Thomas W. Gillespie

Measuring and modelling biodiversity from space

The Earth is undergoing an accelerated rate of native ecosystem conversion and degradation and there is increased interest in measuring and modelling biodiversity from space. Biogeographers have a long-standing interest in measuring patterns of species occurrence and distributional movements and an interest in modelling species distributions and patterns of diversity. Much progress has been made in identifying plant species from space using high-resolution satellites (QuickBird, IKONOS), while the measurement of species movements has become commonplace with the ARGOS satellite tracking system which has been used to track the movements of thousands of individual animals. There have been significant advances in land-cover classifications by combining data from multi-passive and active sensors, and new classification techniques. Species distribution modelling has been growing at a striking rate and the incorporation of spaceborne data on climate, topography, land cover, and vegetation structure has great potential to improve models. There have been significant advances in modelling species richness, alpha diversity, and beta diversity using multisensors to quantify land-cover classifications and landscape metrics, measures of productivity, and measures of heterogeneity. Remote sensing of nature reserves can provide natural resources managers with near real-time data within and around reserves that can be used to support conservation efforts anywhere in the world. Future research should focus on incorporating recent spaceborne sensors, more extensive integration of available spaceborne imagery, and the collection and dissemination of high-quality field data. This will improve our understanding of the distribution of life on earth.

Remotely Sensed Spectral Heterogeneity As a Proxy of Species Diversity: Recent Advances and Open Challenges

Abstract Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time.

PREDICTING WOODY-PLANT SPECIES RICHNESS IN TROPICAL DRY FORESTS: A CASE STUDY FROM SOUTH FLORIDA, USA

Tropical dry forests are one of the worlds most endangered forest types. Currently there are no comparative data on extent or levels of species richness for remaining forest fragments. This research identifies landscape metrics and spectral indices that can be applied at the stand and patch level to predict woody-plant species richness in tropical dry forests. This study was undertaken in 18 stands of tropical dry forest with nine sites in the Florida Keys and nine sites within an urban–agricultural matrix in mainland Florida, USA. Woody-plant species richness was quantified at the stand level (belt transects totaling 500 m2) and patch level (presence/absence data for 65 native tropical plants ≤2.5 cm dbh) for all study sites. Landsat Enhanced Thematic Mapper Plus (ETM+) satellite images (pixel resolution 30 × 30 m) were used to assess the utility of landscape metrics (forest patch area, nearest neighbor distance, shape index, boundary complexity) and spectral indices (normalized-difference vegetation index [NDVI] for nine pixels and 500 pixels directly over transects, and all pixels in the forest patch area) for predicting stand- and patch-level species richness. The 18 stands of tropical dry forest sampled in this study included 4248 woody plants, representing 71 species. Islands in the Florida Keys had higher levels of woody-plant species richness than mainland sites. There was a significant positive relationship between mean NDVI for the nine pixels over each stand and stand species richness and a significant negative relationship between species richness and standard deviation of NDVI for nine pixels over each stand. The density of evergreen plants explained 66% of the variability in mean NDVI. At the patch level, forest patch area and mean NDVI at the stand, 500-pixel, and patch level were all positively associated with patch species richness. However, combining forest patch area with NDVI significantly improved the prediction of patch species richness. Results from this study support the species–energy theory at the level of a forest stand and patch and suggest that a first-order approximation of woody-plant species richness in stands and patches of tropical dry forest is possible in biodiversity hot spots.

Mental Health in Sumatra after the Tsunami

OBJECTIVES We assessed the levels and correlates of posttraumatic stress reactivity (PTSR) of more than 20,000 adult tsunami survivors by analyzing survey data from coastal Aceh and North Sumatra, Indonesia. METHODS A population-representative sample of individuals interviewed before the tsunami was traced in 2005 to 2006. We constructed 2 scales measuring PTSR by using 7 symptom items from the Post Traumatic Stress Disorder (PTSD) Checklist-Civilian Version. One scale measured PTSR at the time of interview, and the other measured PTSR at the point of maximum intensity since the disaster. RESULTS PTSR scores were highest for respondents from heavily damaged areas. In all areas, scores declined over time. Gender and age were significant predictors of PTSR; markers of socioeconomic status before the tsunami were not. Exposure to traumatic events, loss of kin, and property damage were significantly associated with higher PTSR scores. CONCLUSIONS The tsunami produced posttraumatic stress reactions across a wide region of Aceh and North Sumatra. Public health will be enhanced by the provision of counseling services that reach not only people directly affected by the tsunami but also those living beyond the area of immediate impact.

Baghdad Nights: Evaluating the US Military ‘Surge’ Using Nighttime Light Signatures

Baghdad Nights: Evaluating the US Military ‘Surge’ Using Nighttime Light Signatures John Agnew Thomas W. Gillespie Jorge Gonzalez Brian Min CCPR-064-08 December 2008 Latest Revised: December 2008 California Center for Population Research On-Line Working Paper Series

Assessment and prediction of natural hazards from satellite imagery

Since 2000, there have been a number of spaceborne satellites that have changed the way we assess and predict natural hazards. These satellites are able to quantify physical geographic phenomena associated with the movements of the earths surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires). Most of these satellites contain active or passive sensors that can be utilized by the scientific community for the remote sensing of natural hazards over a number of spatial and temporal scales. The most useful satellite imagery for the assessment of earthquake damage comes from high-resolution (0.6 m to 1 m pixel size) passive sensors and moderate resolution active sensors that can quantify the vertical and horizontal movement of the earths surface. High-resolution passive sensors have been used successfully to assess flood damage while predictive maps of flood vulnerability areas are possible based on physical variables collected from passive and active sensors. Recent moderate resolution sensors are able to provide near real-time data on fires and provide quantitative data used in fire behavior models. Limitations currently exist due to atmospheric interference, pixel resolution, and revisit times. However, a number of new microsatellites and constellations of satellites will be launched in the next five years that contain increased resolution (0.5 m to 1 m pixel resolution for active sensors) and revisit times (daily < 2.5 m resolution images from passive sensors) that will significantly improve our ability to assess and predict natural hazards from space.

Tropical dry forests in New Caledonia

Tropical dry forest is the most endangered major vegetation type in the New Caledonia biodiversity hotspot. Vegetation surveys following a transect method used by Gentry were undertaken in two tropical dry forest sites, Ouen-Toro and Pindai, in order to compare species richness, floristic composition, and structure. Pindai contained significantly higher species richness than Ouen-Toro, although there was little difference in forest structure. Tropical dry forest sites in New Caledonia were compared to seven other biodiversity hotspots with tropical dry forest where Gentrys transect method was employed. New Caledonia and other tropical dry forests on islands contain significantly lower species richness than mainland tropical dry forests in biodiversity hotspots. However, New Caledonia contained the highest number of threatened species based on IUCN global conservation categories. Tropical dry forest in New Caledonia appears to be the worlds most endangered tropical dry forest based on the extent of forest, number of reserves, and threatened species. Management of tropical dry forests on private and community lands is absolutely imperative to the long-term persistence of this ecosystem.

Remote sensing of animals

The improved accuracy and precision of animal tracking via satellites has made a significant impact on quantifying large-scale biogeographic patterns for a variety of taxa with important implications for conservation and natural resource management. This paper reviews research undertaken from 1995 to 1999 to provide an overview of advances in the remote sensing of animal movements in both terrestrial and marine environments and to identify promising trends for biogeographic research in the twenty-first century. Remote sensing of animals by satellite provides a new method to test a number of biogeographic hypotheses related to migration and can identify a number of environmental correlates associated with the distributions of species. Tracking of smaller species and increases in sample size are sure to occur as transmitter size and cost continue to decrease in the next decade. Geographers can significantly contribute to the understanding of species dispersal and distributional patterns by combining real-time and archived global and regional datasets with existing data from past studies and future research projects. Only four studies used GIS data or remote sensed imagery in this review, while the remaining studies cited used simple digital line graphs of countries, topography, land and sea boundaries.

Mortality, The Family and The Indian Ocean Tsunami*

Over 130,000 people died in the 2004 Indian Ocean tsunami. The correlates of survival are examined using data from the Study of the Tsunami Aftermath and Recovery (STAR), a population-representative survey collected in Aceh and North Sumatra, Indonesia, before and after the tsunami. Children, older adults and females were the least likely to survive. Whereas socio-economic factors mattered relatively little, the evidence is consistent with physical strength playing a role. Pre-tsunami household composition is predictive of survival and suggests that stronger members sought to help weaker members: men helped their wives, parents and children, while women helped their children.

Impacts of Spatial Variability on Aboveground Biomass Estimation from L-Band Radar in a Temperate Forest

Estimation of forest aboveground biomass (AGB) has become one of the main challenges of remote sensing science for global observation of carbon storage and changes in the past few decades. We examine the impact of plot size at different spatial resolutions, incidence angles, and polarizations on the forest biomass estimation using L-band polarimetric Synthetic Aperture Radar data acquired by NASA’s Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne system. Field inventory data from 32 1.0 ha plots (AGB 0.5 ha), suggesting a stability of field-estimated biomass at scales of about 1.0 ha. UAVSAR backscatter was linked to the field estimates of aboveground biomass to develop parametric equations based on polarized returns to accurately map biomass over the entire radar image. Radar backscatter values at all three polarizations (HH, VV, HV) were positively correlated with field aboveground biomass at all four spatial scales, with the highest correlation at the 1.0 ha scale. Among polarizations, the cross-polarized HV had the highest sensitivity to field estimated aboveground biomass (R2 = 0.68). Algorithms were developed that combined three radar backscatter polarizations (HH, HV, and VV) to estimate aboveground biomass at the four spatial scales. The predicted aboveground biomass from these algorithms resulted in decreasing estimation error as the pixel size increased, with the best results at the 1 ha scale with an R2 of 0.67 (p < 0.0001), and an overall RMSE of 44 Mg·ha−1. For AGB < 150 Mg·ha−1, the error reduced to 23 Mg·ha−1 (±15%), suggesting an improved AGB prediction below the L-band sensitivity range to biomass. Results also showed larger bias in aboveground biomass estimation from radar at smaller scales that improved at larger spatial scales of 1.0 ha with underestimation of −3.62 Mg·ha−1 over the entire biomass range.