Topographic Openness Research Articles

Remotely mapping gullying and incision in Maryland Piedmont headwater streams using repeat airborne lidar

Headwater streams can contribute significant amounts of fine sediment to downstream waterways, especially when severely eroded and incised. Potential upstream sediment source identification is crucial for effective management of water quality, aquatic habitat, and sediment loads in a watershed. This study explored topographic openness (TO) derived from 1-m lidar for its ability to predict incision in headwater streams and to remotely detect changes in incision over time. Field surveys were conducted in one forested and two recently urbanized headwater watersheds in the Maryland Piedmont physiographic province, USA to characterize the level of stream channel incision (none, moderate, or severe) in the main stem of each watershed. Predictions of the severity of stream channel incision derived from TO were compared against the field surveys. Channel incision was detected with an overall accuracy of 67 %, with best performance in reaches with either severe or no incision (79–86 % accuracy). The method was also applied to repeat lidar collected over the same area to model the extent of channel incision in 2002 before urban development began and in 2008 and 2013 during active construction in the urban watersheds. Results showed increasing incision over time in all three watersheds, with similar patterns in the forested and urban watersheds. This new method of remotely measuring channel incision can be used to identify potential sediment sources across a watershed, enhance water and habitat quality predictions, and detect changes over time where multiple years of overlapping lidar are available.

Changes in assemblage and functional composition of benthos in response to changes in tidal flat environments including topographic openness on islands in the Seto Inland Sea

Changes in assemblage and functional composition of benthos in response to changes in tidal flat environments including topographic openness on islands in the Seto Inland Sea

Gully Head-Cuts Inventory and Semi-Automatic Gully Extraction Using LiDAR and Topographic Openness—Case Study: Covurlui Plateau, Eastern Romania

The Covurlui Plateau, a subunit of the Moldavian Plateau located in eastern Romania, possesses a high natural agricultural potential, significantly impacted by soil erosion, particularly gully erosion. The only inventory in the Moldavian Plateau that comprises approximately 9000 gullies extracted from topographical maps was conducted during the 90s. Nowadays, with the advent of advanced techniques and geodata, such as GIS software, aerial photograms, high-resolution satellite images, and high-resolution digital elevation models, we aim to achieve an (1) up-to-date comprehensive inventory of gully head-cuts and (2) a very detailed mapping of the spatial distribution of gullied lands. Firstly, the gully head-cuts were inventoried for the entire region using platforms such as Google, Esri, and Bing, through the QuickMapService plugin within QGIS 3.16 software, with the assistance of Landsat and Sentinel satellite images. Secondly, the automatic mapping of gullies was carried out using a 5 m high-resolution Digital Elevation Model and the Topographic Openness module offered by SAGA GIS software through QGIS software. As a result, we identified 5868 gully head-cuts for the Covurlui Plateau, with an average density of 2.57 gully head-cuts per square kilometer. Additionally, the identified gullies occupy over 3570 hectares, representing 1.57% of the total area. Overall, the topographic openness index proves to be an efficient tool for the semi-automatic extraction of gullies from high-resolution digital elevation models.

The relationship between ignimbrite lithofacies and topography in a foothill setting formed on Miocene pyroclastics – a case study from the Bükkalja, Northern Hungary

Units with extremely variable erodibility are typical in the succession of pyroclastic-dominated volcanic fields. Welded ignimbrites are usually resistant to erosion, thus, they often appear as positive landforms, i.e., mesas or tilted plateaus after millions of years of denudation. The Bükkalja Volcanic Area being part of the most extended foothill area of the North Hungarian Mountains, is composed predominantly of Miocene ignimbrites, where the frequency distributions of elevation a.s.l., slope, aspect, as well as topographic openness, were investigated using a 30 m resolution SRTM-based digital surface model at four sample areas located at different relative distances from the assumed source localities of the ignimbrites, showing both non-welded and welded facies. The degree of dissection was also examined along swath profiles. The topography of the sample area closest to the source localities is dominated by slabs of moderately dissected welded ignimbrites, gently dipping towards SE. Farther away from the source the topography is dominated by erosional valleys and ridges, resulting in a narrower typical elevation range, a higher proportion of pixels with greater than 5° slope, higher frequencies of NE and SW exposures, and more significant incision resulted in more frequent pixels with positive topographic openness less than 1.5 radians here. Higher thicknesses and emplacement temperatures of ignimbrites, often showing welded facies are more common closer to the source vent. Thus, the erosional pattern around calderas can be used to draw conclusions on the spatial extent of the most intense ignimbrite accumulation, i.e., the location of eruption centres even in highly eroded ignimbrite fields.

Ephemeral Stream Network Extraction from Lidar‐Derived Elevation and Topographic Attributes in Urban and Forested Landscapes

AbstractUnder‐representations of headwater channels in digital stream networks can result in uncertainty in the magnitude of headwater habitat loss, stream burial, and watershed function. Increased availability of high‐resolution (<2 m) elevation data makes the delineation of headwater channels more attainable. In this study, elevation data derived from light detection and ranging was used to predict ephemeral stream networks across a forested and urban watershed in the Maryland Piedmont USA. A method was developed using topographic openness (TO) and wetness index to remotely predict the extent of stream networks. Predicted networks were compared against a comprehensive field survey of the ephemeral network in each watershed to evaluate performance. Comparisons were also made to the U.S. Geological Survey National Hydrography Dataset (NHD) and a flow accumulation approach where a single drainage area threshold defined channel initiation. Although the NHD and flow accumulation methods resulted in low commission errors, omission errors were highest in these networks. The TO‐based networks detected a larger number of ephemeral channels, but with higher commission error. Small ephemeral channels with less defined banks or originating at groundwater seeps were difficult to detect in all methods. Comparisons between forested and urban watersheds also highlight the difficulty of identifying headwater channels using topographic attributes in human‐modified landscapes.

Topographic Analysis of Intertidal Polychaete Reefs (Sabellaria alveolata) at a Very High Spatial Resolution

In temperate coastal regions of Western Europe, the polychaete Sabellaria alveolata (Linné) builds large intertidal reefs of several hectares on soft-bottom substrates. These reefs are protected by the European Habitat Directive EEC/92/43 under the status of biogenic structures hosting a high biodiversity and providing ecological functions such as protection against coastal erosion. As an alternative to time-consuming field campaigns, a UAV-based Structure-from-Motion photogrammetric survey was carried out in October 2020 over Noirmoutier Island (France) where the second-largest known European reef is located in a tidal delta. A DJI Phantom 4 Multispectral UAV provided a topographic dataset at very high resolutions of 5 cm/pixel for the Digital Surface Model (DSM) and 2.63 cm/pixel for the multispectral orthomosaic images. The reef footprint was mapped using a combination of two topographic indices: the Topographic Openness Index and the Topographic Position Index. The reef structures covered an area of 8.15 ha, with 89% corresponding to the main reef composed of connected and continuous biogenic structures, 7.6% of large isolated structures (<60 m2), and 4.4% of small isolated reef clumps (<2 m2). To further describe the topographic complexity of the reef, the Geomorphon landform classification was used. The spatial distribution of tabular platforms considered as a healthy stage of the reef in contrast to a degraded stage was mapped with a proxy that consists in comparing the reef volume to a theoretical tabular-shaped reef volume. Epibionts colonizing the reef (macroalgae, mussels, and oysters) were also mapped by combining multispectral indices such as the Normalised Difference Vegetation Index and simple band ratios with topographic indices. A confusion matrix showed that macroalgae and mussels were satisfactorily identified but that oysters could not be detected by an automated procedure due to their spectral complexity. The topographic indices used in this work should now be further exploited to propose a health index for these large intertidal reefs.

Landslide Susceptibility Assessment Using Bivariate Method: A Case Study from River Neelum and Jehlum Catchment Area

Landslide is a frequently occurring natural calamity in the northern areas of Pakistan. The current study is aimed to assess the susceptibility of landslide hazard to highlight the vulnerable areas for the purpose of risk reduction along Neelum and Jehlum rivers in district Muzaffarabad. A data-driven predictive approach was adopted to conduct this study by using Weight of Evidence (WOE) model along with eleven conditioning factors. A spatial distribution map of landslides was prepared using orthophoto, previous records, and derivatives (hill shad, topographic openness, slope, aspect, curvature) of Digital Elevation Model (DEM). The results show that the roads, lithology, and rivers are the most important triggering factors for landslides in both valleys. Approximately 30% of the area is under low susceptibility zone in Jhelum valley while only 13% of the area falls under low susceptibility zone in Neelum river valley. In Jhelum river valley the medium susceptibility zone covers 35% of the total area whereas, Neelam river valley has 26% of the total area under medium susceptibility zone. Around 61% of the land in the Neelam river valley and 35% of the land in the Jhelum river valley are under high susceptibility zone. The area under high hazard lies in the north-east of the district due to multiple conducive factors to trigger landslides including weak lithology (mudstone, sandstone, shales, and clays), high altitude along steep slopes and excessive precipitation (1800 mm/ year). Furthermore, the high hazard zone in study area is not suitable for construction purpose but was suitable for plantation. The validation result (89.41%) is justifying the performance of this model.

SPATIAL PREDICTION MODELS FOR LANDSLIDE ACTIVITY MAPPING USING VEGETATION ANOMALIES

Abstract. An area that located in Kundasang which in Ranau district in Sabah, Malaysia that lies along the bank of Kundasang valley was chosen for comparing the reliability of frequency ratio (FR) and weight of evidence (WoE) methods for landslide activity probability mapping by using related vegetation anomalies indicator. The locations of 47 and 189 of active and dormant landslides respectively were identified using 4 raster layers (topographic openness, hillshade, colour composite and high resolution orthophoto). Each landslide activites were randomly divided into two groups as training (70%) and testing (30%) datasets. Tree height irregularities, DVI, NDVI, SAVI, and OSAVI were considered as landslide bio-indicator. The landslide activity probability maps were prepared using the FR and WoE method. The generated maps were validated by calculating the success and prediction rates from area under receiver operating characteristics (ROC) curve. The results of WoE method were relatively reliable (AUC > 0.8) for dormant landslide while only about 40% of active landslide have been predicted accurately. Similar trend yielded for FR method where least accuracy for active landslide prediction.

Turizm ve Rekreasyon Faaliyetlerinin Ekonomi Üzerindeki Etkileri: Antalya Örneği

In the past, landforms were represented in physiographic and morphometric maps by hand drawing. With developments in digital elevation models (DEM), geographic information systems (GIS) and image analyses, automatic extraction of landforms from morphological parameters and data storage in databases is now possible and are actively utilized in various fields, such as geomorphology, soil science, and ecology. In the above scopes, DEM data forms the database of morphometric parameters, such as, relief, slope, curvatures, and topographic openness. Presently, calculation of parameters, implementation of relationships with landforms, scaling, classification methods, topological relations, homogeneity, and generalizations during the transformation of 3D components of landforms, such as mountains, peaks, slopes, valleys, and plain, into 2D geometric elements in computers are being investigated. In this study, the methods and applications for the automatic extraction of the landforms developed by researchers across different disciplines were reviewed. Classification methods were grouped as combined parameters method and unsupervised/supervised classification methods based on pixel/object. This paper emphasizes the importance of adopting machine learning to implement new models applicable to all terrains. 

ASSIMILATING GEOSPATIAL METAMODEL AND INVENTORY MAPPING FOR NON-STRUCTURAL MITIGATION OF LANDSLIDE

Abstract. In Malaysia, issues related to disaster management are always given attention in society and by the responsible parties. However, in general, citizen do not think of the consequential impact of disaster due to less of knowledge regarding the early phase in disaster management. Therefore, citizen in those areas will be more vulnerable to landslide as the citizen face difficulties in identifying specific areas with the tendency of landslides. This paper presents a geospatial metamodel approach for non-structural mitigation of landslide using data from airborne LiDAR and aerial photograph. Disaster management metamodel with geospatial element combines activity for managing disaster along with geospatial database that makes it handy for appreciating the metamodel. On the other hand, the digital terrain model (DTM) from LiDAR and aerial photograph is required to produce landslide inventory mapping. The case study area is located in Kundasang, Sabah, where landslides occur frequently. In order to get better visual in identifying landslides in the study area, three types of data are required to carry out image interpretation. The three types of data are hillshade, topographic openness and colour composite. The result of the landslide inventory map shows that there are five types of landslide, which is debris flow, debris fall, mud flow, deep-seated landslide and shallow landslide. Finally, the result of landslide inventory map will be integrated into the developed metamodel for presentation to the users. This landslide inventory map is used as a non-structural mitigation step in one of disaster management phases that is suitable to prepare and use in mitigating the landslide hazard impact.

Evaluating Concentrated Flowpaths in Riparian Forest Buffer Contributing Areas Using LiDAR Imagery and Topographic Metrics

Riparian forest (CP22) buffers are implemented in the Chesapeake Bay Watershed to trap pollutants in surface runoff thus minimizing the amount of pollutants entering the stream network. For these buffers to function effectively, overland flow must enter the riparian zones as dispersed sheet flow to facilitate slowing, filtering, and infiltrating of surface runoff. The occurrence of concentrated flowpaths, however, is prevalent across the watershed. Concentrated flowpaths limit buffer filtration capacity by channeling overland flow through or around buffers. In this study, two topographic metrics (topographic openness and flow accumulation) were used to evaluate the occurrence of concentrated flowpaths and to derive effective CP22 contributing areas in four Long-Term Agroecosystem Research (LTAR) watersheds within the Chesapeake Bay Watershed. The study watersheds include the Tuckahoe Creek watershed (TCW) located in Maryland, and the Spring Creek (SCW), Conewago Creek (CCW) and Mahantango Creek (MCW) watersheds located in Pennsylvania. Topographic openness identified detailed topographic variation and critical source areas in the lower relief areas while flow accumulation was better at identifying concentrated flowpaths in higher relief areas. Results also indicated that concentrated flowpaths are prevalent across all four watersheds, reducing CP22 effective contributing areas by 78% in the TCW, 54% in the SCW, 38% in the CCW and 22% in the MCW. Thus, to improve surface water quality within the Chesapeake Bay Watershed, the implementation of riparian forest buffers should be done in such a way as to mitigate the effects of concentrated flowpaths that continue to short-circuit these buffers.

A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation

A robust and flexible technique to segment seafloor acoustic mapping data by analyzing co-located bathymetric digital elevation models and acoustic backscatter mosaics is presented. The algorithm first uses principles of topographic openness, pattern recognition, and texture classification to identify geomorphic elements of the seafloor or “area kernels”, and then derives the final seafloor segmentation by merging or splitting the kernels based on principles of similarity and multi-modality. The output is a collection of homogeneous, non-overlapping seafloor segments of consistent morphology and acoustic backscatter texture. Each labeled segment is enriched by a list of derived, physically-meaningful attributes that can be used for subsequent task-specific analysis.

Multilevel spatiotemporal validation of snow/ice mass balance and runoff modeling in glacierized catchments

Abstract. In this study, the fully distributed, physically based hydroclimatological model AMUNDSEN is set up for catchments in the highly glacierized Ötztal Alps (Austria, 558 km2 in total). The model is applied for the period 1997–2013, using a spatial resolution of 50 m and a temporal resolution of 1 h. A novel parameterization for lateral snow redistribution based on topographic openness is presented to account for the highly heterogeneous snow accumulation patterns in the complex topography of the study region. Multilevel spatiotemporal validation is introduced as a systematic, independent, complete, and redundant validation procedure based on the observation scale of temporal and spatial support, spacing, and extent. This new approach is demonstrated using a comprehensive set of eight independent validation sources: (i) mean areal precipitation over the period 1997–2006 derived by conserving mass in the closure of the water balance, (ii) time series of snow depth recordings at the plot scale, (iii–iv) multitemporal snow extent maps derived from Landsat and MODIS satellite data products, (v) the snow accumulation distribution for the winter season 2010/2011 derived from airborne laser scanning data, (vi) specific surface mass balances for three glaciers in the study area, (vii) spatially distributed glacier surface elevation changes for the entire area over the period 1997–2006, and (viii) runoff recordings for several subcatchments. The results indicate a high overall model skill and especially demonstrate the benefit of the new validation approach. The method can serve as guideline for systematically validating the coupled components in integrated snow-hydrological and glacio-hydrological models.

Applying macrophyte community indicators to assess anthropogenic pressures on shallow soft bottoms

Vegetated soft bottoms are under pressure due to a number of anthropogenic stressors, such as coastal exploitation and eutrophication. The ecological value of these biotopes has gained recognition through international conventions and the EU directives, which request methods for assessment of the environmental status of coastal areas. However, currently there is no appropriate method for assessing the status of shallow vegetated soft bottoms in the northern Baltic Sea. Therefore, we developed a macrophyte community index and tested its response in relation to important pressures (eutrophication and boating activity) and natural gradients (topographic openness, depth and salinity) on shallow bays in the northern Baltic Sea. The macrophyte index, and hence the proportion of sensitive to tolerant species, decreased with increasing phosphorus concentration, turbidity and level of boating activity, while the cumulative cover of macrophytes only showed a negative trend in response to increasing turbidity. Juvenile fish abundance was positively related to the index, indicating importance of sensitive macrophyte species for ecosystem functioning. As the index was tested in a wide geographic area, and showed a uniform response across natural gradients, it is a promising tool for assessment of environmental status that may be applied also in other vegetated soft-bottom areas.

Modeling habitat and bycatch risk for dugongs in Sabah, Malaysia

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 24:237-247 (2014) - DOI: https://doi.org/10.3354/esr00600 Modeling habitat and bycatch risk for dugongs in Sabah, Malaysia Dana K. Briscoe1,*, Seth Hiatt2, Rebecca Lewison3, Ellen Hines2,4 1Stanford University, Hopkins Marine Station, 120 Ocean View Blvd, Pacific Grove, CA 93950, USA 2San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA 3San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA 4Marine & Coastal Conservation and Spatial Planning Lab, Romberg Tiburon Center for Environmental Studies, Tiburon, CA 94920, USA *Corresponding author: dbriscoe@stanford.edu ABSTRACT: Bycatch of marine megafauna in fishing gear is a problem with global implications. Bycatch rates can be difficult to quantify, especially in countries where there are limited data on the abundance and distribution of coastal marine mammals, the distribution and intensity of fishing effort, and coincident interactions, and limited bycatch mitigation strategies. The dugong Dugong dugon is an IUCN-listed Vulnerable species found from the eastern coast of Africa to the western Pacific. As foragers of seagrass, they are highly susceptible to bycatch in small-scale fisheries. To address the knowledge gaps surrounding marine mammal bycatch, we used existing survey and fishing effort data to spatially characterize the risk of bycatch for this species. Using Sabah, Malaysia, as a case study, we employed presence-only modeling techniques to identify habitat associations of dugongs using a maximum entropy distribution model (MaxEnt) based on published sightings data and several geophysical parameters: coastal distance, depth, insolation, and topographic openness. Model outputs showed distance from the coast as the highest-contributing variable to the probability of dugong presence. Results were combined with previously published fishing effort maps of this area to develop a predictive bycatch risk surface. Our analyses identified several areas of high risk where dugong surveys were conducted, but also identified high-risk areas in previously unsurveyed locations. Such methods can be used to direct field activities and data collection efforts and provide a robust template for how existing sightings and fishing effort data can be used to facilitate conservation action in data-limited regions. KEY WORDS: Dugong · Dugong dugon · Habitat modeling · Spatial analysis · Fisheries · Bycatch · MaxEnt · Malaysia Full text in pdf format Supplementary material PreviousNextCite this article as: Briscoe DK, Hiatt S, Lewison R, Hines E (2014) Modeling habitat and bycatch risk for dugongs in Sabah, Malaysia. Endang Species Res 24:237-247. https://doi.org/10.3354/esr00600 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 24, No. 3. Online publication date: June 13, 2014 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2014 Inter-Research.

Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping

Semi-automated geomorphological mapping techniques are gradually replacing classical techniques due to increasing availability of high-quality digital topographic data. In order to efficiently analyze such large amounts of data, there is a need for optimizing the processing of automated mapping techniques. In this context, we present a novel approach to semi-automatically map alpine geomorphology using stratified object-based image analysis. We used a 1 m Digital Terrain Model (DTM) derived from laser altimetry data from a mountainous catchment from which we calculated various Land-Surface Parameters (LSPs). The LSPs ‘slope angle’ and ‘topographic openness’ have been combined into a single composite layer for selecting reference material and delineating training samples. We developed a novel method to semi-automatically assess segmentation results by comparing 2D frequency distribution matrices of training samples and image objects. The segmentation accuracy assessment allowed us to automate optimization of the scale parameter and LSPs used for segmentation. We concluded that different geomorphological feature types have different sets of optimal segmentation parameters. The feature-dependent parameters were used in a new approach of stratified feature extraction for classifying karst, glacial, fluvial and denudational landforms. In this way, we have used stratified object-based image analysis to semi-automatically extract contrasting geomorphological features from high-resolution digital terrain data. A further step would be to also automate the optimization of classification rules. We would then be able to create a library of feature characteristics that could be transferred and applied to other mountain regions and further automate geomorphological mapping strategies.

Characterization of volcanic geomorphology and geology by slope and topographic openness

We used a digital elevation model (DEM) of Iwate Volcano, Japan to calculate slope and topographic openness for every geological unit in that area. Further, we conducted principal component analysis (PCA) and stepwise discriminant analysis (SDA) to evaluate statistical differences in the morphometric parameters of the geological units. We categorized the geological units according to their ages and their formation processes. The categorization based on formation processes was as follows: lava-flow/lava, central cone/stratocone, air fall deposit, pyroclastic flow deposit, volcanic fan deposit, and debris avalanche deposit. PCA allowed us to select geological units suitable for analysis, while SDA was used to select some morphometric parameters, which could explain the differences in the formation processes and ages of the geological units. We found that the formation processes correspond well to mean slope, and that the geological ages that control the degree of surface dissection correspond well to the standard deviations of slope and negative topographic openness.

Supervised landform classification of Northeast Honshu from DEM-derived thematic maps

This paper proposes a quantitative method to classify landforms using four morphometric parameters from DEM-derived thematic raster maps of slope and topographic openness. Because the different surficial processes and stages in the evolution of slopes create landscapes with different shapes, these parameters may lead to a genetic interpretation of topography. The raster maps of slope and topographic openness were constructed for Northeast Honshu, Japan, from 50-m DEMs. The mean and standard deviation of morphometric parameters within a 3050 m by 3050 m moving window on the raster maps were calculated. The results for some training areas show that constructional/depositional and erosional landforms with different relief have different morphometric characteristics. A supervised landform classification for Northeast Honshu using the knowledge from the training areas revealed a ladder geomorphological structure composed of high mountains, ranges and volcanoes. The close relationship between the ladder geomorphological structure and volcano distribution indicates that the structure reflects the magmatic plumbing system from the upper mantle to the crust of the Northeast Honshu arc.

Prediction of surface water turnover time in coastal waters using digital bathymetric information

AbstractThis paper presents a new type of geographical information system (GIS) for scientific planning of coastal waters. One hypothesis in the work is that the morphometry of the coast plays a significant role for how the water system functions as a receiving water system, for example, as a receiver of industrial and urban pollution and in response to various forms of aquaculture. A digital technique for transferring information from standard charts into morphometric parameters expressing various characteristics of the coast has been developed. Empirical data on surface water turnover times, which are costly and demanding to determine with traditional hydrodynamic methods, has been obtained from the literature for 20 defined Swedish coastal areas. Two models for simple predictions of the median surface water turnover time, based on morphometric parameters, have been developed to exemplify the use of “morphometrical models” in expressing a coast ecological key parameter. In these models, more than 90 per cent of the variation in empirical values of surface water turnover times can be statistically explained by the topographic openness. The topographic openness describes the exposure of the coastal area towards the open sea or adjacent coastal area. The models are valid for the temperature stratified period (May–October) in areas non‐affected by tides, strong coastal currents and river inflow. The areas should also be in the size range 0.15–150 km2.