Remote Sensing of Mosquito Habitat

Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat

ARSC Group

Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment.

Overview of urban mosquito habitat mapping and modeling phases. The LIDAR and multispectral data processing steps are used as input to the CART based LULC classification. The LULC is used to derive a Vegetation, Impervious and Soil (V-I-S) map [ 10 ]. These map products contribute to mosquito habitat and simulation (outlined with dashed lines and arrows) and development of urban mosquito abundance map.

In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classification.

LiDAR point cloud image of elevation (ft) for a subset of the study area at the University of Arizona campus. Buildings and trees are easily recognized by their height, shape, and texture. The dark blue elevation values are generally used to create the DEM.

Classification and Regression Tree (CART) analyses were used to compare and contrast the enhancements and accuracy of the multi-sensor urban land cover classifications. Eight urban land-cover classes were developed for the city of Tucson, Arizona, USA.

These land cover classes focus on pervious and impervious surfaces and microclimate landscape attributes that impact mosquito habitat such as water ponds, residential structures, irrigated lawns, shrubs and trees, shade, and humidity. Results show that synergistic use of LiDAR, multispectral and the Normalized Difference Vegetation Index data produced the most accurate urban land cover classification with a Kappa value of 0.88.

Error matrix results for the classification performed with 2007 4-band NAIP data, NDVI data, and 2008 LiDAR CHM data.

Fusion of multi-sensor data leads to a better land cover product that is suitable for a variety of urban applications such as exploring the relationship between neighborhood composition and adult mosquito abundance data to inform public health issues.

V-I-S model showing percent cover type based on aggregating the 1m CART classification results to 10 m pixels. Water and shadow are masked in this visualization of multiple attributes that affect mosquito habitat.

Overview of urban mosquito habitat mapping and modeling phases. The LIDAR and multispectral data processing steps are used as input to the CART based LULC classification. The LULC is used to derive a Vegetation, Impervious and Soil (V-I-S) map [ 10 ]. These map products contribute to mosquito habitat and simulation (outlined with dashed lines and arrows) and development of urban mosquito abundance map.

LiDAR point cloud image of elevation (ft) for a subset of the study area at the University of Arizona campus. Buildings and trees are easily recognized by their height, shape, and texture. The dark blue elevation values are generally used to create the DEM.

Error matrix results for the classification performed with 2007 4-band NAIP data, NDVI data, and 2008 LiDAR CHM data.

V-I-S model showing percent cover type based on aggregating the 1m CART classification results to 10 m pixels. Water and shadow are masked in this visualization of multiple attributes that affect mosquito habitat.