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Pennsylvania's (PA) processes and policies for landscape-scale cultural and visual resource conservation are lacking. In PA, like much of the United States (US), landscape change policies are prescriptive and concerned mainly with ecology, health, safety, and welfare issues. These factors combined relegate cultural and scenic aspects to ancillary matters, often leading to their degradation. Culturally focused fields, such as landscape architecture, archaeology, and planning call for rescaling cultural conservation planning to regional scale. Rescaling would treat cultural resources like other environmental and ecological resources, giving cultural resources equal weight in conservation evaluations. The United Kingdom (UK) has policies specifically for visual impact assessment required for development projects. This paper discusses scale issues and political processes within regional visual and cultural resource conservation in PA, US, compares nascent regional-scale planning efforts in PA and the UK, and proposes improvements to PA and, by extension, US cultural landscape conservation policy implementation. [ABSTRACT FROM AUTHOR]

The outbreaks of African Swine Fever (ASF) in China are ongoing, and the inadequate management of the pig supply chain is criticized. In the past four years, a series of preventive and control measures have been supplied national wide, while the outbreaks have not been terminated. This suggests the existing animal disease management at the district level may not be appropriate to control ASF under the current situation of the ASF outbreak in China. It is urgent to further describe real distribution areas of ASF in China. In this study, we combined four regional-scale models to predict the risk distribution of ASF in mainland China and identify risk factors related to ASF outbreaks. The results showed that the four regional-scale models were more accurate in predicting the ASF outbreaks than the nationwide scale model. The four regional-scale models identified the potential risk factors associated with ASF outbreaks, such as population density, pig density, land cover, temperature, and elevation factors. Moreover, seven clusters with high potential risk of ASF outbreaks were identified. Then, based on the results, we proposed more suitable prevention and control plans for ASF, which can assist the implementation of transport management policies within and between risk clusters. [ABSTRACT FROM AUTHOR]

Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an AutoML-based global LSP framework at two spatial resolutions of 90 m and 1000 m, and achieved an area under the receiver operating characteristic above 0.96. The global prediction results were then validated using additional regional landslide inventories, including three countries, three provinces, and two prefecture-level datasets. Moreover, the global prediction results of 90 m are used to improve the performance of regional LSP. Specifically, the low-and very low-prone areas in the global prediction results were used as non-landslide samples for susceptibility modeling. Results demonstrated that the model achieved a better performance than original global prediction results. We believe that this study will be able to reliably promote the application of intelligent learning methods in global LSP.

In cold and humid climates, warming temperatures will result in longer growing seasons, leading to land cover changes that could have long‐term impacts on groundwater recharge (GWR), in addition to the direct impacts of climate change. The objective of this study was therefore to investigate whether land cover (LC) changes need to be considered when simulating long‐term regional‐scale potential GWR in cold and humid climates by (1) quantifying how LC changes impact simulated GWR and (2) quantifying the combined impacts of LC and climate changes on the future GWR changes. Using the region of southern Quebec (Canada) as a case study and a water budget model, this work proposes an innovative coupling of land cover change scenarios and specific future climate conditions to simulate spatially distributed transient GWR over the 1951–2100 period. The results showed that including LC changes in long‐term GWR simulations produced statistically significant increases in GWR compared to using a constant LC through time (average of +13 mm). Massive afforestation taking place on agricultural lands simulated for one of the scenario chains (RCP4.5) increased GWR by reducing runoff during the snow‐dominated period (average − 17 mm). The results also showed that GWR was more sensitive to climate change for scenarios that included intense land cover changes. Additionally, the spatial distribution of the LC changes influenced their simulated impacts on GWR. Considering that the methodology was computationally feasible and entirely transferrable to the new CMIP6 ensemble, LC changes should be considered systematically in long‐term groundwater resources simulations. [ABSTRACT FROM AUTHOR]

Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an AutoML-based global LSP framework at two spatial resolutions of 90 m and 1000 m, and achieved an area under the receiver operating characteristic above 0.96. The global prediction results were then validated using additional regional landslide inventories, including three countries, three provinces, and two prefecture-level datasets. Moreover, the global prediction results of 90 m are used to improve the performance of regional LSP. Specifically, the low-and very low-prone areas in the global prediction results were used as non-landslide samples for susceptibility modeling. Results demonstrated that the model achieved a better performance than original global prediction results. We believe that this study will be able to reliably promote the application of intelligent learning methods in global LSP. [ABSTRACT FROM AUTHOR]

Objectives: The discrete global grid system (DGGS) is a preferred solution in supporting the fusion processing of multi-source geospatial information. The research of hexagonal gird systems has raised wide academic concern. Compared with global grids, the requirements for regional-scale grid application are more extensive, which has been one of the important issues in current hexagonal DGGS research. It combines two adjacent triangle facets of an icosahedron into a logical quad structure, and based on this structure, an algorithm of generating the multi-aperture hexagonal grid systems with regional-scale is proposed.Methods: Firstly, the grid type is analyzed to establish a discrete integer coordinate system and the spatial location of the multi-aperture hexagonal grid cell is described. Secondly, the regional-scale is split based on the logical quad structure on the surface of the spherical icosahedron to create sub-areas. Thirdly, according to the boundary of the sub-area, an external minimum logical quad structure algorithm is designed to eliminate irrelevant cells on the logical quad structure. Finally, the minimum logical quad structure is traversed to generate a multi-aperture partial grid.Results and Conclusions: The comparative experimental results show that the proposed algorithm has the advantages of better flexibility and higher efficiency. The generated grid is used for the organization of raster data, which can significantly reduce the amount of data and has good application potential. [ABSTRACT FROM AUTHOR]

Featured Application: This research deals with a very relevant topic in the framework of landslide susceptibility mapping, highlighting some very critical drawbacks in using a weak landslide inventory for regional-scale assessment. Tools and strategies for recognizing and approaching such limits are given. This research is focused on the evaluation of the reliability of regional landslide susceptibility models obtained by exploiting inhomogeneous (for quality, resolution and/or triggering related type and intensity) collected inventories for calibration. At a large-scale glance, merging more inventories can result in well-performing models hiding potential strong predictive deficiencies. An example of the limits that such kinds of models can display is given by a landslide susceptibility study, which was carried out for a large sector of the coastal area of El Salvador, where an apparently well-performing regional model (AUC = 0.87) was obtained by regressing a dataset through multivariate adaptive regression splines (MARS), including five landslide inventories from volcanic areas (Ilopango and Coatepeque caldera; San Salvador, San Miguel, and San Vicente Volcanoes). A multiscale validation strategy was applied to verify its actual predictive skill on a local base, bringing to light the loss in the predictive power of the regional model, with a lowering of AUC (20% on average) and strong effects in terms of sensitivity and specificity. [ABSTRACT FROM AUTHOR]

This research is focused on the evaluation of the reliability of regional landslide susceptibility models obtained by exploiting inhomogeneous (for quality, resolution and/or triggering related type and intensity) collected inventories for calibration. At a large-scale glance, merging more inventories can result in well-performing models hiding potential strong predictive deficiencies. An example of the limits that such kinds of models can display is given by a landslide susceptibility study, which was carried out for a large sector of the coastal area of El Salvador, where an apparently well-performing regional model (AUC = 0.87) was obtained by regressing a dataset through multivariate adaptive regression splines (MARS), including five landslide inventories from volcanic areas (Ilopango and Coatepeque caldera; San Salvador, San Miguel, and San Vicente Volcanoes). A multiscale validation strategy was applied to verify its actual predictive skill on a local base, bringing to light the loss in the predictive power of the regional model, with a lowering of AUC (20% on average) and strong effects in terms of sensitivity and specificity.

The erosion impact of large coastal storm events typically occurs across broad (100s of km) sections of coastline and may include significant variability both alongshore and vertically between the berm and dunes. Identifying controls of variability in storm erosion is critical to understanding the response of coastlines to present and changing storminess. This contribution analyses immediate pre‐ and post‐storm Lidar data of over 1700 cross‐shore profile transects, determined at every 100 m alongshore and spanning 400km of the southeast Australian coastline. This unique dataset allowed for a data‐driven Bayesian network analysis of the key relationships between the measured storm erosion response and a range of variables describing the antecedent morphology and hydrodynamic forcing at the coastline. It was found that while erosion of the dune and berm was observed to increase with increased exposure of the local profile to incident storm waves, additional erosion controls were found to be different for these two different sections of the beach. Erosion of the berm was specifically linked to the pre‐storm berm volume, with more accreted berms experiencing a greater proportion of erosion of the overall berm, regardless of variability in forcing conditions. In contrast, dune erosion was equally controlled by the exceedance of wave runup above the antecedent dune toe elevation and the width of the beach immediately fronting the dune, with wider beaches resulting in reduced dune erosion. The results of this large, data‐driven analysis provide important affirmation and insights into the primary controls of berm and dune storm erosion. Plain Language Summary: The erosion caused by coastal storm events is often spatially‐variable ‐ some areas of the coast may experience severe erosion of the beach and dune, while adjacent areas may be relatively unaffected. Understanding the controls of this spatial variability in erosion is important to better manage storm erosion now and in the future. This research analyses an unprecedentedly detailed dataset of coastal storm erosion spanning a 400 km region of the southeast Australian coastline to identify controls of spatial variability in storm‐induced coastal erosion. It was found that the magnitude of erosion caused by the storm event at different locations along the coast was controlled by both the storm wave and water level conditions, as well as the pre‐storm state of the beach. In particular, erosion was controlled by 1) the exposure of the coastline to incident storm waves; 2) the elevation of wave runup with respect to dune toe elevation; 3) the pre‐storm volume of sand on the beach; and, 4) the pre‐storm width of the beach. The results provide useful insight into the controls of both local and regional‐scale coastal erosion and have implications for understanding and modelling coastal vulnerability to storms now and into the future. Key Points: Immediate pre‐ and post‐storm Lidar data of 400 km of coastline was analyzed to investigate key controls of variability in storm erosionDune erosion was equally controlled by the amount of runup that exceeded the dune toe and the width of the berm fronting the duneAccreted berms experienced a greater proportion of erosion of the overall berm, regardless of variability in forcing conditions [ABSTRACT FROM AUTHOR]

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification.

In this study, a copula-based stochastic fuzzy-credibility programming (CSFP) method is developed for planning regional-scale electric power systems (REPS). CSFP cannot only deal with multiple uncertainties presented as random variables, fuzzy sets, interval values as well as their combinations, but also reflect uncertain interactions among multiple random variables owning different probability distributions and having previously unknown correlations. Then, a CSFP-REPS model is formulated for planning the electric power systems (EPS) of the Jing-Jin-Ji region, where multiple scenarios with different joint and individual probabilities as well as different credibility levels are examined. Results reveal that electricity shortage would offset [4.8, 5.2]% and system cost would reduce [3.2, 3.3]% under synergistic effect scheme. Results also disclose that the study region’s future electricity-supply pattern would tend to the transition to renewable energies and the share of renewable energies would increase approximately 10% over the planning horizon. Compared to the conventional stochastic programming, the developed CSFP method can more effectively analyze individual and interactive effects of multiple random variables, so that the loss of uncertain information can be mitigated and the robustness of solution can be enhanced. Moreover, based on the main effect analysis and regression analysis, CSFP-REPS can provide multiple joint planning strategies in a cost- and computation-effective way. Findings are useful for reflecting interactions among multiple random variables and disclosing their joint effects on modeling outputs of REPS planning problems. [ABSTRACT FROM AUTHOR]

An increased awareness by river managers of the importance of river channel migration to sediment dynamics, habitat complexity and other ecosystem functions has led to an advance in the science and practice of identifying, protecting or restoring specific erodible corridors across which rivers are free to migrate. One current challenge is the application of these watershed-specific goals at the regional planning scales (e.g., the European Water Framework Directive). This study provides a GIS-based spatial analysis of the channel migration rates at the regional-scale. As a case study, 99 reaches were sampled in the French part of the Rhône Basin and nearby tributaries of the Mediterranean Sea (111,300 km 2 ). We explored the spatial correlation between the channel migration rate and a set of simple variables (e.g., watershed area, channel slope, stream power, active channel width). We found that the spatial variability of the channel migration rates was primary explained by the gross stream power (R 2 = 0.48) and more surprisingly by the active channel width scaled by the watershed area. The relationship between the absolute migration rate and the gross stream power is generally consistent with the published empirical models for freely meandering rivers, whereas it is less significant for the multi-thread reaches. The discussion focused on methodological constraints for a regional-scale modelling of the migration rates, and the interpretation of the empirical models. We hypothesize that the active channel width scaled by the watershed area is a surrogate for the sediment supply which may be a more critical factor than the bank resistance for explaining the regional-scale variability of the migration rates. [ABSTRACT FROM AUTHOR]

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided.

Long-term changes in precipitation and temperature indirectly impact aquifers through groundwater recharge (GWR). Although estimates of future GWR are needed for water resource management, they are uncertain in cold and humid climates due to the wide range in possible future climatic conditions. This work aims to (1) simulate the impacts of climate change on regional GWR for a cold and humid climate and (2) identify precipitation and temperature changes leading to significant long-term changes in GWR. Spatially distributed GWR is simulated in a case study for the southern Province of Quebec (Canada, 36,000 km2) using a water budget model. Climate scenarios from global climate models indicate warming temperatures and wetter conditions (RCP4.5 and RCP8.5; 1951–2100). The results show that annual precipitation increases of >+150 mm/yr or winter precipitation increases of >+25 mm will lead to significantly higher GWR. GWR is expected to decrease if the precipitation changes are lower than these thresholds. Significant GWR changes are produced only when the temperature change exceeds +2 °C. Temperature changes of >+4.5 °C limit the GWR increase to +30 mm/yr. This work provides useful insights into the regional assessment of future GWR in cold and humid climates, thus helping in planning decisions as climate change unfolds. The results are expected to be comparable to those in other regions with similar climates in post-glacial geological environments and future climate change conditions. [ABSTRACT FROM AUTHOR]

In this paper, for the first time, a regional-scale 1:100,000 landslide-susceptibility map (LSM) is presented for Sisak-Moslavina County in Croatia. The spatial relationship between landslide occurrence and landslide predictive factors (engineering geological units, relief, roughness, and distance to streams) is assessed using the integration of a statistically based frequency ratio (FR) into the analytical hierarchy process (AHP). Due to the lack of landslide inventory for the county, LiDAR-based inventories are completed for an area of 132 km2. From 1238 landslides, 549 are chosen to calculate the LSM and 689 for its verification. Additionally, landslides digitized from available geological maps and reported via the web portal "Report a landslide" are used for verification. The county is classified into four susceptibility classes, covering 36% with very-high and high and 64% with moderate and low susceptibility zones. The presented approach, using limited LiDAR data and the extrapolation of the correlation results to the entire county, is encouraging for primary regional-level studies, justifying the cost-benefit ratio. Still, the positioning of LiDAR polygons prerequires a basic statistical analysis of predictive factors. [ABSTRACT FROM AUTHOR]

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification. [ABSTRACT FROM AUTHOR]

The SW Iberian continental margin is well recognized as a tectonically active area, where major canyons and mass wasting events are both present. Earthquake triggered submarine landslides may cause tsunami and result in catastrophic damage to bordering coastal areas. In this setting, submarine landslide susceptibility mapping represents a major step towards a regional risk mitigation strategy. Landslide susceptibility mapping in large offshore areas presents significant challenges as a result of the limited information on controlling variables, large uncertainties in triggering mechanisms and limited geotechnical data. In this study, a geotechnical model-based approach has been followed that narrows the range of controlling factors and, within a probabilistic framework, allows a systematic treatment of parameter uncertainties. This model-based analysis covers the whole SW Iberian margin increasing by three orders of magnitude the areal extent of precedent offshore slope stability susceptibility studies. This jump in spatial scale is facilitated by application of a systematic Bayesian updating procedure, to combine geotechnical information from global databases and that available from regional sites. Seismic shaking is estimated using an available regional database of seismogenic faults. These tools are implemented within a GIS to generate, via Montecarlo simulations, probabilistic landslide susceptibility maps based on two different analytical seismic infinite slope stability models. These models differ mainly in the form of their final results, either as distributions of slope stability safety factors or as distributions of seismic-triggered slope displacements. Receiving Operator Curves are used to assess the different landslide susceptibility predictions obtained against a comprehensive regional database of submarine landslides. It turns out that the models analyzed correctly predict 92% and 82% of the mapped landslide subset chosen for validation for pseudo-static and displacement-based method respectively. This suggests that, within the limits of the currently available databases, seismic events are the dominant factor at the origin of the submarine landslides mapped in the study area. An advantage of the framework presented is that it can quickly incorporate new regional geotechnical information or better regional landslide databases, as they become available. • Generation of two probabilistic submarine landslide susceptibility maps of SW Iberian margin. • Application of Bayesian Equivalent samples method for geotechnical regional characterization with scarce dataset. • Relatively better performance of Equivalent pseudo-static approach over displacement-based model. • Valid assumption that faults dislocate their entire length. [ABSTRACT FROM AUTHOR]

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided. [ABSTRACT FROM AUTHOR]