Evaluation of thermal infrared imaging from uninhabited aerial vehicles for arboreal wildlife surveillance.

An important component of wildlife management and conservation is monitoring the health and population size of wildlife species. Monitoring the population size of an animal group can inform researchers of habitat use, potential changes in habitat and resulting behavioral adaptations, individual health, and the effectiveness of conservation efforts. Arboreal monkeys are difficult to monitor as their habitat is often poorly accessible and most monkey species have some degree of camouflage, making them hard to observe in and below the tree canopy. Surveys conducted using uninhabited aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras can help overcome these limitations by flying above the canopy and using the contrast between the warm body temperature of the monkeys and the cooler background vegetation, reducing issues with impassable terrain and animal camouflage. We evaluated the technical and procedural elements associated with conducting UAV-TIR surveys for arboreal and terrestrial macaque species. Primary imaging missions and analyses were conducted over a monkey park housing approximately 160 semi-free-ranging Japanese macaques (Macaca fuscata). We demonstrate Repeat Station Imaging (RSI) procedures using co-registered TIR image pairs facilitate the use of image differencing to detect targets that were moving during rapid sequence imaging passes. We also show that 3D point clouds may be generated from highly overlapping UAV-TIR image sets in a forested setting using structure from motion (SfM) image processing techniques. A point cloud showing area-wide elevation values was generated from TIR imagery, but it lacked sufficient point density to reliably determine the 3D locations of monkeys.

Assessing the Utility of Satellite Imagery with Differing Spatial Resolutions for Deriving Proxy Measures of Slum Presence in Accra, Ghana.

Little research has been conducted on how differing spatial resolutions or classification techniques affect image-driven identification and categorization of slum neighborhoods in developing nations. This study assesses the correlation between satellite-derived land cover and census-derived socioeconomic variables in Accra, Ghana to determine whether the relationship between these variables is altered with a change in spatial resolution or scale. ASTER and Landsat TM satellite images are each used to classify land cover using spectral mixture analysis (SMA), and land cover proportions are summarized across Enumeration Areas in Accra and compared to socioeconomic data for the same areas. Correlation and regression analyses compare the SMA results with a Slum Index created from various socio-economic data taken from the Census of Ghana, as well as to data derived from a "hard" per-pixel classification of a 2.4 m Quickbird image. Results show that the vegetation fraction is significantly correlated with the Slum Index (Pearson's r ranges from -0.33 to -0.51 depending on which image-derived product is compared), and the use of a spatial error model improves results (multivariate model pseudo-R2 ranges from 0.37 to 0.40 by image product). We also find that SMA products derived from ASTER are a sufficient substitute for classification products derived from higher spatial resolution QB data when using land cover fractions as a proxy for slum presence, suggesting that SMA might be more cost-effective for deriving land cover fractions than the use of high-resolution imagery for this type of demographic analysis.

Monitoring habitat preserves in southern California using high spatial resolution multispectral imagery.

Habitat preserve systems have been established adjacent to the densely populated regions of southern California to support indigenous plant and animal species that are listed as rare, threatened, or endangered. Monitoring the condition of habitat across these broad preserves is necessary to ensure their long-term viability and may be effectively accomplished using remote sensing techniques with high spatial resolution visible and near-infrared (VNIR) multispectral imagery. The utility of 1 m spatial resolution VNIR imagery for detailed change detection and monitoring of Mediterranean-type ecosystems is assessed here. Image acquisition and preprocessing procedures were conducted to ensure that image-detected changes represented real changes and not artifacts. Change classification products with six spectral-based transition classes were generated using multiband image differencing (MID) for three change periods: 1998-1999, 1998-2001, and 1998-2005. Land cover changes relevant to habitat quality monitoring such as human-induced disturbance, fire, vegetation growth/recovery, and drought related vegetation stress were readily detected using the multitemporal VNIR imagery. Suggestions for operational habitat monitoring using image products and mobile geographic information system technologies are provided.

Assessment of the Spatial Co-registration of Multitemporal Imagery from Large Format Digital Cameras in the Context of Detailed Change Detection.

Large format digital camera (LFDC) systems are becoming more broadly available and regularly collect image data over large areas. Spectral and radiometric attributes of imagery from LFDC systems make this type of image data appropriate for semi-automated change detection. However, achieving accurate spatial co-registration between multitemporal image sets is necessary for semi-automated change detection. This study investigates the accuracy of co-registration between multitemporal image sets acquired using the Leica Geosystems ADS40, Intergraph Z/I Imaging® DMC, and Vexcel UltraCam-D sensors in areas of gentle, moderate, and extreme terrain relief. Custom image sets were collected and orthorectified by imagery vendors, with guidance from the authors. Results indicate that imagery acquired by vendors operating LFDC systems may be coregistered with pixel or sub-pixel level accuracy, even for environments with high terrain relief. Specific image acquisition and processing procedures facilitating this level of coregistration are discussed.

Urban Vegetation Cover and Vegetation Change in Accra, Ghana: Connection to Housing Quality.

The objectives are to (1) quantify, map, and analyze vegetation cover distributions and changes across Accra, Ghana, for 2002 and 2010; and (2) examine the statistical relationship between vegetation cover and a housing quality index (HQI) for 2000 at the neighborhood level. Pixel-level vegetation cover maps derived using threshold classification of 2002 and 2010 QuickBird normalized difference vegetation index images have very high overall accuracies and yield an estimate of 5.9 percent vegetation cover reduction over the study area between 2002 and 2010. A high degree of variance in vegetation cover for individual dates is explained by HQI at the neighborhood level, although minimal covariability between absolute or relative vegetation cover change and HQI for 2000 was observed.

Health, Poverty, and Place in Accra, Ghana: Mapping Neighborhoods.

The overall objective of our research project is to understand the spatial inequality in health in Accra, the capital city of Ghana. We also utilize GIS technology to measure the association of adverse health and mortality outcomes with neighborhood ecology. We approached this in variety of ways, including multivariate analysis of imagery classification and census data. A key element in the research has been to obtain in-person interviews from 3,200 female respondents in the city, and then relate health data obtained from the women to the ecology of the neighborhoods in which they live. Detailed maps are a requirement for these field-based activities. However, commercially available street maps of Accra tend to be highly generalized and not very useful for the kind of health and social science research being undertaken by this project, The purpose of this paper is to describe street maps that were created for the project's office in downtown Accra and used to locate households of respondents. They incorporate satellite imagery with other geographic layers to provide the most important visual interpretation of the linkage between imagery and neighborhoods. Ultimately, through a detailed analysis of spatial disparities in health in Accra, Ghana, we aim to provide a model for the interpretation of urban health inequalities in cities of urbanizing and often poor countries.

The effect of input data transformations on object-based image analysis.

The effect of using spectral transform images as input data on segmentation quality and its potential effect on products generated by object-based image analysis are explored in the context of land cover classification in Accra, Ghana. Five image data transformations are compared to untransformed spectral bands in terms of their effect on segmentation quality and final product accuracy. The relationship between segmentation quality and product accuracy is also briefly explored. Results suggest that input data transformations can aid in the delineation of landscape objects by image segmentation, but the effect is idiosyncratic to the transformation and object of interest.