Monitoring disasters with a constellation of satellites – type examples from the International Charter ‘Space and Major Disasters’

In the case of a major disaster, information derived from satellite observation is not only highly useful, it may at times be indispensable because of the damage caused by the disaster to ground infrastructure. The International Charter ‘Space and Major Disasters’ (‘the Charter’) has been one of the primary sources of satellite data for the past 11 years to cover events like floods, fires, tsunamis, ocean storms, earthquakes, volcanic eruptions and oil spills. With the growing membership of the Charter, an increasingly large number of sensors are now available, which can be planned with the required temporal frequency and spectral range to cover a disaster event. Some of the type Charter activation cases are reported in this article to demonstrate the innovative use of multi-satellite imagery for disaster response.     

The Society of University Cartographers – Its Growth and International Status

An attempt to capture and reflect the history, evolution, nature and status of the Society of Cartographers (SoC). Although a small organization numerically, the skill, expertise and dedication of its active members, though summer schools, technical journal, newsletters and especially its rich and informative website, are providing access to knowledge and experience both nationally and globally. This influence is also reflected through the direct involvement of leading SoC office-holders in other national and international mapping organisations. The SoC clearly punches above its weight. It has made a difference to the world of mapping, and continues to do so     

An exploratory method for detecting multi-level clumps in the distribution of points – a computational tool,VCM (variable clumping method)

This paper shows an explanatory tool (a FORTRAN program linked to GIS software), called VCM (Variable Clumping Method), for detecting statistically significant `multi-level clumps' in the distribution of points. The multi-level clumps imply a set of clumps whose clumping radius varies from a small radius to a large radius (in spirits, VCM is similar to the K-function method). Compared with the ordinary (fixed radius) clumping method, VCM can detect overall significant clumps in the distribution of points. Received: 8 March 1999/Accepted: 17 September 1999     

The Palestine Exploration Fund Map (1871–1877) of the Holy Land as a Tool for Analysing Landscape Changes: the Coastal Dunes of Israel as a Case Study

Abstract

The Palestine Exploration Fund (PEF) maps (1871–1877) are highly praised for their accuracy and completeness; however, no systematic analysis of their accuracy has been done to date. To study the potential of these 1:63,360 maps for a quantitative analysis of land cover changes over a period of time, I have compared them to 20th century topographic maps. The map registration error of the PEF maps was 74.4 m using 123 control points of trigonometrical stations and a 1st order polynomial. The median RMSE of all control and test points (n = 1104) was 153.6 m. As a case study of land cover changes, the area of coastal dunes as shown on the PEF maps was compared with that shown on British Mandate 1:20,000 topo-cadastral maps from c. 1930. In five of the six areas analysed, the yearly dunes movement rate was above the estimated annual error due to data resolution (2.96 m/year). The rate of dune movement south of Acre was found to be between 3.9 and 6.3 m/year (depending on the method used for map registration) between 1874 and 1930. Care should be taken when analysing historical maps, as it cannot be assumed that their accuracy is consistent at different parts or for different features depicted on them.     

The nature and possible causes of land cover change (1984 –1996) along a rainfall gradient in southeastern Botswana

Land cover change analysis was undertaken in semi-arid southeastern Botswana. The aim was to determine how remotely sensed data could be applied over time and under different rainfall regimes to help assess the relative significance of biophysical and human factors in causing land cover change in a rapidly evolving developing world context. To this purpose, land cover changes were studied along an east (hardveld)-west (sandveld) gradient of decreasing rainfall and decreasing population density. Three years of Thematic Mapper imagery from 1984, 1994 and 1996, covering the period from the 1980s drought to the 1990s ‘normal’ rainfall regime were analysed using supervised classification techniques. Land cover change analysis revealed that over a large part of the study area the dry and more biophysically vulnerable western sandveld showed greater vegetation recovery than the eastern hardveld with its more productive soils and higher rainfall. Underlying causes behind this apparent reversal of trends are inferred to be mainly socioeconomic in nature and particularly related to higher population density due to the rise of salaried urban occupation opportunities in the hardveld. This work concludes that, while biophysical causes of change are important, the human dimension is regarded as being more significant especially where human factors negate otherwise positive biophysical effects in an agrarian developing country.     

‘Video Strip Mapping (VSM)’ for Time‐sequential Monitoring of Revegetation of a Pipeline Route

Abstract

A frequent requirement in post‐construction management of a land pipeline route is to detect revegetation status on a time-sequential basis (usually 5‐10 years). The principal advantage of video is the capacity to provide, in a cost‐effective manner, information required for such a very narrow and long strip target utilising the narrow view angle and dynamic stereo coverage. This paper reports that a video strip monitoring technique requiring solely end lap has been specifically developed with focus on such a narrow and long strip target (e.g. 20‐30m wide and several hundred km long). Such large‐scale video systems are often discussed as being inadequate for a change‐detection application due to geometric and radiometric calibration problems. In spite of such limitations, changes of major thematic land cover classes, particularly for the rapidly recovered target of a pipeline corridor, could be detected successfully by visual or quantitative methods and through further ‘patch dynamics’ analysis in a GIS environment. The results of this study indicate that such calibration problems are generally not a major drawback in acquiring change‐detection information in a practical operational application which requires mostly generalized thematic mapping of relatively simple classes. The results also indicate that Video Strip Mapping (VSM) could be extensively used for other examples of linear thematic mapping.     

The potential of Pléiades images with high angle of incidence for reconstructing the coastal cliff face in Normandy (France)

To monitor chalk cliff face along the Normandy coast (NW France) which is prone to erosion, we tested the potential of cliff face 3D reconstruction using pairs of images with high angle of incidence at different dates from the agile Pléiades satellites. The verticality aspect of the cliff face brings difficulties in the 3D reconstruction process. Furthermore, the studied area is challenging mainly because the cliff face is north-oriented (shadow). Pléiades images were acquired over several days (multi-date stereoscopic method) with requested incidence angles until 40°. 3D reconstructions of the cliff face were compared using two software: ASP® and ERDAS IMAGINE®. Our results are twofold. Firstly, despite ASP® provides denser point clouds than ERDAS IMAGINE® (an average of 1.60 points/m² from 40° incidence angle stereoscopic pairs on the whole cliff face of Varengeville-sur-Mer against 0.77 points/m² respectively), ERDAS IMAGINE® provides more reliable point clouds than ASP® (precision assessment on the Varengeville-sur-Mer cliff face of 0.31 m ± 2.53 and 0.39 m ± 4.24 respectively), with a better spatial distribution over the cliff face and a better representation of the cliff face shape. Secondly, the quality of 3D reconstructions depends mostly on the amount of noise from raw images and on the shadow intensity on the cliff face (radiometric quality of images).     

Spatial analysis of human-induced vegetation productivity decline over eastern Africa using a decade (2001–2011) of medium resolution MODIS time-series data

Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities.     

The socio-environmental data explorer (SEDE): a social media–enhanced decision support system to explore risk perception to hazard events

ABSTRACT

Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS.     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat,India

Although poor precipitation due to delayed arrival and/or early retreat of the southwest monsoon is considered the chief architect of drought in India, heat waves may also play a crucial role in the intensification of droughts. In the Indian subcontinent, occurrence of heat waves during the pre-monsoon and high air-temperature in the subsequent monsoon season imparts thermal stress on vegetation causing degradation of vegetation health (VH). In the present study, various vegetation indices and land-use/land-cover data derived from multi-sensor satellite have been used to assess VH and agricultural drought in Gujarat during 1981–2010. This Geographical Information Systems-based study has also used heat wave and temperature data to analyze the adverse effects of high temperature on VH. The time series of Vegetation Condition Index and Temperature Condition Index (TCI) has shown that the combined influence of moisture-stress and thermal stress determines the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). A strong correlation among aboveground air-temperature, the TCI and the VHI indicates definite influence of thermal stress on VH. Further, a systematic variation and strong resemblance between temperature, crop yield, TCI and VHI has established the impact of thermal stress on agricultural productivity.     

Image matching as a data source for forest inventory – Comparison of Semi-Global Matching and Next-Generation Automatic Terrain Extraction algorithms in a typical managed boreal forest environment

Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method.Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant.     

The long-wave infrared (8-12 μm) spectral features of selected rare earth element—Bearing carbonate,phosphate and silicate minerals

Rare earth elements (REEs) are a group of metals essential to high technology industries. This high demand, combined with a high supply risk, has led to an understanding that REEs are critical to society. Despite the potential that hyperspectral imaging (HSI) data offers for a fast and non-invasive characterization of the REEs, it is still poorly understood whether REEs have some information in the long-wave infrared (LWIR; 8–12 μm) wavelength range that can be used for their identification. To partially fill this gap, we have investigated the spectroscopy of twelve REE-bearing mineral samples using relatively high spatial and spectral resolution LWIR hyperspectral imaging data. These samples were formerly characterized using electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and hyperspectral imaging data acquired in the 0.4–2.5 μm wavelength range. Results from these analyses were compared to and used to guide the analysis of the HSI data recorded in the LWIR range. This information was further compared to a reference spectral library of rare earth oxides. Our findings suggest that the spectral features of the samples can generally be traced to the asymmetric degenerate stretching and bending modes of the X-O (X = C, Si, P) groups. Moreover and contrary to what has been observed in the shorter wavelengths, there are no definitive spectral features in the LWIR wavelength region that could be assigned to any specific REE.     

The structural and thematic mapping of coral reefs using high Resolution SPOT data: Application to the Tétembia reef (New Caledonia)

This paper describes the application of high‐resolution SPOT data for the structural and thematic mapping of shallow coral reefs. Results are presented as structural and thematic maps of the main reef environment types. Different substrate themes including soft bottom, coral débris, coral rubble, and living coral have been identified. The separation of various living coral themes, as measured by differences in areal coral density, has also been achieved.     

A global,high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km² of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal ‘inland water’ biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds – effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).     

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

ABSTRACT

Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa?=?0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html     

Classification from Landsat TM of Indurated Volcanic Materials (tepetates) of the Mexican Neo‐volcanique Belt

Abstract

Tepetates, altered indurated and sterile volcanic tuffs, are located among the neo‐volcanic belt in central Mexico. They are divided into three classes according to the erosion level: outcrop tepetates, discontinuous tepetates (in process of erosion), and underlying tepetates (related to fragile areas with high erosion risk). For local communities and peasants, the existence of tepetates is a heavy constraint, with the impossibility to cultivate the land as it is. In order to know the localization and extension of tepetates, we experimented with various classification techniques of a Landsat Thematic Mapper satellite image. Masking techniques were used, assisted both by a DEM and photo interpretation, in addition to radiometric segmentation to obtain a map of tepetates in a study area. The resulting map showed the location of outcrop tepetates well, in agreement with existing soil maps, but underestimated underlying tepetates and high‐risk erosion localities, due mainly to the heterogeneity of the classes at the scale used (30 m pixel).