Geography

It is essential to measure whether maps of various scenarios of future land change are meaningfully different, because differences among such maps serve to inform land management. This paper compares the output maps of different scenarios of future land change in a manner that contrasts two different approaches to account for the uncertainty of the simulated projections. The simpler approach interprets the scenario storyline concerning the quantity of each land change transition as assumption, and then considers the range of possibilities concerning the value added by a simulation model that specifies the spatial allocation of land change. The more complex approach estimates the uncertainty of future land maps based on a validation measurement with historic data. The technique is illustrated by a case study that compares two scenarios of future land change in the Plum Island Ecosystems of northeastern Massachusetts, in the United States. Results show that if the model simulates only the spatial allocation of the land changes given the assumed quantity of each transition, then there is a clearly bounded range for the difference between the raw scenario maps; but if the uncertainties are estimated by validation, then the uncertainties can be so great that the output maps do not show meaningful differences. We discuss the implications of these results for a future research agenda of land change modeling. We conclude that a productive approach is to use the simpler method to distinguish clearly between variations in the scenario maps that are due to scenario assumptions versus variations due to the simulation model.

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.

A time series of geographic images can be viewed from two perspectives: as a set of images, each image representing a slice of time, or as a grid of temporal profiles (one at each pixel location). In the context of Principal Components Analysis (PCA), these different orientations are known as T-mode and S-mode analysis respectively. In the sparse literature on these modes it is recognized that they produce different results, but the reasons have not been fully explored. In this paper we investigate the interactions between space-time orientation and standardization and centering in PCA. Standardization refers to the eigenanalysis of the inter-variable correlation matrix rather than the variance-covariance matrix while centering refers to the subtraction of the mean in the development of either matrix. Using time series of monthly anomalies in lower tropospheric temperature from the Microwave Sounding Unit (MSU) as well as in CO2 in the middle troposphere from the Atmospheric Infrared Sounder (AIRS), we show that with T-mode PCA, standardization has the effect of giving equal weight to each time step while centering has the effect of detrending over time. In contrast, with S-mode PCA, standardization has the effect of giving equal weight to each location in space while centering detrends over space. Further, in the formation of components, S-mode PCA preferences patterns that are prevalent over space while T-mode PCA preferences patterns that are prevalent over time. The two orientations thus provide complementary insights into the nature of variability within the series.

ABSTRACT 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 Document Type: Research Article DOI: http://dx.doi.org/10.1111/j.1467-9671.2011.01280.x Affiliations: Graduate School of Geography, Clark University Publication date: October 1, 2011 $(document).ready(function() { var shortdescription = $(".originaldescription").text().replace(/\\&/g, '&').replace(/\\, '<').replace(/\\>/g, '>').replace(/\\t/g, ' ').replace(/\\n/g, ''); if (shortdescription.length > 350){ shortdescription = "" + shortdescription.substring(0,250) + "... more"; } $(".descriptionitem").prepend(shortdescription); $(".shortdescription a").click(function() { $(".shortdescription").hide(); $(".originaldescription").slideDown(); return false; }); }); Related content In this: publication By this: publisher In this Subject: Geography By this author: Neeti, Neeti ; Eastman, J. Ronald GA_googleFillSlot("Horizontal_banner_bottom");

ABSTRACT Spectral decomposition techniques have widely been used to analyze ocean and atmospheric properties over time. One such technique is Canonical Correlation Analysis (CCA) which is commonly used for describing coupled systems using time series of geographical data. However, the manner in which the time series is organized (referred to here as orientation) can have a major impact on the analytical results. For example, in Principal Components Analysis, organizing a geographic time series such that the time steps are considered as variables, known as T-mode orientation, can yield very different results from one where the geographic locations (pixels) are considered to be the variables, known as S-mode orientation. In S- and T- orientation mode, variable (geophysical variable such as sea surface temperature) dimension is constant. Since CCA involves relationships between multiple sets of variables over space and time, it therefore involves slightly different orientation modes. This research explores two orientation modes, S-P and T-P for CCA. S-P refers to the mode of analysis used when the primary focus is the relationship between samples in different variables in space over time. T-P refers to the mode of analysis when the primary focus is the relationship between samples in different variables in time over space. Commonly, CCA is carried out in S-P mode in which the canonical variates generated are orthogonal in time but are not orthogonal in space. With T-P mode CCA, the canonical variates are orthogonal in space but are not orthogonal in time. The differences between the two orientations are analyzed by evaluating their descriptive and predictive capabilities using monthly global Sea Surface Temperature (SST) and Lower Tropospheric Temperature (TLT) anomaly images for 1982-2010. The results shows that spatial centering in T-P orientation removes the temporal structure and therefore relationships between the temporal structures of the datasets and temporal centering in S-P orientation removes the spatial structure and therefore relationships between the spatial structures of the datasets. While S-P mode CCA is known for temporal prediction, T-P mode CCA has the potential to be used for spatial prediction or for filling data gaps. Specifically, in understanding ocean-atmosphere dynamics, the T-P orientation mode CCA is able to find coupled systems prevalent in time compared to space (e.g., NAO and AMO). The S-P orientation mode CCA is able to find coupled systems prevalent in space compared to time (e.g., the Atmospheric Bridge).

ABSTRACT With earth observation data, one of the primary concerns is the discovery of recurrent patterns over time. For example, the ENSO phenomenon is a major climatological pattern of global significance. As a spatial/two-dimensional extension of Singular Spectrum Analysis (SSA), Multichannel Singular Spectrum Analysis (MSSA) seeks to uncover the temporal evolution of recurrent space-time patterns within a specified time frame (known as the embedding dimension) by a method of spectral decomposition equivalent to Extended Principal Components Analysis. However, it suffers from the same limitations as PCA with regard to the propensity to develop components that are mixtures of multiple dominant patterns. In this paper we introduce a novel procedure we call Multichannel Empirical Orthogonal Teleconnection (MEOT) analysis as a simple extension of the logic of Empirical Orthogonal Teleconnections (EOT). A global sea surface temperature dataset spanning the 1982-2007 time period is utilized to explore the similarities and differences between MSSA and MEOT. The techniques are applied with a 13 month embedding dimension to extract spatio-temporal patterns that exhibit clear basis vectors in quadrature. Findings indicate that MEOT is capable of detecting more patterns in quadrature than MSSA. MEOT identifies three climate events as quadratures corresponding to the El Niño Southern Oscillation (ENSO), the Atlantic Meridional Mode (AMM) and the Atlantic Niño/ Tropical Southern Atlantic (TSA) mode. All of these climate events have phase change within a year. MSSA in contrast, only identified the ENSO event. Moreover, since MEOT does not suffer from a bi-orthogonality constraint, it is capable of extracting fewer mixed modes of variability than MSSA. Thus, results suggest a better identification and representation of individual climate events by the MEOT method.

ABSTRACT This article introduces T-mode pre-filtered canonical correlation analysis (CCA) as an extension to T-mode CCA for identifying recurring spatial patterns over time shared between two image time series. T-mode pre-filtered CCA does this by first pre-filtering the individual image time series using T-mode PCA and then identifying the joint spatial variability between the principal components of the two series. There are two major advantages of the T-mode pre-filtered CCA over the T-mode CCA. Since the T-mode principal components are orthogonal, estimation of the inverse matrix for CCA becomes possible when the original data sets are highly correlated, which is mostly true in the case of image time series. The second advantage is that reducing the dimensionality of the original data decreases the number of variables substantially (typically from hundreds down to less than 10) compared to the number of observations and thus resolves the statistical requirement for such methods to have substantially more observations than variables. As will be illustrated through a case study, T-mode pre-filtered CCA finds shared relationships between spatially recurring patterns in the different data fields consistent with T-mode CCA.

ABSTRACT This article introduces four new modes of principal component analysis (PCA) to investigate space-time variability in an image time series. Using the concept of tensors, an image time series can be understood as a space-time cube and can be analysed using six different orientations by grouping the basic elements (voxels) of the cube across different dimensions. Voxels grouped across columns or rows of the cube to produce vectors result in profiles. Voxels grouped across different planes to produce matrices result in slices. The traditional S-mode and T-mode PCA are thus the profile modes and slice modes across time and across space, respectively. This research introduces two profile-mode orientations across longitude and latitude and two slice-mode orientations across longitude-time and latitude-time. The research shows that a more complete understanding of the spatio-temporal variability in the data set can be achieved by investigating these different orientation modes, as individual modes have the capability of capturing variability in a particular dimension of a spatio-temporal data set. A case study was carried out using weekly anomalies of the AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data) sea surface height product filtered for tropical instability waves (TIWs) for a three-year time period from 1997 to 1999 in the tropical Pacific region. The results show that PCA with longitude as the dimension of variability and latitude-time as the dimension of variability were able to capture the TIW and barotropic Rossby wave propagation across the equatorial Pacific. The other two orientation modes were able to detect dominant latitudinal locations for TIW.

Ecological fiscal transfers (EFT) transfer public revenue between governments within a country based on ecological indicators. EFT can compensate subnational governments for the costs of conserving ecosystems and in principle can incentivize greater ecological conservation. We review established EFT in Brazil, Portugal, France, China and India, and emerging or proposed EFT in ten more countries. We analyse common themes related to EFT emergence, design and effects. EFT have grown rapidly from US$0.35 billion yr−1 in 2007 to US$23 billion yr−1 in 2020. We discuss the scope of opportunity to expand EFT to other countries by ‘greening’ intergovernmental fiscal transfers. The transfer of public funds between governments within a country based on ecological indicators is an emerging tool in environmental policy. A review of extant and proposed schemes identifies challenges and opportunities to expand the use of this instrument.