J. Ronald Eastman

Application of fuzzy measures in multi-criteria evaluation in GIS

Multi-criteria evaluation (MCE) is perhaps the most fundamental of decision support operations in geographical information systems (GIS). This paper reviews two main MCE approaches employed in GIS, namely Boolean and Weighted Linear Combination (WLC), and discusses issues and problems associated with both. To resolve the conceptual differences between the two approaches, this paper proposes the application of fuzzy measures, a concept that is broader but that includes fuzzy set membership, and argues that the standardized factors of MCE belong to a general class of fuzzy measures and the more specific instance of fuzzy set membership. This perspective provides a strong theoretical basis for the standardization of factors and their subsequent aggregation. In this context, a new aggregation operator that accommodates and extends the Boolean and WLC approaches is discussed: the Ordered Weighted Average. A case study of industrial allocation in Nakuru, Kenya is employed to illustrate the different approaches.

Multi-criteria and multi-objective decision making for land allocation using GIS

Geographic Information Systems (GIS) are designed for the acquisition, management, analysis and display of georeferenced data. As such they have clear implications for informing the spatial decision making process. Subsequently, recent developments in GIS software and in the conceptual basis for decision making have led to dramatic improvements in the capabilities of GIS for resource allocation. These developments are reviewed through an examination of procedures for Multi-Criteria and Multi-Objective land allocation in GIS. Special emphasis is given to the problems of incorporating subjective expertise in the context of participatory decision making; the expression of uncertainty in establishing the relationship between evidence and the decision to be made; procedures for the aggregation of evidence in the presence of varying degrees of tradeoff between criteria; and procedures for conflict resolution and conflict avoidance in cases of multiple objective decision problems.

Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982–2011

A 30-year series of global monthly Normalized Difference Vegetation Index (NDVI) imagery derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g archive was analyzed for the presence of trends in changing seasonality. Using the Seasonal Trend Analysis (STA) procedure, over half (56.30%) of land surfaces were found to exhibit significant trends. Almost half (46.10%) of the significant trends belonged to three classes of seasonal trends (or changes). Class 1 consisted of areas that experienced a uniform increase in NDVI throughout the year, and was primarily associated with forested areas, particularly broadleaf forests. Class 2 consisted of areas experiencing an increase in the amplitude of the annual seasonal signal whereby increases in NDVI in the green season were balanced by decreases in the brown season. These areas were found primarily in grassland and shrubland regions. Class 3 was found primarily in the Taiga and Tundra biomes and exhibited increases in the annual summer peak in NDVI. While no single attribution of cause could be determined for each of these classes, it was evident that they are primarily found in natural areas (as opposed to anthropogenic land cover conversions) and that they are consistent with climate-related ameliorations of growing conditions during the study period.

LAND CHANGE IN THE SOUTHERN YUCATÁN AND CALAKMUL BIOSPHERE RESERVE: EFFECTS ON HABITAT AND BIODIVERSITY

The southern Yucatán contains the largest expanse of seasonal tropical forests remaining in Mexico, forming an ecocline between the drier north of the peninsula and the humid Petén, Guatemala. The Calakmul Biosphere Reserve resides in the center of this region as part of the Mesoamerican Biological Corridor. The reserves functions are examined in regard to land changes throughout the region, generated over the last 40 years by increasing settlement and the expansion and intensification of agriculture. These changes are documented from 1987/1988 to 2000, and their implications regarding the capacity of the reserve to protect the ecocline, forest habitats, and butterfly diversity are addressed. The results indicate that the current landscape matrix serves the biotic diversity of the reserve, with several looming caveats involving the loss of humid forests and the interruption of biota flow across the ecocline, and the amount and proximity of older forest patches beyond the reserve. The highly dynamic land cover changes underway in this economic frontier warrant an adaptive management approach that monitors the major changes underway in mature forest types, while the paucity of systematic ecological and environment-development studies is rectified in order to inform policy and practice.

Seasonal trend analysis of image time series

A procedure is introduced for the analysis of seasonal trends in time series of Earth observation imagery. Called Seasonal Trend Analysis (STA), the procedure is based on an initial stage of harmonic analysis of each year in the series to extract the annual and semi‐annual harmonics. Trends in the parameters of these harmonics over years are then analysed using a robust median‐slope procedure. Finally, images of these trends are used to create colour composites highlighting the amplitudes and phases of seasonality trends. The technique specifically rejects high‐frequency sub‐annual noise and is robust to short‐term interannual variability up to a period of 29% of the length of the series. It is, thus, a very effective procedure for focusing on the general nature of longer‐term trends in seasonality.

A Contextual Mann-Kendall Approach for the Assessment of Trend Significance in Image Time Series

One of the most common problems in estimating trends in image time series is the presence of contaminants such as clouds. There are many techniques for estimating robust trends but evaluating the significance of the trends can be difficult due to this increased variance. This article presents a novel approach called the Contextual Mann-Kendall (CMK) test for assessing significant trends. This test uses the principle of spatial autocorrelation to characterize geographical phenomena, according to which a pixel would not be expected to exhibit a radically different trend from neighboring pixels. The procedure removes serial correlation through a prewhitening process. Then, similar to the logic of the Regionally Averaged Mann-Kendall (RAMK) test, it combines the information from neighboring pixels while adjusting for cross-correlation. CMK was compared with the Mann-Kendall (MK) test in which contextual information was not involved for the mean annual NDVI over 22 years (1982–2003) in West Africa. With the MK test, ∼11% of the study area showed significant (p < 0.001) trends which increased to 16% when tested using the CMK test. Thus the CMK test produces a result that makes intuitive sense from a geographical perspective and enhances the ability to detect trends in relatively short time series.

Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms

This study assesses the performance of five Machine Learning Algorithms (MLAs) in a chronically modified mixed deciduous forest in Massachusetts (USA) in terms of their ability to detect selective timber logging and to cope with deficient reference datasets. Multitemporal Landsat Enhanced Thematic Mapperplus (ETM+) imagery is used to assess the performance of three Artificial Neural Networks ‐ Multi-Layer Perceptron, ARTMAP, Self-Organizing Map, and two Classification Tree splitting algorithms: gini and entropy rules. MLA performance evaluations are based on susceptibility to reduced training set size, noise, and variations in the training set, as well as the operability/transparency of the classification process. Classification trees produced the most accurate selective logging maps (gini and entropy rule decision tree mean overall map accuracy � 94 percent and mean per-class kappa of 0.59 and 0.60, respectively). Classification trees are shown to be more robust and accurate when faced with deficient training data, regardless of splitting rule. Of the neural network algorithms, self-organizing maps were least sensitive to the introduction of noise and variations in training data. Given their robust classification capabilities and transparency of the classselection process, classification trees are preferable algorithms for mapping selective logging and have potential in other forest monitoring applications.

Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico

This research examines the spatio-temporal trends in Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) time series to ascribe land use change and precipitation to observed changes in land cover from 1982 to 2007 in the Mexican Yucatán Peninsula, using seasonal trend analysis (STA). In addition to discrete land cover transitions across the study region, patterns of agricultural intensification, urban expansion and afforestation in protected areas have enacted changes to the seasonal patterns of apparent greenness observed through STA greenness parameters. The results indicate that the seasonal variation in NDVI can be used to distinguish among different land cover transitions, and the primary differences among these transitions were in changes in overall greenness, peak annual greenness and the timing of the growing season. Associations between greenness trends and precipitation were weak, indicating a human-dominated system for the 26 years examined. Changes in the states of Campeche, Quintana Roo and Yucatán appear to be associated with pasture cultivation, urban expansion-extensive cultivation and urban expansion-intensive cultivation, respectively.

Thematic mapper detection of changes in the leaf area of closed canopy pine plantations in Central Massachusetts

Abstract Remote sensing studies of conifer forests have previously reported that the Thematic Mapper Band 4/Band 3 ratio is positively correlated with regional differences in leaf area index (LAI). Our study was an attempt to determine whether Landsat Thematic Mapper data can be used to detect differences and changes in the LAI of closed canopy pine plantations on a local scale in central Massachusetts. Field measurements of LAI were obtained using locally-derived allometric relationships between leaf area and trunk diameter (DBH). A thinning treatment, which reduced the LAI of one of the larger plantations by more than 25%, resulted in a significant decrease ( P Such a reduction in LAI would demonstrate the limitations of allometric equations for evaluating LAI under conditions in which the relationship between leaf area and DBH may be changing from year to year. It also would explain why no significant relationship ( P > 0.1) was found between the 4/3 ratio and the LAI of the different unthinned plantations which had LAI values ranging from 3.96 to 7.01. We conclude that the TM sensor may be a better guide to moderate changes and differences in the LAI of closed canopy pine plantations at local scales than field measurements involving allometric equations.

Integrated Analysis of Ecosystem Interactions with Land‐Use Change: The Southern Yucatan Peninsular Region

The southern Yucatan peninsular region is a seasonal tropical forest biome that experiences drought, hurricane, and agricultural disturbance. Substantial agricultural expansion over the past 50 years has opened and fragmented much of the forest surrounding the Calakmul Biosphere Reserve, posing a series of threats to the coupled human-environment systems. These threats are traced in a conceptual model borrowed from vulnerability studies and detailed in regard to ecosystem responses, including forest structure and composition, above-ground biomass, soil nutrients, balance in species and biotic diversity, and invasive species. The land-use and land-cover changes underway pose trade-offs in ecosystem services and raise several scalar issues important to deforestation studies.