Satellite–Derived Topography and Morphometry for VHR Coastal Habitat Mapping: The Pleiades–1 Tri–Stereo Enhancement

The evolution of the coastal fringe is closely linked to the impact of climate change, specifically increases in sea level and storm intensity. The anthropic pressure that is inflicted on these fragile environments strengthens the risk. Therefore, numerous research projects look into the possibility of monitoring and understanding the coastal environment in order to better identify its dynamics and adaptation to the major changes that are currently taking place in the landscape. This new study aims to improve the habitat mapping/classification at Very High Resolution (VHR) using Pleiades–1–derived topography, its morphometric by–products, and Pleiades–1–derived imageries. A tri–stereo dataset was acquired and processed by image pairing to obtain nine digital surface models (DSM) that were 0.50 m pixel size using the free software RSP (RPC Stereo Processor) and that were calibrated and validated with the 2018–LiDAR dataset that was available for the study area: the Emerald Coast in Brittany (France). Four morphometric predictors that were derived from the best of the nine generated DSMs were calculated via a freely available software (SAGA GIS): slope, aspect, topographic position index (TPI), and TPI–based landform classification (TPILC). A maximum likelihood classification of the area was calculated using nine classes: the salt marsh, dune, rock, urban, field, forest, beach, road, and seawater classes. With an RMSE of 4 m, the DSM#2–3_1 (from images #2 and #3 with one ground control point) outperformed the other DSMs. The classification results that were computed from the DSM#2–3_1 demonstrate the importance of the contribution of the morphometric predictors that were added to the reference Red–Green–Blue (RGB, 76.37% in overall accuracy, OA). The best combination of TPILC that was added to the RGB + DSM provided a gain of 13% in the OA, reaching 89.37%. These findings will help scientists and managers who are tasked with coastal risks at VHR.

High-resolution snow depth prediction using Random Forest algorithm with topographic parameters and an ecosystem map: a case study in the Greiner Watershed, Nunavut

Increased surface temperatures (0.7℃ per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties (depth and density) at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. As arctic snow is strongly influenced by vegetation, an ecotype map at 10 m resolution was added to a method with the Random Forest (RF) algorithm previously developed for alpine environments and applied here over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up-wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Ecotypes with taller vegetation with moister soils were found to have deeper snow because of the trapping effect. Using feature importance with RF, snow depth distributions were predicted from topographic and ecosystem parameters with a root mean square error = 8 cm (23%) (R² = 0.79) at 10 m resolution for an arctic watershed (1 500 km²) in western Nunavut, Canada.

Influence of Neighborhood Competition on Douglas-Fir Growth Is Not Altered by Local Environmental Conditions and Climate

To evaluate impacts of competitive conditions in tree neighborhoods on growth responses as influenced by moisture availability and local environmental conditions, we sampled 102 codominant 40- to 70-year-old coastal Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in regions with relatively high and low moisture availability. We quantified local environmental conditions (topographic position index, heat load, and soil depth), and the annual basal area increment and climate moisture deficits during two growth periods: a five-year period prior to commercial thinning and years 6 to 10 after thinning. In both regions and growth periods, tree growth was higher for trees growing in local neighborhoods with lower competition. The density/growth relationships differed by region and by growth period in the moist regions, but they were not influenced by climate moisture deficit. Furthermore, including topographic position index, heat load, or soil depth did not improve model support. Our results highlight the importance of managing local competition and indicate that environmental factors such as soil depth, heat load, and topography may be less likely to warrant consideration when developing thinning prescriptions. This could allow foresters to accommodate other ecosystem services when designing density management treatments, at least within typical growing conditions. Study Implications: Concerns about climate change have led to questions whether existing management practices, such as current thinning prescriptions, need to be modified to ensure sustainable provision of ecosystem services. Our results highlight the prominent role of local competitive conditions and indicate that fine-scale differences in topography and soils within our study region are not useful criteria for modifying thinning prescriptions to alter how trees responds to climate conditions, at least under typical growing conditions. Thus, foresters can focus their prescriptions on other aspects when developing thinning or other partial harvesting operations, such as timber production or wildlife habitat.

A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.

Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories

Landslides are a key hazard in high-relief areas around the world and pose a risk to population and infrastructure. It is important to understand where landslides are likely to occur in the landscape to inform local analyses of exposure and potential impacts. Large triggering events such as earthquakes or major rain storms often cause hundreds or thousands of landslides, and mapping the landslide populations generated by these events can provide extensive datasets of landslide locations. Previous work has explored the characteristic locations of landslides triggered by seismic shaking, but rainfall induced landslides are likely to occur in different parts of a given landscape when compared to seismically induced failures. Here we show measurements of a range of topographic parameters associated with rainfall-induced landslides inventories, including a number of previously unpublished inventories which we also present here. We find that average upstream angle and compound topographic index are strong predictors of landslide headscarp location, while local relief and topographic position index provide a stronger sense of where landslide material may end up (and thus where hazard may be highest). By providing a large compilation of inventory data for open use by the landslide community, we suggest that this work could be useful for other regional and global landslide modelling studies and local calibration of landslide susceptibility assessment, as well as hazard mitigation studies.

Evolution of Habitat Quality and Its Topographic Gradient Effect in Northwest Hubei Province from 2000 to 2020 Based on the InVEST Model

Regional land use change and ecological security are important fields and have been popular issues in global change research in recent years. Regional habitat quality is also an important embodiment of the service function and health of ecosystems. Taking Shiyan City of Hubei Province as an example, the spatiotemporal differences in habitat quality in Shiyan City were evaluated using the habitat quality module of the InVEST model and GIS spatial analysis method based on DEM and land use data from 2000, 2005, 2010, 2015, and 2020. According to the habitat quality index values, the habitats were divided into four levels indicating habitat quality: I (very bad), II (bad), III (good), and IV (excellent), and the topographic gradient effect of habitat quality was studied using the topographic position index. The results show the following. (1) The habitat quality of Shiyan City showed relatively high and obvious spatial heterogeneity overall and, more specifically, was high in the northwest and southwest, moderate in the center, and low in the northeast. The higher quality habitats (levels III, IV) were mainly distributed in mountain and hill areas and water areas, while those with lower quality habitats (levels I, II) were mainly distributed in agricultural urban areas. (2) From 2000 to 2020, the overall average habitat quality of Shiyan City first increased, then decreased, and then increased again. Additionally, the habitat area increased with an improvement in the level. There was a trend in habitat transformation moving from low to high quality level, showing a spatial pattern of “rising in the southwest and falling in the northeast”. (3) The habitat quality in the water area and woodland area was the highest, followed by grassland, and that of cultivated land was the lowest. From 2000 to 2020, the habitat quality of cultivated land, woodland, and grassland decreased slightly, while the habitat quality of water increased significantly. (4) The higher the level of the topographic position index, the smaller the change range of land use types with time. The terrain gradient effect of habitat quality was significant. With the increase in terrain level, the average habitat quality correspondingly improved, but the increasing range became smaller and smaller. These results are helpful in revealing the spatiotemporal evolution of habitat quality caused by land use changes in Shiyan City and can provide a scientific basis for the optimization of regional ecosystem patterns and land use planning and management, and they are of great significance for planning the rational and sustainable use of land resources and the construction of an ecological civilization.

Ambientes de ocorrência do porífero Oncosclera jewelli no Parque Estadual Do Tainhas, Planalto Meridional do RS: uma análise geomorfométrica

Tainhas State Park embraces locations of occurrence of freshwater sponge Oncosclera jewelli. Our objective is to indicate the areas of potential occurrence of the species from factors related to the geomorphometric signature of the occurance points along the Tainhas River in the Park and its buffer zone. Connections and data analysis were performed from the construction and manipulation of a geographic database, in SIGs SPRING-5.4.3 and QGIS-2.18, containing: a) MDEs from Topodata, Embrapa and Alos bases, obtained by remote orbitals sensors for the entire study area, and MDE obtained by drone-generated aerial images of the geomorphometric variables: slope, aspect and topographic position index; b) geological map of Rio Grande do Sul; c) land use and coverage map, based on images from the GeoEye satellite. The results demonstrated that the species occurs exclusively on the lithological unit of Serra Geral Formation. The plain is marked by terrain of low slope and south and east orientation and flattened plains. The Potential Occurrence Map of the species was generated, demonstrating that approximately 4.5% of the total length of watercourse stretches in the study area meet the analyzed geomorphometric conditions.

Remotely Sensed Seasonal Shoreward Intrusion of the East Australian Current: Implications for Coastal Ocean Dynamics

For decades, the presence of a seasonal intrusion of the East Australian Current (EAC) has been disputed. In this study, with a Topographic Position Index (TPI)-based image processing technique, we use a 26-year satellite Sea Surface Temperature (SST) dataset to quantitatively map the EAC off northern New South Wales (NSW, Australia, 28–32°S and ~154°E). Our mapping products have enabled direct measurement (“distance” and “area”) of the EAC’s shoreward intrusion, and the results show that the EAC intrusion exhibits seasonal cycles, moving closer to the coast in austral summer than in winter. The maximum EAC-to-coast distance usually occurs during winter, ranging from 30 to 40 km. In contrast, the minimum distance usually occurs during summer, ranging from 15 to 25 km. Further spatial analyses indicate that the EAC undergoes a seasonal shift upstream of 29°40′S and seasonal widening downstream. This is the first time that the seasonality of the EAC intrusion has been confirmed by long-term remote-sensing observation. The findings provide new insights into seasonal upwelling and shelf circulation previously observed off the NSW coast.

Modelling the Structure of Terrestrial Landscapes in Urban Areas

The study of internal and external factors in the formation of an urban geosystem is determined by its complex structure and multiple connections. Based on geoinformation modelling, an analysis of the landscape structure of the city territory is carried out, which can be a basis for further geoecological research. Morphometric indicators, which make it possible to determine the elementary geochemical landscapes, are indicated according to the data of the digital elevation model. A standardised topographic position index (TPI) is used to determine locations. Spatial zoning according to the type of land use reflects the qualitative features of the external load and technogenic impact. The data on the composition of the lithogenic base show the properties of the depositing medium and determine the natural background. Number of categories of landscapes identified are 58, characterised by a homogeneous geological composition, technogenic load and conditions for the migration of matter. The ratios of various landscape zones have been calculated. The study area as a whole is characterised by the predominance of migration processes over accumulation.

Mapping the Coastal Upwelling East of Taiwan Using Geostationary Satellite Data

Coastal upwelling is important for coastal ecosystems and the blue economy because of its large productivity and large potential for catching fish. However, coastal upwelling along the Taiwan east coast has received little attention from the research community. This study used five-year daily Himawari-8 geostationary satellite sea surface temperature data to map the coastal upwelling east of Taiwan during the summer monsoon season. We applied a semi-automatic image process technique based on the topographic position index for the quantitative upwelling mapping. The results show clear evidence of seasonal coastal upwelling along the entire Taiwan east coast, mainly under the influence of upwelling-favorable southwesterly/southerly winds. There are three broad upwelling centers along the Taiwan east coast: north, central, and south. The upwelling around the northern center has the longest upwelling season, lasting from May to September. The upwelling extents are larger between June and August during the height of the summer monsoon.