A Comparison of Multisensor Integration Methods for Land Cover Classification in the Brazilian Amazon

GIScience & Remote SensingGIScience & Remote Sensing, Volume 48, Number 3 / July-September 2011

Dengsheng Lu, Guiying Li, Emilio Moran, Luciano Dutra, and Mateus Batistella

“Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods—principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)—were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%-5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%-6.1% and 7.6%-12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution.”

Multiscale Analyses of Mammal Species Composition – Environment Relationship in the Contiguous USA

PLoS ONEPLoS ONE 6(9): e25440, Published 27 September 2011

Rafi Kent, Avi Bar-Massada, and Yohay Carmel

“Relationships between species composition and its environmental determinants are a basic objective of ecology. Such relationships are scale dependent, and predictors of species composition typically include variables such as climate, topographic, historical legacies, land uses, human population levels, and random processes. Our objective was to quantify the effect of environmental determinants on U.S. mammal composition at various spatial scales.

 Explained variance rates of individual environmental variables in mammal species composition: a) climatic variables; b) topographic variables and c) LULC variables.

Explained variance rates of individual environmental variables in mammal species composition: a) climatic variables; b) topographic variables and c) LULC variables.

“We found that climate was the predominant factor affecting species composition, and its relative impact increased in correlation with the increase of the spatial scale. Another factor affecting species composition is land-use–land-cover. Our findings showed that its impact decreased as the spatial scale increased. We provide quantitative indication of highly significant effect of climate and land-use–land-cover variables on mammal composition at multiple scales.”

Targeting Pediatric Pedestrian Injury Prevention Efforts: Teasing the Information Through Spatial Analysis

The Journal of Trauma Journal of Trauma-Injury Infection & Critical Care, 71(5):S511-S516, November 2011

Statter, Mindy; Schuble, Todd; Harris-Rosado, Michele; Liu, Donald; and Quinlan, Kyran

“Background: Pediatric pedestrian injuries remain a major cause of childhood death, hospitalization, and disability. To target injury prevention efforts, it is imperative to identify those children at risk. Racial disparities have been noted in the rates of pediatric pedestrian injury and death. Children from low-income families living in dense, urban residential neighborhoods have a higher risk of sustaining pedestrian injury. Geographic information systems (GIS) analysis of associated community factors such as child population density and median income may offer insights into prevention.

“Methods: Using trauma registry E-codes for pedestrian motor vehicle crashes, children younger than 16 years were identified, who received acute care and were hospitalized at the University of Chicago Medical Center, a Level I pediatric trauma center, after being struck by a motor vehicle from 2002 to 2009. By retrospective chart review and review of the Emergency Medical Services run sheets, demographic data and details of the crash site were collected. Crash sites were aggregated on a block by block basis. A “hot spot” analysis was performed to localize clusters of injury events. Using Gi* statistical method, spatial clusters were identified at different confidence intervals using a fixed distance band of 400 m (∼¼ mile). Maps were generated using GIS with 2000 census data to evaluate race, employment, income, density of public and private schools, and density of children living in the neighborhoods surrounding our medical center where crash sites were identified. Spatial correlation is used to identify statistically significant locations.

All crashes, 2002-2009.

All crashes, 2002-2009.

“Results: There were 3,521 children admitted to the University of Chicago Medical Center for traumatic injuries from 2002 to 2009; 27.7% (974) of these children sustained injuries in pedestrian motor vehicle injuries. From 2002 to 2009, there were a total of 106 traumatic deaths, of which 29 (27.4%) were due to pedestrian motor vehicle crashes. Pediatric pedestrian motor vehicle crash sites occurred predominantly within low-income, predominantly African-American neighborhoods. A lower prevalence of crash sites was observed in the predominantly higher income, non–African-American neighborhoods.

“Conclusions: Spatial analysis using GIS identified associations between pediatric pedestrian motor vehicle crash sites and the neighborhoods served by our pediatric trauma center. Pediatric pedestrian motor vehicle crash sites occurred predominantly within low-income, African-American neighborhoods. The disparity in prevalence of crash sites is somewhat attributable to the lower density of children living in the predominantly higher income, non-African-American neighborhoods, including the community immediately around our hospital. Traffic volume patterns, as a denominator of these injury events, remain to be studied.”