Spatial Resolution Multispectral Imagery Research Articles

Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances

Pine plantations in Australia are subject to a range of abiotic and biotic damaging agents that affect tree health and productivity. In order to optimise management decisions, plantation managers require regular intelligence relating to the status and trends in the health and condition of trees within individual compartments. Remote sensing technology offers an alternative to traditional ground-based assessment of these plantations. Automated estimation of foliar crown health, especially in degraded crowns, can be difficult due to mixed pixels when there is low or fragmented vegetation cover. In this study we apply a linear spectral unmixing approach to high spatial resolution (50cm) multispectral imagery to quantify the fractional abundances of the key image endmembers: sunlit canopy, shadow, and soil. A number of Pinus radiata tree crown attributes were modelled using multiple linear regression and endmember fraction images. We found high levels of significance (r2=0.80) for the overall crown colour and colour of the crown leader (r2=0.79) in tree crowns affected by the fungal pathogen Sphaeropsis sapinea, which produces both needle necrosis and chlorosis. Results for stands associated with defoliation and chlorosis through infestation by the aphid Essigella californica were lower with an r2=0.33 for crown transparency and r2=0.31 for proportion of crown affected. Similar analysis of data from a nitrogen deficient site produced an outcome somewhat in between the other two damaging agents. Overall the sunlit canopy image fraction has been the most important variable used in the modelling of forest condition for all damaging agents.

Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data

Fine spatial resolution multispectral imagery and light detection and ranging (lidar) data capture differing, yet complementary characteristics of forest structure. Using a dataset consisting of fine spatial resolution multispectral imagery, discrete-return lidar data, and detailed ground-based measurements of individual tree attributes, we applied an automatic tree delineation routine (tree identification and delineation algorithm) to compare and contrast remotely sensed predictions with field observations. The results indicate the automatically extracted crowns derived from lidar data matched tree crown area (coefficient of determination r2 = 0.46, n = 36) and height (r2 = 0.88, n = 36) better than spatial clusters defined in the multispectral imagery (crown area r2 = 0.26, n = 36) for individual trees that were identifiable in both the lidar and multispectral imagery. Differences between crown delineation characteristics were related to the information content of the lidar and multispectral fine spatial resolution data. Investigation of the spectral characteristics of objects defined in the multispectral imagery revealed strong relationships between the vertical positions derived from the lidar data and the apparent multispectral reflectance, with low-reflectance spatial clusters occurring lower in the forest canopy. The application of lidar and multispectral datasets together, in the context of tree crown delineation, provides information not available from either data source independently.

Forest Characterization in the Central Plateau of Yellowstone National Park Using Multispectral High Resolution Digital Photography

Detailed knowledge of forest structure is an important component in research focusing on forest biodiversity monitoring, carbon budgeting studies, fire modeling and forest inventory estimation. In forest inventory mapping, high spatial resolution multispectral imagery are becoming a valuable and often a critical tool to effective resource management planning. In this report, we describe and illustrate the forestry applications of a multispectral digital imaging camera system (DuncanTech MS3100) that was experimentally flown over several study areas in the Central Plateau of Yellowstone National Park, in July, 2001. The purpose of this demonstration is three fold; first we will apply geostatistical methods, co-kriging specifically, to model forest canopy components such as height, canopy thiclmess, species, density and structure; second, we will examine the spectral and spatial characteristics of seedling regeneration in the 1988 burn sites; finally, forest stand characteristics such as stem counts, crown species and amount of deadfall (potential fire fuel) will be estimated for selected sites. Our results will be compared to information acquired from coarser resolution, multispectral Landsat TM data obtained on the same date.

Local-scale variation in malaria infection amongst rural Gambian children estimated by satellite remote sensing

We investigated local-scale variation in malaria transmission and infection in children within a continuous landscape by retrospective spatial analysis of entomological and clinical data collected during 1988 and 1989 in The Gambia, West Africa. Parasite prevalence was negatively correlated with vector abundance and exposure to malaria parasites in 10 villages where entomological surveillance had been carried out. Variation in bednet use did not explain this finding. Mosquito-breeding habitat was retrospectively mapped using 20-m spatial resolution multispectral SPOT satellite imagery from 1988. From these data we estimated by linear regression the risk of exposure to malaria parasites in 26 villages where clinical surveys of children had been made. As exposure increased, so did parasite prevalence; but at higher levels of exposure, parasite prevalence declined. Our findings demonstrate marked differences in exposure to malaria in villages over distances of less than 2 km from mosquito breeding sites and suggest that there are also large differences in immunity between neighbouring settlements.

Using very high spatial resolution multispectral satellite sensor imagery to monitor refugee camps

Detailed geographic information is a key factor in decision making processes during refugee relief operations. The forthcoming commercial very high spatial resolution (VHSR) satellite sensors will be capable of acquiring multispectral (MS) images at spatial resolutions of 1m (panchromatic) and 4m (multispectral) of refugee camps and their environment. This work demonstrates how refugee camp environment, area and population can be analysed using a VHSR MS satellite sensor image from the Russian KVR-1000 sensor. This image, with a spatial resolution of 3.3m, was used to study Thailand's Site 2 refugee camps, which were established to accommodate Khmer refugees on the Thai-Kampuchean border. At the time of image acquisition, the total population of Site 2's five refugee camps was close to 143000. The VHSR MS image was found to be suitable for mapping the refugee camp environment and area. A statistically significant linear relationship between camp area and population was determined. Accordingly, the study suggests that VHSR MS images in general may be useful for refugee camp planning and management and points toward the utilization of forthcoming commercial VHSR MS satellite sensor images in humanitarian relief operations.