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1.
Animals select habitat resources at multiple spatial scales. Thus, explicit attention to scale dependency in species–habitat relationships is critical to understand the habitat suitability patterns as perceived by organisms in complex landscapes. Identification of the scales at which particular environmental variables influence habitat selection may be as important as the selection of variables themselves. In this study, we combined bivariate scaling and Maximum entropy (Maxent) modeling to investigate multiscale habitat selection of endangered brown bear (Ursus arctos) populations in northwest Spain. Bivariate scaling showed that the strength of apparent habitat relationships was highly sensitive to the scale at which predictor variables are evaluated. Maxent models on the optimal scale for each variable suggested that landscape composition together with human disturbances was dominant drivers of bear habitat selection, while habitat configuration and edge effects were substantially less influential. We found that explicitly optimizing the scale of habitat suitability models considerably improved single-scale modeling in terms of model performance and spatial prediction. We found that patterns of brown bear habitat suitability represent the cumulative influence of habitat selection across a broad range of scales, from local resources within habitat patches to the landscape composition at broader spatial scales.  相似文献   

2.
This paper explains a ray tracing method which is applied to prediction and visualization of diffracted and reflected GPS signals in dense urban areas. Reflected and diffracted signals can have a detrimental effect on GPS positioning accuracy especially in highly built‐up areas. The ray tracing technique implemented in this paper is specially geared to LiDAR height pole data at 1‐m spatial resolution and 2D building footprints in raster and vector format, respectively. Such a simple data format allows for rapid implementation of 3D ray tracing in a GIS without further processing so that detailed 3D urban models in vector format are not required. Issues of spatial uncertainty in the data used are also addressed in relation to the identification of multipath signals. Some preliminary results obtained from fieldwork are presented and analysed in detail.  相似文献   

3.
We analyze geomorphic properties extracted from LiDAR and SRTM (Shuttle Radar Topography Mission) data to test whether the damage zone along the central San Jacinto Fault (SJF) zone can be resolved with remotely-sensed data in a quantitative fashion. The SJF is one of the most active faults in southern California, with well expressed geomorphology and a fast slip rate, as seen in the geology and by GPS. We use ArcMap and the TauDEM toolbox to compare several morphometric parameters, including drainage density (Dd), on both sides of the fault, using a 1 km and a 5 km buffer for the LiDAR and SRTM data, respectively. We also analyze the spatial patterns of Dd near the fault, using two different definitions of spatial Dd. The high resolution of the LiDAR data allows us to focus on a single fault, eliminating the effects of parallel nearby faults. From the LiDAR data we find that the highest Dd values occur in areas between two fault strands, followed generally by rocks on the northeast side of the fault, with the lowest Dd values occurring on the southwest side of the fault. The SRTM data shows a band of high Dd values centered on the main fault trace with ~ 1 km width. Our results indicate that there is a strong correlation between drainage density and proximity to the fault, with zones of structural complexity along the fault displaying the highest Dd. We interpret this to largely be an effect of degree of rock damage, as these are areas that are expected to be more damaged, and field observations support this contention. If we are correct, then it appears that the northeast side of the SJF is generally more damaged. South of the trifurcation area there is evidence that the signal is reversed on the larger scale, with more damage on the southwest side of the fault inferred from the SRTM data, possibly caused by extension between the Coyote Creek and Clark faults. The implications of the observed asymmetry could be geological evidence for rupture propagation direction, because a preferred propagation direction is predicted to produce asymmetric damage structure that would be recorded in the volume of rock surrounding a fault.  相似文献   

4.
ABSTRACT

Submarine canyons play an important role in the regional distribution, abundance and dispersal of marine biota and are increasingly being recognised as geomorphic features of high conservation significance along Australia’s continental margin. Certain canyons have been described as foraging ‘hotspots’ attributable to the high abundance of apex cetacean species aggregating in these areas. Anecdotal evidence of large seasonal aggregations of killer whales in the Bremer Canyon, south-west Australia, has attracted significant research attention in the last decade. To identify important environmental drivers influencing aggregation patterns, a predictive spatial habitat model using the Maxent model was developed based on presence-only whale sighting data. In addition, remotely sensed sea surface temperature and chlorophyll-a concentrations were assessed to investigate the spatio-temporal variation in sea surface conditions. Habitat preference was predicted in areas between canyon heads, with the most influential predictor variables being depth and distance from the continental shelf break. Analysis of remote-sensing data highlighted low localised variability in surface waters and illustrated the seasonal trends of the Leeuwin Current. This study demonstrates the influence of bathymetry and submarine geomorphology on enhanced cetacean abundance and highlights the need for recognition of this potential foraging area in marine reserve planning.  相似文献   

5.
Species distribution models of stray cats were developed using two types of occurrence data: (i) a combined dataset of stray cats and cat colonies in Auckland and projected to the wider New Zealand area; and (ii) population density as an analogue for country-wide stray cat occurrence. These occurrence data, together with sets of environmental variables were used as input to the Maxent modelling tool to produce maps of suitability for the species. Environmental variables used in the models consist of current bioclimatic conditions, and a future climate scenario (RCP8.5 for year 2070 CCSM model). Commonly occurring bias in the modelling process due to latitude, the area for selecting background points in model evaluation, inherent spatial autocorrelation of occurrence points, and correlated bioclimatic variables were explicitly addressed. Results show that the North Island consistently provide more suitable areas for stray cats with increased suitability in a high emission climate change condition. Key protected areas at risk from the increased suitability to stray cats are also presented.  相似文献   

6.
S. Rayburg  M. Thoms  M. Neave 《Geomorphology》2009,106(3-4):261-270
It can be challenging to accurately determine the topography of physically complex landscapes in remote areas. Ground-based surveys can be difficult, time consuming and may miss significant elements of the landscape. This study compares digital elevation models (DEMs) generated from three different data sources, of the physically complex Narran Lakes Ecosystem, a major floodplain wetland ecosystem in Australia. Topographic surfaces were generated from an airborne laser altimetry (LiDAR) survey, a ground-based differential GPS (DGPS) survey containing more than 20,000 points, and the 9″ DEM of Australia. The LiDAR- and DGPS-derived data generated a more thorough DEM than the 9″ DEM; however, LiDAR generated a surface topography that yielded significantly more detail than the DGPS survey, with no noticeable loss of elevational accuracy. Both the LiDAR- and the DGPS-derived DEMs compute the overall surface area and volume of the largest floodplain lake within the system to within 1% of each other. LiDAR is shown to be a highly accurate and robust technique for acquiring large quantities of topographic data, even in locations that are unsuitable for ground surveying and where the overall landscape is of exceptionally low relief. The results of this study highlight the potential for LiDAR surveys in the accurate determination of the topography of floodplain wetlands. These data can form an important component of water resource management decisions, particularly where environmental water allocations for these important ecosystems need to be determined.  相似文献   

7.
Climate and land-use changes are projected to threaten biodiversity over this century. However, few studies have considered the spatial and temporal overlap of these threats to evaluate how ongoing land-use change could affect species ranges projected to shift outside conservation areas. We evaluated climate change and urban development effects on vegetation distribution in the Southwest ecoregion, California Floristic Province, USA. We also evaluated how well a conservation network protects suitable habitat for rare plant species under these change projections and identified primary sources of uncertainty. We used consensus-based maps from three species distribution models (SDMs) to project current and future suitable habitat for 19 species representing different functional types (defined by fire-response – obligate seeders, resprouting shrubs – and life forms – herbs, subshrubs), and range sizes (large/common, small/rare). We used one spatially explicit urban growth projection; two climate models, emission scenarios, and probability thresholds applied to SDMs; and high-resolution (90 m) environmental data. We projected that suitable habitat could disappear for 4 species and decrease for 15 by 2080. Averaged centroids of suitable habitat (all species) were projected to shift tens (up to hundreds) of kilometers. Herbs showed a small-projected response to climate change, while obligate seeders could suffer the greatest losses. Several rare species could lose suitable habitat inside conservation areas while increasing area outside. We concluded that (i) climate change is more important than urban development for vegetation habitat loss in this ecoregion through 2080 due to diminishing amounts of undeveloped private land in this region; (ii) the existing conservation plan, while extensive, may be inadequate to protect plant diversity under projected patterns of climate change and urban development, (iii) regional assessments of the dynamics of the drivers of biodiversity change based on high-resolution environmental data and consensus predictive mapping, such as this study, are necessary to identify the species expected to be the most vulnerable and to meaningfully inform regional-scale conservation.  相似文献   

8.
Road building in Congo Basin forests has increased due to expansion of commercial logging, with potential to expose intact forests to greater establishment of agriculture. We developed new knowledge of agriculture clearing sizes, spatial patterns, and relationships with roads in seven case study sites comprising 7,529 km2. Using very high spatial resolution satellite imagery, we mapped roads and rivers, plus clearings for agriculture, settlements, and logging. Mapped clearings (N = 1,781) ranged in size from 0.008 ha to more than 300 ha; most were smallholder agriculture, with 64 percent ≤ 1 ha. Statistical tests of spatial pattern confirmed that agriculture occurred in an inhomogeneous-aggregated pattern, suggesting interactions with other landscape elements. Proximity analyses showed that 76 percent of clearings were within 1 km of a road or river. Thirty-five percent of agriculture clearings were within 1 km of main public roads built before 1990, compared to 17 percent for logging roads built after 2000. Less than 6 percent of agriculture clearings were within 1 km of logging roads with overgrown canopies, suggesting transient relationships. Results based on fine-scale data provide new empirical support for understanding the interactions between agriculture and roads in one of the remaining relatively intact forest areas of the Congo Basin.  相似文献   

9.
As managers and researchers of protected natural areas continue to seek balance in promoting visitor use but limiting negative experiential and natural resource impacts, the integration of social and physical spatial data may play a critical role in understanding how visitors and the community interface with the landscape within these protected areas. These spatial considerations are important for inventorying, monitoring, and managing the conditions of natural resources within parks. Specifically related to the visitor impacts of natural resources in parks are the use and condition of multiple-use trails. Technology such as GPS and GIS may allow for a unique assessment of the relationship between factors that influence this resource. This paper focuses on how visitor use distribution (measured through GPS tracking), activity type, and trail design influence the impacts to trail conditions. This paper also addresses statistical concerns related to spatial dependency. Results suggest that failure to account for spatial dependency can lead to erroneous Type I findings. Additionally, activity type (specifically horseback riders) and trail design were found to best predict trail impacts when controlling for spatial dependency.  相似文献   

10.
The geographical distribution of a species is limited by factors such as climate, resources, disturbances and species interactions. Environmental niche models attempt to encapsulate these limits and represent them spatially but do not always incorporate disturbance factors. We constructed MaxEnt models derived from a remotely sensed vegetation classification with, and without, an agricultural modification variable. Including agricultural modification improved model performance and led to more sites with native vegetation and fewer sites with exotic or degraded native vegetation being predicted suitable for A. parapulchella. Analysis of a relatively well-surveyed sub-area indicated that including agricultural modification led to slightly higher omission rates but markedly fewer likely false positives. Expert assessment of the model based on mapped habitat also suggested that including agricultural modification improved predictions. We estimate that agricultural modification has led to the destruction or decline of approximately 30–35% of the most suitable habitat in the sub-area studied and approximately 20–25% of suitable habitat across the entire study area, located in the Australian Capital Territory, Australia. Environmental niche models for a range of species, particularly habitat specialists, are likely to benefit from incorporating agricultural modification. Our findings are therefore relevant to threatened species planning and management, particularly at finer spatial scales.  相似文献   

11.
Landing a rescue helicopter in a wilderness environment, such as Yosemite National Park, requires suitable areas that are flat, devoid of tree canopy, and not within close proximity to other hazards. The objective of this study was to identify helicopter landing areas that are most likely to exist based on available geographic data using two GIScience methods. The first approach produced an expert model that was derived from predefined feature constraints based on existing knowledge of helicopter landing area requirements (weighted overlay algorithm). The second model is derived using a machine learning technique (maximum entropy algorithm, Maxent) that derives feature constraints from existing presence-only points; that is, geographic one-class data. Both models yielded similar output and successfully classified test coordinates, but Maxent was more efficient and required no user-defined weighting that is typically subject to human bias or disagreement. The pros and cons of each approach are discussed and the comparison reveals important considerations for a variety of future land suitability studies, including ecological niche modeling. The conclusion is that the two approaches complement each other. Overall, we produced an effective geographic information system product to support the identification of suitable landing areas in emergent rescue situations. To our knowledge, this is the first GIScience study focused on estimating the location of landing zones for a search-and-rescue application.  相似文献   

12.
At the local spatial scale, land-use variables are often employed as predictors for ecological niche models (ENMs). Remote sensing can provide additional synoptic information describing vegetation structure in detail. However, there is limited knowledge on which environmental variables and how many of them should be used to calibrate ENMs. We used an information-theoretic approach to compare the performance of ENMs using different sets of predictors: (1) a full set of land-cover variables (seven, obtained from the LGN6 Dutch National Land Use Database); (2) a reduced set of land-cover variables (three); (3) remotely sensed laser data optimized to measure vegetation structure and canopy height (LiDAR, light detection and ranging); and (4) combinations of land cover and LiDAR. ENMs were built for a set of bird species in the Veluwe Natura 2000 site (the Netherlands); for each species, 26–214 records were available from standardized monitoring. Models were built using MaxEnt, and the best performing models were identified using the Akaike’s information criterion corrected for small sample size (AICc). For 78% of the bird species analysed, LiDAR data were included in the best AICc model. The model including LiDAR only was the best performing one in most cases, followed by the model including a reduced set of land-use variables. Models including many land-use variables tended to have limited support. The number of variables included in the best model increased for species with more presence records. For all species with 33 records or less, the best model included LiDAR only. Models with many land-use variables were only selected for species with >150 records. Test area under the curve (AUC) scores ranged between 0.72 and 0.92. Remote sensing data can thus provide regional information useful for modelling at the local and landscape scale, particularly when presence records are limited. ENMs can be optimized through the selection of the number and identity of environmental predictors. Few variables can be sufficient if presence records are limited in number. Synoptic remote sensing data provide a good measure of vegetation structure and may allow a better representation of the available habitat, being extremely useful in this case. Conversely, a larger number of predictors, including land-use variables, can be useful if a large number of presence records are available.  相似文献   

13.
Warm deserts world-wide provide habitats for rich lizard species assemblages; North American deserts are no exception, however the desert regions of the US and Mexico are experiencing increasing habitat changes from multiple anthropogenic sources. Our objective here was to document current lizard species richness patterns across the North American deserts within the existing network of conservation areas. We identified 110 lizard species occurring across one or more of the 19 sites we analyzed. Three species richness hot spots were identified; a northern Baja California faunal extension into southern California in the US, and in Mexico, two sites within the state of Coahuila, as well as high endemism in the Cape Region of Baja California Sur. Species richness was associated with sites where desert ecoregions overlap and with insular isolation. Our uncertainty regarding how species will respond to the multifaceted aspects of global change is such that large protected natural areas with complex topography may be the most effective strategy for protecting desert lizards along with overall biodiversity. The 19 sites we analyzed represent the cores of a more robust conservation network that will be needed for the protection of biodiversity across North American Deserts.  相似文献   

14.
Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features.  相似文献   

15.
Wildlife ecologists frequently make use of limited information on locations of a species of interest in combination with readily available GIS data to build models to predict space use. In addition to a wide range of statistical data models that are more commonly used, machine learning approaches provide another means to develop predictive spatial models. However, comparison of output from these two families of models for the same data set is not often carried out. It is important that wildlife managers understand the pitfalls and limitations when a single set of models is used with limited GIS data to try to predict and understand species distribution. To illustrate this, we carried out two sets of models (generalized linear mixed models (GLMMs) and boosted regression trees (BRTs)) to predict geographic occupancy of the eastern coyote (Canis latrans) on the island of Newfoundland, Canada. This exercise is illustrative of common spatial questions in wildlife research and management. Our results show that models vary depending on the approach (GLMM vs. BRT) and that, overall, BRT had higher predictive ability. Although machine learning has been criticized because it is not explicitly hypothesis-driven, it has been used in other areas of spatial modelling with success. Here, we demonstrate that it may be a useful approach for predicting wildlife space use and to generate hypotheses when data are limited. The results of this comparison can help to improve other models for species distributions and also guide future sampling and modelling initiatives.  相似文献   

16.
We studied the roles of soil moisture contents and vegetation structure in the spatial distribution of small mammals in the typical steppes of Inner Mongolia, China, using logistic and linear regressions of a data set collected in a 6-year study. Our results indicated that soil moisture contents remained in the most parsimonious models for Spermophilus dauricus, Cricetulus barabensis, Microtus maximowiczii, M. gregalis, and Ochotona daurica. The relative abundance of C. barabensis, M. maximowiczii, and O. daurica was inversely related to soil moisture contents, while that of M. gregalis and S. dauricus was positively related to soil moisture contents in logistic regressions. Linear regression analyses showed that soil moisture contents and the number of small mammal species were inversely related. The negative effects of wet soil were consistent at both small mammal population and community levels in the semi-arid steppes. Above-ground plant biomass and plant coverage also affected the spatial distribution of small mammals in the typical steppe of Inner Mongolia.  相似文献   

17.

Because of its species-rich hay meadows and old pollards that are traditionally managed, Ulvund in Myrkdalen, Voss, is one of 14 areas in Hordaland county that are considered to be of special interest for cultural landscape conservation. The spatial patterns of species richness were investigated in hay meadows at Ulvund. Two main types of vegetation were recognised in managed meadows. Unfertilised and species-rich areas with a rather short field layer were situated in the steep parts of the meadows. Fertilised and species-poor areas with a high field layer were situated on flat or gently sloping areas. This spatial pattern is recognised in other west Norwegian hay meadows as well and is very likely linked to historical differences in land management; the flatter areas were formerly used as heavily fertilised permanent tilled fields for grain production, while the steep areas were used for hay production. As the conservation value largely lies in small, localized parts of the farms, conservation management actions can also be localized and thus feasible under modern conditions.  相似文献   

18.
Viewshed and line-of-sight are spatial analysis functions used in applications ranging from urban design to archaeology to hydrology. Vegetation data, a difficult variable to effectively emulate in computer models, is typically omitted from visibility calculations or unrealistically simulated. In visibility analyzes performed on a small scale, where calculation distances are a few hundred meters or less, ineffective incorporation of vegetation can lead to significant modeling error. Using an aerial LiDAR (light detection and ranging) data set of a lodgepole pine (Pinus contorta) dominant ecosystem in Idaho, USA, tree obstruction metrics were derived and integrated into a short-range visibility model. A total of 15 visibility plots were set at a micro-scale level, with visibility modeled to a maximum of 50 m from an observation point. Digital photographs of a 1 m2 target set at 5 m increments along three sightline paths for each visibility plot were used to establish control visibility values. Trunk obstructions, derived from mean vegetation height LiDAR data and processed through a series of tree structure algorithms, were factored into visibility calculations and compared to reference data. Results indicate the model calculated using trunk obstructions with LiDAR demonstrated a mean error of 8.8% underestimation of target visibility, while alternative methods using mean vegetation height and bare-earth models have an underestimation of 65.7% and overestimation of 31.1%, respectively.  相似文献   

19.
In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and micro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution of permafrost were studied to understand the distribution patterns of permafrost in Wenquan on the Qinghai-Tibet Plateau. Cluster and correlation analysis were performed based on 30 m Global Digital Elevation Model (GDEM) data and field data obtained using geophysical exploration and borehole drilling methods. A Multivariate Adaptive Regression Spline model (MARS) was developed to simulate permafrost spatial distribution over the studied area. A validation was followed by comparing to 201 geophysical exploration sites, as well as by comparing to two other models, i.e., a binary logistic regression model and the Mean Annual Ground Temperature model (MAGT). The MARS model provides a better simulation than the other two models. Besides the control effect of elevation on permafrost distribution, the MARS model also takes into account the impact of direct solar radiation on permafrost distribution.  相似文献   

20.
Why GPS makes distances bigger than they are   总被引：1，自引：0，他引：1  
Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.  相似文献   

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