Kush and its vicinity (Northwest Pakistan and Northeast Afghanistan). We used an analysis grid of regular squares of different sizes to calculate maximum, minimum, and mean elevations. The spatial distributions of HI do not show clear spatial patterns and correlation with mean elevation or relief amplitude. We applied spatial pattern analysis using Local Indices of Spatial Autocorrelation (LISA) to measure the degree to which our HI distribution was clustered, dispersed, or randomized. LISA analysis shows that the data are autocorrelated because of high positive z-scores. Hot spots (clusters with high HI values) are consistent with tectonic uplift and show a strong correlation with the different structural domains in the region. Cold spots represent recent sedimentation close to faults and coincide with shallow earthquake clusters in the region. The HI values do not show any correlation with relative topographic position or lithology. Analysis of HI distribution shows that they are robust and independent of digital elevation model (DEM) resolution but are strongly scale dependent. The LISA technique allows the extraction of clusters of the HI that reveal recent tectonic processes; otherwise it is difficult to interpret the high variability of HI values. The scale dependency of the HI may reflect the varying importance of drainage network and hillslope processes.

Measuring the relative topographic position of archaeological sites in the landscape, a case study on the Bronze Age barrows in northwest Belgium

Local topography is an important parameter determining the erection of a certain type of site on a certain location in the landscape. Despite the importance of topography in archaeological landscape research, the role of local topography has remained rather unexplored compared to other specific topographic parameters such as slope, aspect, curvature or visibility. Therefore, three methods to assess the relative topographic position of sites are applied and discussed here. The Bronze Age barrow dataset of northwest Belgium acts as the subject for this methodological case study. First, elevation percentile calculates the area that is lower than the central point within a predetermined neighborhood. Secondly, difference from mean elevation measures the relative topographic position of the central point as the difference between the elevation of this central point and the mean elevation within a predetermined neighborhood. And finally, deviation from mean elevation calculates the relative topographic position of the central point as the difference from mean elevation divided by the standard deviation of elevation, within a predetermined neighborhood. These three methods, each with their advantages and disadvantages, prove to be an added value for archaeological landscape research.

Geographic Information Systems (GIS) analysis of high-resolution multibeam bathymetry and remotely operated vehicle data to model rockfish habitat preference

Management of declining rockfish stocks requires effective tools capable of providing accurate stock assessments of near-shore, high-relief habitat. Multibeam bathymetry, when analyzed with GIS landscape analysis tools, can create models which can identify preferred habitat based on species-specific parameters. For this study, high-resolution multibeam data of the Del Monte shale beds in Monterey Bay, California were analyzed in GIS for slope, rugosity, and relative topographic position to assess rockfish (Sebastes spp.) habitat preference. Video transects collected by a remotely operated vehicle (ROV) provided habitat ground-truth and fish distribution data. A series of habitat suitability models was created in GIS by combining different suitability factors from multibeam-derived grids: slope, rugosity, topographic position index (TPI), and depth. Distance to preferred categories for each parameter were determined for eight rockfish species. Of these, distance to peak features identified by TPI50 proved the most effective means of modeling fish distribution, successfully predicting an average of 80% of the eight rockfish species. Using fish distribution information, stock estimates were calculated for the study area. By combining GIS landscape analysis tools with multibeam bathymetry and ROV video data, we have created a predictive tool that can locate areas of most suitable habitat given rockfish-specific parameters.

An investigation of the spatial association between snow depth and topography in a Prairie agricultural landscape using digital terrain analysis

The association of snowcover depth with topography and land cover was examined for a 1.5 km 2 agricultural site in the Canadian Prairies. Topography was quantified by a suite of general geomorphometric variables that were measured directly by the analysis of a raster digital elevation model (DEM) with a 10 m grid cell size. These variables expressed local surface morphology and relative topographic position. The major land covers represented in the study area were wheat stubble and summerfallow. Point observations of snow depth at the site were obtained near the time of peak snow accumulation and during melt. Initial analysis was made of scatterplots and correlations between snow depth and individual topographic variables. These correlations were weak but illustrated that, at the local scale, snow depth patterns cannot be adequately modelled through simple bivariate relationships with topographic variables. The correlations also showed that snow depth is more strongly related to relative topographic position variables than to variables expressing local surface morphology. Subsequent analysis involved a divisive subdivision of the study site into six terrain units defined in terms of several relative topographic position and land cover variables. The units were used to model the spatial patterns of snow depth that occurred during the observed snowcover situations. Although the terrain classification only reduced the total snow depth variance by approximately 30%, it delineated the major patterns of snow depth observed over the study area and elucidated the spatial relationships between snowcover depth and landscape attributes.

Superposed neoglacial and late Pinedale (Wisconsinan) tills, Titcomb Basin, Wind River Mountians, western Wyoming

Late Pinedale (Wisconsinan) and Neoglacial deposits mantle most valley floors above the timberlane in the Wind River Mountains. Although these tills are generally assigned ages by radiocarbon, relative topographic position, weathering characteristics, and soil morphology, they are rarely superposed. This paper describes a sequence of moraines in Titcomb Basin where early Neoglacial till (now called Indian Basin Till) is superposed on a moraine of late Pinedale age, providing important information on relative ages for the two tills. Late Pinedale deposits radiocarbon date at ca. 8000 yr. B.P., from bog bottom organic materials. Time of onset of Neoglaciation is unknown, while the termination of the Indian Basin advance is placed at ca. 3000 yr. B.P. Differences in soil profile morphology, particle size, mineralogy and chemistry are described and discussed with respect to relative age assignment. While profile morphology and particle size differences appear important in differentiating the two soils, differences in clay mineral composition appear to have greater utility in discriminating post-Indian Basin soils from post-late Pinedale soils.

GROUNDWATER DISCHARGE: A COMMON GENERATOR OF DIVERSE GEOLOGIC AND MORPHOLOGIC PHENOMENA

Abstract Four natural conditions co-exist inherently with the hydraulic discharge of groundwater: positive gradient of the fluid potential, low relative topographic position, allochthonous water quality, and allochthonous water temperature. In turn, these conditions are consistently associated with springs, seepages, quicksand, soap holes, geysers, frost mounds, pingos, groundwater lakes and marshes, and certain near-surface and surface accumulations of salts, landslides, slumps, soil creep and gullying, which is considered as an indication that these features have one common generator: groundwater discharge. The above geomorphic and geologic phenomena are, therefore, interpreted as environmentally modified expressions of the hydraulic discharge of groundwater through topographically controlled gravity flow systems.