Medium Resolution Satellite Data Research Articles

Change detection at Conde municipality coastal zone, State of Bahia, Brazil - using medium resolution satellite data

Change detection at Conde municipality coastal zone, State of Bahia, Brazil - using medium resolution satellite data

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation. Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.

Estimating regional forest cover in East Texas using Enhanced Thematic Mapper (ETM+) data

The USDA Forest Service, through its Forest Inventory and Analysis (FIA) program, periodically estimates forest/non-forest area at the county level using aerial photographs. Satellite-based remotely sensed data and digital image processing techniques could substantially reduce the time required to generate this information. Satellites collect data on a repeat basis and with higher frequency than the aerial photos that are currently used for this purpose. In addition to the forest cover estimates, the USDA could use satellite data to generate maps depicting the spatial distribution of forest cover. However, few studies have tested the utility of medium-resolution satellite data for FIA purposes. We tested the potential for using LANDSAT satellite data to obtain forest cover estimates for a six-county region in East Texas. Satellite data were processed using a combination of image classification techniques that could be repeated in other regions of the USA. Results were compared with the results of traditional photo-based estimation techniques and were comparable within a 95% confidence interval. Based on this study we recommend that medium-resolution satellite data can be used for obtaining county-level forest cover estimates.

An evaluation of the use of satellite data for monitoring early development of young Sitka spruce plantation forest growth

Summary This paper describes an evaluation of the potential of medium resolution (20‐30 m pixel size) satellite data to monitor the growth of Sitka spruce (Picea sitchensis Bong. Carr.) plantations in the UK. The aim is to develop a cost-effective methodology to allow crops to be monitored by remote sensing and, in particular, to provide a mechanism to identify compartments that fail to establish as commercially viable crops. The method used involves predicting forest variables from optical reflectance imagery and field observations using generalized linear modelling. The first stage of this process is to establish the quantitative relationship between forest structure and reflectance data from the Landsat ETM+ and SPOT 4 HRVIR sensors. This was investigated for 25 compartments of Sitka spruce ranging in age from 2 to 17 years. The generalized linear models used showed that mean height is most strongly correlated with reflectance data. There is, however, no relationship between tree density and reflectance data. Interestingly, despite the differences in spatial resolution between Landsat and SPOT data, the predictive models derived were almost identical. Independent field checking of these models indicates that the root mean square error of crop height predictions is 1.5 m. The study suggests that by using repeated satellite surveys, tree growth can be monitored for very low cost per unit area and data can easily be integrated into a GIS (Geographical Information System) allowing foresters to interrogate and visualize the predictions over large areas.

Biophysical remote sensing of arctic environments

Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.

A contextual approach to the mapping of burned areas in tropical Australian savannas using medium-resolution satellite data

Our objective was to develop and test a spectral‐spatial method to detect burned areas in savanna landscapes. The hypothesis tested was that the inclusion of spatial information (context) would better detect burning than do spectral-only tests. The spectral‐spatial measure used was the standard deviation (SD) of a near-infrared (NIR) channel for a 5 × 5 pixels window because this measure exhibits a spike on burning. This measure was compared with spectral-only tests in the Australian tropical savannas during the 2000 dry season (April‐November) using Système pour l'Observation de la Terre (SPOT) VEGETATION (VGT) data. Using a population of 23 burned areas, the superiority of the SD NIR signal was demonstrated and, using videography, the influences of surface factors on the SD NIR signal were examined. A large area (29 000 km2) comparison was made of the several burning detection tests, all referred to two manually interpreted datasets. Using the criterion of user accuracy, two spatial‐spectral tests outperformed two spectral-only tests. However, the largest common class of error was omission, indicating a failure of all tests to completely capture burned areas. This deficiency is not crippling, for it can be largely overcome by spatial filtering.

GeoComp-n, an advanced system for the processing of coarse and medium resolution satellite data. Part 2: Biophysical products for Northern ecosystems

Effective use of satellite data for environmental monitoring requires consistent, high-throughput processing of large volumes of data as it is transformed from raw measurements to useful higher-level products. "GeoComp-n", the next generation of the Geocoding and Compositing System developed at the Canada Centre for Remote Sensing, Natural Resources Canada, was developed as a software solution to this challenge, for use with satellites that provide daily data for the landmass of Canada or comparably large areas. In this paper, the authors discuss the characteristics of the algorithms and methods used in the generation of GeoComp-n products. The theoretical basis and assumptions in the algorithms are described, and the quality of the products is discussed based on validation studies. Examples of a suite of products for Canada during one 10-day period illustrate the diversity and quality of observations for the terrestrial biosphere that may be derived frequently and over large areas from satellites. Issues related to quality assessment in a production environment are also discussed.