Hierarchical Scale-space Representational Measure for Estimating Land Cover

Space use by resident and transient coyotes in an urban–rural landscape mosaic

Wildlife Research ◽

10.1071/wr15020 ◽

2015 ◽

Vol 42 (6) ◽

pp. 461 ◽

Cited By ~ 14

Author(s):

Numi Mitchell ◽

Michael W. Strohbach ◽

Ralph Pratt ◽

Wendy C. Finn ◽

Eric G. Strauss

Keyword(s):

Land Cover ◽

Rhode Island ◽

Human Activity ◽

Urban Areas ◽

Agricultural Land ◽

Space Use ◽

Scale Space ◽

Rural Landscape ◽

Urban Land Use ◽

Mixed Effect

Context Coyotes (Canis latrans) have adapted successfully to human landscape alteration in the past 150 years and in recent decades have successfully moved into urban areas. While this causes concern about human–wildlife conflicts, research also suggests that coyotes tend to avoid humans and human activity in urban areas. For improving management, a better understanding of space use by coyotes is needed. Aims To study how coyote social behaviour influences fine-scale space use in urban areas we present results from an extensive, multi-year GPS telemetry study (2005–13). The study area in coastal Rhode Island is a mosaic of rural, suburban and urban land use and coyotes have only recently arrived. Methods We differentiated between two social classes: residents (individuals that have established a territory; n = 24) and transients (individuals that have no territory; n = 7). Space use was analysed using mixed effect models and detailed land-cover data. Key results Coyotes tended to select for agricultural and densely vegetated land cover and against land used for housing and commerce. Pasture and cropland were preferred by residents and avoided by transients, especially at night, indicating the role of agricultural land as prime foraging habitat. Both groups selected densely vegetated land cover for daytime shelter sites. Transients selected for densely vegetated land cover both day and night, indicating use for both shelter and foraging. Resident coyotes avoided high- and medium-density housing more than transients. Conclusions We interpret land-cover selection by resident coyotes as indicative of coyote habitat preference, while transients more often occupied marginal habitats that probably do not reflect their preferences. Differences in land cover selection between residents and transients suggest that transients have a corollary strategy to avoid residents. Implications With cover and food appearing to be important drivers of space use, coexistence strategies can build on controlling food resources as well as on the tendency of coyotes to avoid humans. Nevertheless, transients, having the need to avoid territorial resident coyotes as well, show a reduced aversion to land cover with high human activity, creating a higher potential for human–wildlife conflicts.

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Biophysical Evaluation of Land-Cover Products for Land–Climate Modeling

Earth Interactions ◽

10.1175/2009ei276.1 ◽

2009 ◽

Vol 13 (6) ◽

pp. 1-16 ◽

Cited By ~ 6

Author(s):

Jianjun Ge ◽

Nathan Torbick ◽

Jiaguo Qi

Keyword(s):

Land Cover ◽

Land Surface ◽

Climate Models ◽

Climate Modeling ◽

Spatial Scales ◽

Scale Space ◽

Atmospheric Modeling ◽

Biophysical Characterization ◽

Area Index ◽

Moderate Resolution Imaging Spectroradiometer

Abstract The need for accurate characterization of the land surface as boundary conditions in climate models has been recognized widely in the climate modeling community. A large number of land-cover datasets are currently used in climate models either to better represent surface conditions or to study the impacts of surface changes. Deciding upon land-cover datasets can be challenging because the datasets are made with different sensors, ranging methodologies, and varying classification objectives. A new statistical measure Q was developed to evaluate land-cover datasets in land–climate interaction research. This measure calculates biophysical precision of land-cover datasets using 1-km monthly Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product. This method aggregates within-class biophysical consistency, calculated as LAI variation, across a study domain and over multiple years into a single statistic. A smaller mean Q value for a land-cover product indicates more precise biophysical characterization within the classes. As an illustration, four land-cover products were assessed in the East Africa region: Global Land Cover 2000 (GLC2000), MODIS land cover, Olson Global Ecosystems (OGE), and Land Ecosystem–Atmosphere Feedback (LEAF) model. The evaluation was conducted at three different spatial scales corresponding to 30 × 30, 50 × 50, and 100 × 100 km quadrates. The Q measure found that GLC2000 ranked higher compared to the other three land-cover products for every quadrate size. For the 30 × 30 km quadrate size GLC2000 was significantly better than LEAF, which is currently used in the Regional Atmospheric Modeling System. The statistic ranks MODIS land cover above OGE, which is above LEAF. As quadrate size increases, differences between Q decrease indicating greater uncertainty at coarser resolution. The utility of the measure is that it can be applied to any continuous parameter over any scale (space or time) to evaluate the biophysical precision of any land-cover dataset.

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A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w5-263-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 263-268 ◽

Cited By ~ 4

Author(s):

A. Hadavand ◽

M. Saadatseresht ◽

S. Homayouni

Keyword(s):

Image Analysis ◽

Land Cover ◽

Scale Space ◽

Aerial Image ◽

Surface Model ◽

Image Objects ◽

Object Based Image Analysis ◽

Object Based ◽

Land Cover Map ◽

New Framework

In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

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