Paul Mausel

INTEGRATING AMAZONIAN VEGETATION, LAND-USE, AND SATELLITE DATA

Attention to differential patterns and rates of secondary succession on deforested land in the Amazon Basin can help formulate future policies. Amazon deforestation is driven by policies that favor cattle over people as occupants of the frontier, not primarily population growth as in Asia. Deforestation has transformed Brazil into the worlds fourth major contributor of carbon to the atmosphere. This article discusses the following topics: How and why deforestation occured; use of Landsat satellite data to study deforestation and vegetation patterns; analytical procedures for satellite data analysis; Transamazon highway vegetational change. 62 refs., 5 figs., 3 tabs.

COMPARISON OF LAND-COVER CLASSIFICATION METHODS IN THE BRAZILIAN AMAZON BASIN

Four distinctly different classifiers were used to analyze multispectral data. Which of these classifiers is most suitable for a specific study area is not always clear. This paper provides a comparison of minimum-distance classifier (MDC), maximumlikelihood classifier (MLC), extraction and classification of homogeneous objects (ECHO), and decision-tree classifier based on linear spectral mixture analysis (DTC-LSMA). Each of the classifiers used both Landsat Thematic Mapper data and identical field-based training sample datasets in a western Brazilian Amazon study area. Seven land-cover classes— mature forest, advanced secondary succession, initial secondary succession, pasture lands, agricultural lands, bare lands, and water—were classified. Classification results indicate that the DTC-LSMA and ECHO classifiers were more accurate than were the MDC and MLC. The overall accuracy of the DTCLSMA approach was 86 percent with a 0.82 kappa coefficient and ECHO had an accuracy of 83 percent with a 0.79 kappa coefficient. The accuracy of the other classifiers ranged from 77 to 80 percent with kappa coefficients from 0.72 to 0.75.

Land use change in the Amazon estuary: Patterns of caboclo settlement and landscape management

Landsat TM scenes for 1985 and 1991 are used to produce a georeferenced map of land cover and land use for an area of the Amazon estuary inhabited by three populations of caboclos with distinct patterns of land use. This information is combined in a geographic information system with ethnographic and survey research carried out over the past 5 years to develop representative spectral “signatures” which permit measurement and differentiation of land uses and the detection of change even between small areas of managed floodplain forest and unmanaged forest, and between three distinct age/growth classes of secondary succession following deforestation. Implementation of these procedures permit the scaling up or down of research at different resolutions. Three distinct patterns of land use are examined with differential impact on the environment. Mechanized agriculture at one site has eliminated virtually all the mature upland forest and is now dominated by secondary successional vegetation. The more traditional system of diversified land use at the next site shows a subtle cycling of flooded forest to managed palm forest through time in response to the price of palm fruit and cycling in the use of fallow land. A third site, based on palm fruit extractivism, shows minimal changes in land cover due to persistent specialization on management of flooded forest extraction. There is little evidence that the community with the greatest impact on forest cover is any better off economically than the two communities which have minimal impact on the landscape. This study suggests how a balance between use and conservation in Amazonia may be achieved in floodplain and estuarine areas, and the effectiveness of monitoring these types of land cover from satellite platforms.

Spectral identification of successional stages following deforestation in the Amazon

Abstract Land use and land cover features of a 3,000 sq. km. area west of Altamira, State of Para, Brazil, along the Transamazon Highway was assessed using three dates of Landsat TM data acquired for late July/early August 1985, 2988, and 1991. These data, supplemented by field observations and interviews with land users conducted in 1992, permitted classification of nine features, including three of secondary succession (SS). The research emphasis focused on developing multitemporal field level information through remote sensing that could be used to help assess SS characteristics vital in understanding the area dynamics and processes. Research results indicate that multitemporal TM data can be used successfully to identify three SS land cover classes and their rates of change. Classification accuracy of the features of interest varied from 81 to 98 percent. Information developed from analysis of the classifications included delineation of several patterns of different speeds or rates of SS, rate and spa...

Land‐cover binary change detection methods for use in the moist tropical region of the Amazon: a comparative study

Many land‐cover change detection techniques have been developed; however, different conclusions about the value or appropriateness of each exist. This difference of opinion is often influenced by the landscape complexity of study areas and data used for analysis. Which method is most suitable for land‐cover change detection in Amazon tropical regions remains unclear. In this paper, 10 binary change detection methods were implemented and compared with respect to their capability to detect land‐cover change and no change conditions in moist tropical regions. They are image differencing (ID), modified image differencing (MID), a combination of image differencing and principal component analysis (IDPCA), principal component differencing (PCD), multitemporal PCA (MPCA), change vector analysis (CVA), vegetation index differencing (VID), image ratioing (IR), modified image ratioing (MIR), and a combination of image ratioing and PCA (IRPCA). Multi‐temporal Thematic Mapper (TM) data were used to conduct land‐cover binary change detection. Research results indicate that MID, PCD and ID using TM band 5 are significantly better than other binary change detection methods and they are recommended specifically for implementation in the Amazon basin.

Classification of successional forest stages in the Brazilian Amazon basin

Research on secondary succession in the Amazon basin has attracted great interest in recent years. However, methods used to classify successional stages are limited. This research explores a method that can be used to differentiate regrowth stages. The vegetation inventory data were collected in Altamira, Bragantina, Pedras, and Tome-Acu of the eastern Amazon basin. A nested sampling strategy, organized by region, site, plot, and subplot, was employed for field data collection. Above-ground biomass (AGB), forest stand volume (FSV), basal area, average stand height, average stand diameter (ASD), age, ratio of tree biomass to total biomass (RTB), ratio of tree volume to total volume, and ratio of tree basal area to total basal area were calculated at the site level. Canonical discriminant analysis (CDA) was used to differentiate successional stages and to identify the best forest stand parameters to distinguish these stages. This research indicates that the CDA approach can be used to classify successional forest stages, but using RTB or a combination of two stand parameters such as AGB and ASD are more feasible and recommended in practice. # 2003 Elsevier Science B.V. All rights reserved.

Application of spectral mixture analysis to Amazonian land-use and land-cover classification

Abundant vegetation species and associated complex forest stand structures in moist tropical regions often create difficulties in accurately classifying land-use and land-cover (LULC) features. This paper examines the value of spectral mixture analysis (SMA) using Landsat Thematic Mapper (TM) data for improving LULC classification accuracy in a moist tropical area in Rondônia, Brazil. Different routines, such as constrained and unconstrained least-squares solutions, different numbers of endmembers, and minimum noise fraction transformation, were examined while implementing the SMA approach. A maximum likelihood classifier was also used to classify fraction images into seven LULC classes: mature forest, intermediate secondary succession, initial secondary succession, pasture, agricultural land, water, and bare land. The results of this study indicate that reducing correlation between image bands and using four endmembers improve classification accuracy. The overall classification accuracy was 86.6% for the seven LULC classes using the best SMA processing routine, which represents very good results for such a complex environment. The overall classification accuracy using a maximum likelihood approach was 81.4%. Another finding is that use of constrained or unconstrained solutions for unmixing the atmospherically corrected or raw Landsat TM images does not have significant influence on LULC classification performances when image endmembers are used in a SMA approach.

Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf coast.

Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA hyperspectral imagery and image transformation and classification techniques for mapping black mangrove populations on the south Texas Gulf coast. AISA hyperspectral imagery was acquired from two study sites and both minimum noise fraction (MNF) and inverse MNF transforms were performed. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the noise-reduced hyperspectral imagery and to the band-reduced MNF imagery for distinguishing black mangrove from associated plant species and other cover types. Accuracy assessment showed that overall accuracy varied from 84 percent to 95 percent for site 1 and from 69 percent to 91 percent for site 2 among the eight classifications for each site. The MNF images provided similar or better classification results compared with the hyperspectral images among the four classifiers. Kappa analysis showed that there were no significant differences among the four classifiers with the MNF imagery, though maximum likelihood provided excellent overall and class accuracies for both sites. Producer’s and user’s accuracies for black mangrove were 91 percent and 94 percent, respectively, for site 1 and both 91 percent for site 2 based on maximum likelihood applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments.

Spectral analysis of coastal vegetation and land cover using AISA + hyperspectral data

This paper describes a spectral analysis of several coastal land cover types in South Padre Island, Texas using AISA+ hyperspectral remote sensing data. AISA+ hyperspectral data (1.5 metre) were acquired throughout the area on 9 March 2005. Data over mangrove areas were converted to percent reflectance using four 8×8 metre reflectance tarps (4%, 16%, 32% and 48%) and empirical line calibration. These data were then compared to percent reflectance values of other terrestrial features to determine the ability of AISA+ data to distinguish features in coastal environments. Results suggest that these data may be appropriate to discriminate coastal mangrove vegetation and provide researchers with high resolution spatial and spectral information to more effectively manage coastal ecosystems.

Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin

Above-ground biomass estimation of successional and mature forests in moist tropical regions is attracting increasing attention. Because of complex stand structure and abundant vegetation species, rarely has remote-sensing research been successfully conducted in biomass estimation for moist tropical areas. In this paper, two study areas in the Brazilian Amazon basin—Altamira and Bragantina— with different biophysical characteristics were selected. Atmospherically corrected Thematic Mapper (TM) images and field vegetation inventory data were used in the analysis, and different vegetation indices and texture measures were explored. Multiple regression models were developed through integration of image data (including TM bands, different vegetation indices, and texture measures) and vegetation inventory data. These models were used for biomass estimation in both selected study areas. This study concludes that neither TM spectral bands nor vegetation indices alone are sufficient to establish an effective model for biomass estimation, but multiple regression models that consist of spectral and textural signatures improve biomass estimation performance. The models developed are especially suitable for above-ground biomass estimation of dense vegetation areas.