The Influence of DEM Quality on Mapping Accuracy of Coniferous- and Deciduous-Dominated Forest Using TerraSAR‑X Images

Remote Sensing, 2012, 4(3), 661-681

Sonia M. Ortiz, Johannes Breidenbach, Ralf Knuth and Gerald Kändler

“Climate change is a factor that largely contributes to the increase of forest areas affected by natural damages. Therefore, the development of methodologies for forest monitoring and rapid assessment of affected areas is required. Space-borne synthetic aperture radar (SAR) imagery with high resolution is now available for large-scale forest mapping and forest monitoring applications. However, a correct interpretation of SAR images requires an adequate preprocessing of the data consisting of orthorectification and radiometric calibration. The resolution and quality of the digital elevation model (DEM) used as reference is crucial for this purpose. Therefore, the primary aim of this study was to analyze the influence of the DEM quality used in the preprocessing of the SAR data on the mapping accuracy of forest types.

Application of the classification model in Biberach.

Application of the classification model in Biberach. (a) Map of deciduous- and
coniferous-dominated forest based on combined leaf-on and leaf-off TerraSAR-X images (acquired in 2008 and 2009) preprocessed with the ALS DTM; (b) official forest stand map (established 2007); (c) ortho-photographs (acquired on 2007).

“In order to examine TerraSAR-X images to map forest dominated by deciduous and coniferous trees, High Resolution SpotLight images were acquired for two study sites in southern Germany. The SAR images were preprocessed with a Shuttle Radar Topography Mission (SRTM) DEM (resolution approximately 90 m), an airborne laser scanning (ALS) digital terrain model (DTM) (5 m resolution), and an ALS digital surface model (DSM) (5 m resolution). The orthorectification of the SAR images using high resolution ALS DEMs was found to be important for the reduction of errors in pixel location and to increase the classification accuracy of forest types. SAR images preprocessed with ALS DTMs resulted in the highest classification accuracies, with kappa coefficients of 0.49 and 0.41, respectively. SAR images preprocessed with ALS DTMs resulted in greater accuracy than those preprocessed with ALS DSMs in most cases. The classification accuracy of forest types using SAR images preprocessed with the SRTM DEM was fair, with kappa coefficients of 0.23 and 0.32, respectively.Analysis of the radar backscatter indicated that sample plots dominated by coniferous trees tended to have lower scattering coefficients than plots dominated by deciduous trees. Leaf-off images were only slightly better suited for the classification than leaf-on images. The combination of leaf-off and leaf-on improved the classification accuracy considerably since the backscatter changed between seasons, especially in deciduous-dominated forest.”

Read the paper [PDF]

Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data

Remote Sensing, 2012, 4(4), 810-829

Sandra Eckert

“Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression.

Overview and zoom map of the study area.

Overview and zoom map of the study area.

“Pearson’s correlation coefficients revealed that (a) texture measures correlated more with biomass and carbon than spectral parameters, and (b) correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%). For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%). These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a) developing and applying forest stratum–specific models, and (b) including textural information in addition to spectral information.”

Clinton Climate Initiative Receives 2012 Special Achievement in GIS Award

Clinton FoundationLast month at the Esri International User Conference, Clinton Climate Initiative (CCI) received the 2012 Special Achievements in GIS Award. The award recognized CCI for using geographic information system (GIS) technology to help countries monitor their carbon levels.

CCI’s Forestry Program is developing forestry projects and carbon measurement systems that help governments and local communities receive compensation for preserving and re-growing forests. As global warming is caused by increased carbon dioxide in the atmosphere from burning fossil fuels – and deforestation accounts for about 15% of total carbon dioxide emissions in the world – scientists predict that if governments and communities don’t take action to reduce carbon dioxide emissions, our world will face increasingly drastic consequences ranging from stronger heat waves to more droughts and floods to increasing sea level. All of these affect agriculture, food security, viability of coastal cities, and water availability around the world.

In order to reduce emissions, governments and economies must use less fossil fuels and increase use of energy efficient technologies and renewable technologies. CCI’s Forestry Program focuses on helping developing countries reverse deforestation and plant new trees. If countries are able to show that they can monitor and verify that they are reducing their carbon dioxide emissions, countries become eligible for funding to manage their forest programs and other low-carbon economic activities.

CCI was recognized with the 2012 Special Achievement in GIS Award for helping the country of Guyana become eligible for $70 million in forest-based payments from the government of Norway. Guyana is now using this funding to facilitate specific elements of a Low Carbon Development Plan envisioned and put in place by former Guyana President Bharrat Jagdeo. This project is part of CCI’s Forestry Program and has been supported by the Rockefeller Foundation and the governments of Norway and Australia.

CCI uses GIS technology as a centerpiece of forest carbon measurement, reporting, and verification (MRV) systems for developing countries. GIS is one of three legs of the platform–Data, Models, and GIS–that allows countries to determine how much carbon they have, how it is changing, and how the drivers of deforestation and forest degradation can be monitored and adjusted as required.

With GIS systems in place for forestry, developing countries can be eligible for direct payments through international agreements based on the effectiveness of their MRV systems. Once in place, the GIS systems can be used more broadly by the countries for other resource development, land surveys, and determination of land tenure.

Read more about CCI’s forestry projects.

[Source: Clinton Foundation press release]

University of Pittsburgh Geologists Map Prehistoric Climate Changes in Canada’s Yukon Territory

Pitt study one of many across the nation focused on understanding Arctic region’s climate changes

Researchers at the University of Pittsburgh have joined an international group of scientists to study past climate changes in the Arctic. Comprising geologists from Pitt’s Department of Geology and Planetary Science, the team has analyzed sedimentary and geochemical records of water-level changes in Rantin Lake, located in the boreal forest of Canada’s southeastern Yukon Territory. The results were published online in the April issue of Journal of Paleolimnologyas one of 18 articles dedicated to reconstructing Arctic lake sediments climate and environmental changes during the Holocene (about 12,000 years before present day).

“During the last 10,000 years, there have been certain times in which rapid climate change events occurred,” said David Pompeani, lead author and a Pitt PhD geology student. “By analyzing Rantin Lake, we’ve contributed a piece of the puzzle toward mapping the timing and magnitude of these prehistoric events throughout the Arctic.”

Rantin Lake is part of a watershed containing a series of small lakes hydrologically connected through groundwater flow. The regional climate is subarctic and characterized by warm, wet summers and dry, cold winters. The lake is located at 60 degrees north in the Canadian Arctic, only 30 degrees away from the North Pole, where climate change is expected to be amplified.

In July 2006, the Pitt team—including Mark Abbott, associate professor of geology and planetary science, and Byron Steinman, a former PhD geology student (now a postdoctoral researcher at Penn State University)—collected two sediment cores from the lake for analysis. The sediment cores were split and analyzed for paleoclimate proxy indicators, including geochemical composition, sedimentary structure, and macrofossil content (that which is visible without a microscope). The amount of water in a lake is directly related to its depth. Therefore, a loss in water during droughts is recorded by drop in lake levels, whereas wet periods are characterized by deep waters.

Using these proxy indicators, the researchers were able to make inferences about past variations in the balance between precipitation and evaporation in the southern Yukon. A comparison of the lake-level proxies with a previously developed fossil pollen record from the same lake found that rapid vegetation changes over the Holocene also occurred during shifts in the precipitation/evaporation balance, suggesting hydrologic conditions played an integral role in the evolution of the Yukon’s ecosystem. The development of unique shallow-water sediment at the deep-water core site indicated that lake levels dropped significantly during a “megadrought” in the early Holocene.

“About 8,400 years ago, the lake almost dried out,” said Pompeani. “We documented the timing of this drought and studied its transition to conditions more typical of what we observed in the late Holocene.”

Pitt’s study, says Pompeani, contributes to the long-term perspective on natural climate variability that is needed to understand historically unprecedented changes now occurring in the Arctic. Rapid changes in the Arctic climate system that occurred in the relatively recent past can be compared with climate models to improve the understanding of the processes responsible for such nonlinear changes.

Funding for this project was provided by the National Science Foundation.

The Holocene climate project focuses on climate records from the last 8,000 years, including two focus regions: eastern Beringia and the northwest Atlantic. For more information on the Holocene climate project, visit

[Source: University of Pittsburgh press release]

The Strauss Center Uses Esri Technology to Better Target Aid

Mapping Tool Provides Insight into How Climate Change Is Affecting the Security of African Nations

The Strauss Center’s Climate Change and African Political Stability (CCAPS) program has implemented Esri technology to view how climate change impacts vulnerable populations in Africa. CCAPS created the dynamic mapping tool in partnership with AidData for use by researchers, policy makers, journalists, and citizens. Users can visualize any combination of CCAPS data on climate change, conflict, and aid on a map to discover how different forces overlap or intersect.

Active Aid Projects in Malawi

Active Aid Projects in Malawi

“This mapping tool allows policy makers to analyze data from multiple sources at once, providing integrated analysis of the drivers and responses related to security risks stemming from climate change,” said Francis J. Gavin, director of the Strauss Center.

The tool is already being used in the country of Malawi for a solution that tracks and reports on the country’s external funding. Aid information is mapped along with data on climate change vulnerability and incidents of conflict. This sheds light on whether aid is effectively targeting regions where climate change or conflict poses the most significant risk to the sustainable development and political stability of the country.

“Climate change poses an enormous threat to the livelihoods of millions of Africans,” said Jean-Louis Sarbib, CEO of Development Gateway. “The level of risk, however, is not evenly spread and certainly doesn’t respect national boundaries. To ask critical questions about how development assistance can reduce vulnerability, you need hyperlocal data on climate and also on aid-funded interventions. This is what the new CCAPS mapping tool shows in a digestible, interactive way.”

By integrating CCAPS research on climate change, along with existing datasets such as topographic maps, imagery, and thematic information on conflicts, the CCAPS mapping tool aims to provide the most comprehensive view possible of climate change and security in Africa.

“The great work of these organizations is a real game changer for the development community,” said Jack Dangermond, president of Esri. “Being able to create a tool that allows people to communicate with others all over the world using maps is powerful. I am impressed with the work being done and excited to see what they will think of next.”

CCAPS and AidData will continue to release upgrades to the mapping tool throughout 2012. The current mapping tool is available to use now at For more information on AidData, go to Learn more about GIS solutions for climate change from Esri at

[Source: Esri press release]

Species’ Geographic Distributions through Time: Playing Catch-up with Changing Climates

Evolution: Education and OutreachEvolution: Education and Outreach, Published Online 02 March 2012

A. Townsend Peterson and Bruce S. Lieberman

“Species’ ranges are often treated as a fixed characteristic, rather than a fluid, ever-changing manifestation of their ecological requirements and dispersal abilities. Paleontologists generally have had a better appreciation of the changeable nature of species’ ranges than neontologists, but each perspective can improve by appreciating the other. Here, we provide an overview of paleontological and neontological perspectives on species’ geographic distributions, focusing on what can be learned about historical variations in distributions. In particular, we focus on enriching the field of phylogeography with a more explicit view of geography, taking into account variation through time in the geographic distribution of different environments, effectively integrating information from the fossil record, molecular genetics, and paleoclimatology. The cross-disciplinary view that would result offers novel perspectives on biogeography and macroevolution.”

Knowledge-based Classification of Remote Sensing Data for the Estimation of Below- and Above-ground Organic Carbon Stocks in Riparian Forests

Wetlands Ecology and ManagementWetlands Ecology and Management, Published Online 02 March 2012

L. Suchenwirth, M. Förster, A. Cierjacks, F. Lang, and B. Kleinschmit

“Floodplain forests play a crucial role in the storage of organic carbon (Corg). However, modeling of carbon stocks in these dynamic ecosystems remains inherently difficult. Here, we present the spatial estimation of Corg stocks in riparian woody vegetation and soils (to a depth of 1 m) in a Central European floodplain using very high spatial resolution remote sensing data and auxiliary geodata. The research area is the Danube Floodplain National Park in Austria, one of the last remaining wetlands with near-natural vegetation in Central Europe. Different vegetation types within the floodplain show distinct capacities to store Corg. We used remote sensing to distinguish the following vegetation types: meadow, reed bed and hardwood, softwood, and cottonwood forests. Spectral and knowledge-based classification was performed with object-based image analysis. Additional knowledge rules included distances to the river, object area, and slope information. Five different classification schemes based on spectral values and additional knowledge rules were compared and validated. Validation data for the classification accuracy were derived from forest inventories and topographical maps. Overall accuracy for vegetation types was higher for a combination of spectral- and knowledge-based classification than for spectral values alone. While water, reed beds and meadows were clearly detectable, it remained challenging to distinguish the different forest types. The total carbon storage of soils and vegetation was quantified using a Monte Carlo simulation for all classified vegetation types, and the spatial distribution was mapped. The average storage of the study site is 428.9 Mg C ha−1. Despite certain difficulties in vegetation classification this method allows an indirect estimation of Corg stocks in Central European floodplains.”

Niche Models Tell Half the Story: Spatial Context and Life-history Traits Influence Species Responses to Global Change

Journal of BiogeographyJournal of Biogeography, published online 01 March 2012

Rebecca M. Swab, Helen M. Regan, David A. Keith, Tracey J. Regan and Mark K. J. Ooi

“Aim: While niche models are typically used to assess the vulnerability of species to climate change, they have been criticized for their limited assessment of threats other than climate change. We attempt to evaluate this limitation by combining niche models with life-history models to investigate the relative influence of climate change and a range of fire regimes on the viability of a long-lived plant population. Specifically, we investigate whether range shift due to climate change is a greater threat to an obligate seeding fire-prone shrub than altered fire frequency and how these two threatening processes might interact.

“Location: Australian sclerophyll woodland and heathland.

“Methods: The study species is Leucopogon setiger, an obligate seeding fire-prone shrub. A spatially explicit stochastic matrix model was constructed for this species and linked with a dynamic niche model and fire risk functions representing a suite of average fire return intervals. We compared scenarios with a variety of hypothetical patches, a patch framework based upon current habitat suitability and one with dynamic habitat suitability based on climate change scenarios A1FI and A2.

“Results: Leucopogon setiger was found to be sensitive to fire frequency, with shorter intervals reducing expected minimum abundances (EMAs). Spatial decoupling of fires across the landscape reduced the vulnerability of the species to shortened fire frequencies. Shifting habitat, while reducing EMAs, was less of a threat to the species than frequent fire.

“Main conclusions: Altered fire regime, in particular more frequent fires relative to the historical regime, was predicted to be a strong threat to this species, which may reflect a vulnerability of obligate seeders in general. Range shifts induced by climate change were a secondary threat when habitat reductions were predicted. Incorporating life-history traits into habitat suitability models by linking species distribution models with population models allowed for the population-level evaluation of multiple stressors that affect population dynamics and habitat, ultimately providing a greater understanding of the impacts of global change than would be gained by niche models alone. Further investigations of this type could elucidate how particular bioecological factors can affect certain types of species under global change.”

A GIS Model Predicting Potential Distributions of a Lineage: A Test Case on Hermit Spiders (Nephilidae: Nephilengys)

PLoS ONE: Research Article, published 06 Jan 2012

Magdalena Năpăruş and Matjaž Kuntner

“Background: Although numerous studies model species distributions, these models are almost exclusively on single species, while studies of evolutionary lineages are preferred as they by definition study closely related species with shared history and ecology. Hermit spiders, genus Nephilengys, represent an ecologically important but relatively species-poor lineage with a globally allopatric distribution. Here, we model Nephilengys global habitat suitability based on known localities and four ecological parameters.

Predicted habitat suitability for Nephilengys cruentata within its directional distribution area

Predicted habitat suitability for Nephilengys cruentata within its directional distribution area

“Methodology/Principal Findings: We geo-referenced 751 localities for the four most studied Nephilengys species: N. cruentata (Africa, New World), N. livida (Madagascar), N. malabarensis (S-SE Asia), and N. papuana (Australasia). For each locality we overlaid four ecological parameters: elevation, annual mean temperature, annual mean precipitation, and land cover. We used linear backward regression within ArcGIS to select two best fit parameters per species model, and ModelBuilder to map areas of high, moderate and low habitat suitability for each species within its directional distribution. For Nephilengys cruentata suitable habitats are mid elevation tropics within Africa (natural range), a large part of Brazil and the Guianas (area of synanthropic spread), and even North Africa, Mediterranean, and Arabia. Nephilengys livida is confined to its known range with suitable habitats being mid-elevation natural and cultivated lands. Nephilengys malabarensis, however, ranges across the Equator throughout Asia where the model predicts many areas of high ecological suitability in the wet tropics. Its directional distribution suggests the species may potentially spread eastwards to New Guinea where the suitable areas of N. malabarensis largely surpass those of the native N. papuana, a species that prefers dry forests of Australian (sub)tropics.

“Conclusions: Our model is a customizable GIS tool intended to predict current and future potential distributions of globally distributed terrestrial lineages. Its predictive potential may be tested in foreseeing species distribution shifts due to habitat destruction and global climate change.”

Spatial-Epidemiological Modelling in Megacities: Statistical and Spatial Analysis for Urban Health under a Changing Climate

University of Bielefeld, School of Public Health03-07 September 2012

University of Bielefeld, School of Public Health, Department of Public Health Medicine

Global climate change and urban health are of global concern because the majority of the world’s population live in urban areas. Health problems are particularly prevalent in the rapidly urbanising megacities of developing countries, where a growing number of residents live in slums. Moreover, urban health in developing countries is increasingly affected by the diverse effects of global climate change, e.g. through droughts, inundations and an increased burden of infectious and non-infectious diseases and injuries. Research on the complex of health and the environment in urban areas of developing countries is urgently needed. Although transdisciplinary research in this scientific area is gaining importance, capacity building measures are still rare.

We offer a one-week seminar that focuses on statistical analysis and spatial-epidemiological modelling in the context of urban health in megacities of developing countries. Seminar topics are concentrating on health and the urban environment under a changing climate, including megacity development, burden of disease, and socio-ecological health determinants. The aim is to combine theoretical and lab work on statistical analysis and spatial-epidemiological modelling techniques in a transdisciplinary approach. We expect 30 participants with at least half of them being from developing countries.

Expected outcomes
Our participants will get a deeper understanding of the spatial and epidemiological dimensions of urban health under the view of climate change. Participants will be able to understand the multiple dimensions of health problems in megacities of developing countries and to apply statistical techniques which are commonly used in health sciences and in geography. They will further be able to work more effectively in collaboration with other disciplines for understanding and investigating multidisciplinary problems. These measures will enable them to develop sustainable strategies for the improvement of living conditions in megacities of developing countries.