Ola Hall

A Multiscale Object-Specific Approach to Digital Change Detection

Abstract Landscape spatial pattern is dependent not only on interacting physiographic and physiological processes, but also on the temporal and spatial scales at which the resulting patterns are assessed. To detect significant spatial changes occurring through space and time three fundamental components are required. First, a multiscale dataset must be generated. Second, a change detection framework must be applied to the multiscale dataset. Third, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce an object-specific multiscale digital change detection approach. This approach incorporates multitemporal SPOT Panchromatic (Pan) data, object-specific analysis (OSA), object-specific up-scaling (OSU), marker-controlled watershed segmentation (MCS) and image differencing change detection. By applying this framework to SPOT Pan data, image-objects that have changed between registration dates can be identified and delineated at their characteristic scale of expression. Results illustrate that this approach has the ability to automatically detect changes at multiple scales as well as suppress sensor related noise. This study was conducted in the forest region of the Orebro Administrative Province, Sweden.

Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation

Complex systems, such as landscapes, are composed of different critical levels of organization where interactions are stronger within levels than among levels, and where each level operates at relatively distinct time and spatial scales. To detect significant features occurring at specific levels of organization in a landscape, two steps are required. First, a multiscale dataset must be generated from which these features can emerge. Second, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce a framework for the automatic definition of multiscale landscape features using object-specific techniques and marker-controlled watershed segmentation. By applying this framework to a high-resolution satellite scene, image-objects of varying size and shape can be delineated and studied individually at their characteristic scale of expression. This framework involves three main steps: 1) multiscale dataset generation using an object-specific analysis and upscaling technique, 2) marker-controlled watershed transformation to automatically delineate individual image-objects as they evolve through scale, and 3) landscape feature identification to assess the significance of these image-objects in terms of meaningful landscape features. This study was conducted on an agro-forested region in southwest Quebec, Canada, using IKONOS satellite data. Results show that image-objects tend to persist within one or two scale domains, and then suddenly disappear at the next, while new image-objects emerge at coarser scale domains. We suggest that these patterns are associated to sudden shifts in the entire image structure at certain scale domains, which may correspond to critical landscape thresholds.

Characterization and quantification of data voids in the shuttle Radar topography mission data

The goal of this study was to characterize and quantify the occurrence of data voids in data from the Shuttle Radar Topography Mission (SRTM) for the conterminous United States. For this purpose, SRTM data and corresponding data from the national elevation data were downloaded in 21 samples spatially organized to cover the main topography of the U.S. Void locations in SRTM data were compared to terrain attributes and subsequently the area of individual data voids to the same attributes. It was found that data voids amounted to 0.3% of the total dataset. Data voids were found in all topographic settings but more often in slopes steeper than approximately 20/spl deg/ that face south (170/spl deg/), and also in flat areas such as lakes and rivers. It was also found that more than 50% of all data voids were composed of connected pixels in groups less than six pixels. The largest data voids could be attributed to water bodies, while the rest could be explained by terrain-radar interaction characteristics.

A method for landscape regionalization based on fuzzy membership signatures

The aim of this paper is to present a new method for regionalization that can be used in landscape analysis and planning. The approach in this study concentrates on the possibility to use membership functions developed from training sites as a mean to characterize whole regions. Instead of traditional classification routines a fuzzy method was developed. The method is described with an example from a study area located in Sweden. Pre-classified and unclassified satellite data were used in the study. The frequency distribution of three land cover types was calculated for three different training sites. The frequency distributions were transformed into membership functions. As such they describe the graded membership for every frequency pixel to all the training sites. Thereafter, were the membership images combined through a weighted linear combination. The result shows the membership for every location in the study area to one of the training sites. The images were also classified into one resulting image showing the crisp regions of the study area. An accuracy assessment of the method was performed. The result showed an overall classification accuracy of 86% compared to existing regional divisions of the study area.

Remote Sensing in Social Science Research

Since the early days of satellite remote sensing in the 1950’s, accessibility, quality, and scope of remote sensing image data has been continuously improving, making it a rich data source with a wide range of applications. Today, the use of remote sensing techniques and data is commonplace within many disciplines in the natural sciences. Although there are quite a few examples of remote sensing to be found in the social sciences, developments here have, on the whole, been less pronounced. This paper investigates 1) how remote sensing data has been put to use in social science studies, and 2) how social science could better utilize the huge potential of remote sensing data. The first part of the paper gives an overview of existing types of remote sensing techniques and data collection. The second part consistsof a review of social science applications of remote sensing data. In the conclusions it is argued that remote sensing data is at its most valuable in the social sciences when used in combination with traditional methods such as surveys, public records, interviews and direct observation. (Less)

Saving the woodpeckers: Social Capital, Governance, and Policy performance

This article investigates if higher levels of social capital, better governance structures, and a more ambitious conservation policy are positively linked to the ability of states to address biodiversity loss. Serving this purpose is a data set containing estimates of woodpecker diversity in 20 European countries. These data are argued to be a more valid indicator of biodiversity than most other available cross-national measures of environmental quality. A seemingly unrelated regression analysis reveals that none of the indicators are linked to higher levels of woodpecker diversity, which in turn leads to the conclusion that present institutions, environmental policies, and social structures have negligible effects on biodiversity compared to long-term landscape transformations.

World poverty, environmental vulnerability and population at risk for natural hazards

Abstract Please click here to download the map associated with this article. The objective of the accompanying map is to show the relation between world poverty, environmental vulnerability and population at risk for natural hazards. Sub-national infant mortality rates are used as proxy for poverty and mapped as a bivariate choropleth map together with national levels of environmental vulnerability. Past density and distribution of natural hazards were mapped on to a textonequarter degree grid and presented as an inset map. An inset map with global population densities is also provided. All maps are in Lambert cylindrical equal-area projection. The main map scale is 1:100 000 000. According to the result from the bivariate mapping of poverty and environmental vulnerability, the world can be stratified into three groups. 1) Regions with low poverty rates and relatively high degree of environmental vulnerability (e.g. Scandinavia, North America). 2) Regions with high levels of poverty and a relatively low degree of environmental vulnerability (e.g. parts of East-Africa, parts of Russia). 3) Regions with high poverty rates and high degrees of environmental vulnerability levels (e.g. middle parts of Asia). When this information is combined with that of population density and natural hazard density and distribution it is clear that those belonging to group three are very vulnerable with usually high population densities and a location prone to natural hazards. This type of small scale mapping is a good way of exploring relations between variables, observing geographical patterns and bringing forward new hypotheses for future research directions, and should be viewed as a complement to large scale mapping and field inventories.

Nighttime lights and population changes in Europe 1992–2012

Nighttime satellite photographs of Earth reveal the location of lighting and provide a unique view of the extent of human settlement. Nighttime lights have been shown to correlate with economic development and population but little research has been done on the link between nighttime lights and population change over time. We explore whether population decline is coupled with decline in lighted area and how the age structure of the population and GDP are reflected in nighttime lights. We examine Europe between the period of 1992 and 2012 using a Geographic Information System and regression analysis. The results suggest that population decline is not coupled with decline in lighted area. Instead, human settlement extent is more closely related to the age structure of the population and to GDP. We conclude that declining populations will not necessarily lead to reductions in the extent of land development.

From Census to Grids: Comparing the Gridded Population of the World with Swedish Census Records

The increased availability of digital spatial data combined with improved capabilities of Geographic Information Systems (GIS) have allowed for the development of several global population distribution databases, such as the GPW, and LandScan. Making population distribution data available as a high-resolution raster database which facilitates rapid GIS analysis at the local level and for any zoning. Due to the complex nature of population as a geographical variable, several approaches have been adopted to estimate their spatial distribution, including statistical modeling, surface modeling, and cartographic methods. However, many of these methods require assumptions that oversimplify the reality or disaggregate population totals based on the heuristic or empirical parameters. Recently, critical voices were heard, questioning the quality and usability of gridded population data. In this paper, we compare gridded population data products for parts of Sweden with high-resolution population records obtained from the Swedish National Registry through the Regional Office of Scania, Sweden. Ground-truth consists of the total population in Scania located as points at the center coordinates of their real estate (

Assessing Recovery from the 2004 Indian Ocean Tsunami : An Application of Night-time Light Data and Vegetation Index

It has been 10 years since the Indian Ocean Tsunami caused serious damage to the coastal areas in South and Southeast Asia. The effects on vegetation and human settlements in the affected areas were enormous. This study presents the results of an analysis estimating the long-term recovery using two longitudinal remotely sensed dataset: 1. Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI), a dataset accounting for change in the landscape and vegetation; and 2. Defense Meteorological Satellite Program-Optical Line Scanner (DMSP-OLS) night-time light data in order to estimate the effects on human and economic activities. It is evident from the results of this study that the night-time light and vegetation index datasets can both be beneficial in identifying changes caused by natural disasters and can be used to track recovery. The results using night-time light indicates a large loss of lighted area but also a rapid recovery of night-time light after the tsunami. Already in year 2005-2006, the levels of lighted area and sum of the lighting (SOL) intensity reached the same levels as pre-tsunami. For MODIS vegetation index, a drop can be observed in 2005/2006 on locations close to the coastline using 1 year temporal resolution; however, when utilizing the 16 day temporal resolution, the impact of the tsunami is illustrated as a dramatic drop, mostly in pixels located within 3km from the coast. Following the drop in vegetation index due to the tsunami, it was observed that most pixels exhibited at least some level of recovery in 2 years after the event. (Less)