Scott A. Macomber

Classification and change detection using landsat TM data : When and how to correct atmospheric effects?

Abstract The electromagnetic radiation (EMR) signals collected by satellites in the solar spectrum are modified by scattering and absorption by gases and aerosols while traveling through the atmosphere from the Earths surface to the sensor. When and how to correct the atmospheric effects depend on the remote sensing and atmospheric data available, the information desired, and the analytical methods used to extract the information. In many applications involving classification and change detection, atmospheric correction is unnecessary as long as the training data and the data to be classified are in the same relative scale. In other circumstances, corrections are mandatory to put multitemporal data on the same radiometric scale in order to monitor terrestrial surfaces over time. A multitemporal dataset consisting of seven Landsat 5 Thematic Mapper (TM) images from 1988 to 1996 of the Pearl River Delta, Guangdong Province, China was used to compare seven absolute and one relative atmospheric correction algorithms with uncorrected raw data. Based on classification and change detection results, all corrections improved the data analysis. The best overall results are achieved using a new method which adds the effect of Rayleigh scattering to conventional dark object subtraction. Though this method may not lead to accurate surface reflectance, it best minimizes the difference in reflectances within a land cover class through time as measured with the Jeffries–Matusita distance. Contrary to expectations, the more complicated algorithms do not necessarily lead to improved performance of classification and change detection. Simple dark object subtraction, with or without the Rayleigh atmosphere correction, or relative atmospheric correction are recommended for classification and change detection applications.

Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors

Landsat 7 ETM+ provides an opportunity to extend the area and frequency with which we are able to monitor the Earths surface with fine spatial resolution data. To take advantage of this opportunity it is necessary to move beyond the traditional image-by-image approach to data analysis. A new approach to monitoring large areas is to extend the application of a trained image classifier to data beyond its original temporal, spatial, and sensor domains. A map of forest change in the Cascade Range of Oregon developed with methods based on such generalization shows accuracies comparable to a map produced with current state-of-the-art methods. A test of generalization across sensors to monitor forest change in the Rocky Mountains indicates that Landsat 7 ETM+ data can be combined with earlier Landsat 5 TM data without retraining the classifier. Methods based on generalization require less time and effort than conventional methods and as a result may allow monitoring of larger areas or more frequent monitoring at reduced cost. One key component to achieving this goal is the improved availability and affordability of Landsat 7 imagery. These results highlight the value of the existing Landsat archive and the importance for continuity in the Landsat Program.

Mapping forest vegetation using landsat TM imagery and a canopy reflectance model

Abstract Estimates of mean tree size and cover for each forest stand from an invertible forest canopy reflectance model are part of a new forest vegetation mapping system. Image segmentation defines stands which are sorted into general growth forms using per-pixel image classifications. Ecological models based on terrain relations predict species associations for the conifer, hardwood, and brush growth forms. The combination of the model-based estimates of tree size and cover with species associations yields general-purpose vegetation maps useful for a variety of land management needs. Results of timber inventories in the Tahoe and Stanislaus National Forests indicate the vegetation maps form a useful basis for stratification. Patterns in timber volumes for the strata reveal that the cover estimates are more reliable than the tree size estimates. A map accuracy assessment of the Stanislaus National Forest shows high overall map accuracy and also illustrates the problems in estimating tree size.

A Neural Network Method for Mixture Estimation for Vegetation Mapping

While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife habitat studies, can benefit from knowledge of mixes. This article examines various algorithms that use data from the Landsat Thematic Mapper (TM) satellite to estimate mixtures of vegetation types within forest stands. Included in the study are maximum likelihood classification and linear mixture models as well as a new methodology based on the ARTMAP neural network. Two paradigms are considered: classification methods, which describe stand-level vegetation mixtures as mosaics of pixels, each identified with its primary vegetation class; and mixture methods, which treat samples as blends of vegetation, even at the pixel level. Comparative analysis of these mixture estimation methods, tested on data from the Plumas National Forest, yields the following conclusions: 1) Accurate estimates of proportions of hardwood and conifer cover within stands can be obtained, particularly when brush is not present in the understory; 2) ARTMAP outperforms statistical methods and linear mixture models in both the classification and the mixture paradigms; 3) topographic correction fails to improve mapping accuracy; and 4) the new ARTMAP mixture system produces the most accurate overall results. The Plumas data set has been made available to other researchers for further development of new mapping methods and comparison with the quantitative studies presented here, which establish initial benchmark standards.

Forest mapping with a generalized classifier and Landsat TM data

Monitoring landcover and landcover change at regional and global scales often requires Landsat data to identify and map landscape features and patterns with sufficient detail. Analytical methods based on image-by-image interpretation are too time-consuming and labor-intensive for studies of large areas to be undertaken with any degree of frequency. One potential solution is to develop algorithms or classifiers that can be generalized beyond the arena of the initial training to new images from different spatial, temporal or sensor domains. Building upon earlier success with a generalized classifier to monitor forest change, we now address the question of generalization for classifications of stable landcovers. We evaluate the ability of a supervised neural network, Fuzzy ARTMAP, to identify conifer forest across time and space with Landsat Thematic Mapper (TM) images for a region in northwest Oregon. We also assess the effects of atmospheric corrections on generalized classification accuracies. Using midsummer images atmospherically corrected with a simple dark-object-subtraction (DOS) method, there is no statistically significant loss of accuracy as the classification is extended from the initial training image to other images from the same scene (path and row): temporal generalization is successful. Extending the classifier across space and time to nearby scenes results in a mean decline of 8–13% accuracy depending on the atmospheric correction used. Obvious sources of error, such as seasonality, solar angle variation, and complexity of landcover identification, do not explain the decline in error. Additionally, the patterns in generalization accuracies are complex, and the relationship between pairs of training and testing images is not necessarily reciprocal, i.e., good training data are not necessarily good testing data. Simple DOS atmospheric corrections produce classifications with comparable accuracies as classifications from the more complex radiative transfer corrections. These findings are based on over 200 classifications. A high degree of variability in the classification accuracies underscores the importance of extensive, in-depth analysis of remote sensing techniques and applications, and highlights the potential problem for misleading results based on just a few tests. Generalization is well suited for multitemporal classifications of one Landsat scene. Using simple DOS and midsummer images, generalization offers the opportunity for frequent landcover mapping of a Landsat scene without having to retrain the classifier for each time period of interest. However, at this point, the utility of regional landcover mapping with a generalized classifier remains limited.

Mapping and monitoring conifer mortality using remote sensing in the Lake Tahoe Basin

Abstract A prolonged drought in the western United States has resulted in alarming levels of mortality in conifer forests. Satellite remote sensing holds the potential for mapping and monitoring the effects of such environmental changes over large geographic areas in a timely manner. Results from the application of a forest canopy reflectance model using multitemporal Landsat TM imagery and field measurements, indicate conifer mortality can be effectively mapped and inventoried. The test area for this project is the Lake Tahoe Basin Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The difference in cover estimates between the dates forms the basis for stratifying stands into mortality classes, which are used as both themes in a map and the basis of the field sampling design. Field measurements from 61 stands collected in the summer of 1992 indicate 15 % of the original timber volume in the true fir zone died between 1988 and 1992. The resulting low standard error of 11 % for this estimate indicates the utility of these mortality classes for detecting areas of high mortality. Also, the patterns in the estimated mean timber volume loss for each class follow the expected trends. The results of this project are immediately useful for fire hazard management, by providing both estimates of the degree of overall mortality and maps showing its location. They also indicate current remote sensing technology may be useful for monitoring the changes in vegetation that are expected to result from climate change.