Michael Schultz Rasmussen

Mapping fractional forest cover across the highlands of mainland Southeast Asia using MODIS data and regression tree modelling

Data from the moderate‐resolution imaging spectroradiometer (MODIS) sensor, in combination with new mapping techniques, has the potential to improve regional research on tropical forest resources and land use dynamics. In this study, a supervised regression tree model was used to map fractions of (1) mature forest, (2) secondary forest, and (3) non‐forest, using multi‐temporal MODIS 250‐m data as explanatory variables, and land cover information derived from high‐spatial resolution image data as the response variables. From independent validation data, the overall mean absolute deviation of the resulting maps are estimated at 14.6% for mature forest, 21.6% for secondary forest, and 17.1% for non‐forest cover. This study shows the increased potential of this new mapping technique to infer human imprints on forest cover across the highlands of mainland Southeast Asia, compared to other existing map sources.

Using remote sensing to assess the protective role of coastal woody vegetation against tsunami waves

This paper describes how remote sensing techniques were used to study the effect of mangroves and other woody coastal vegetation as a protective measure against the 2004 Indian Ocean Tsunami. Remote sensing made it possible to compare pre‐ and post‐Tsunami images of large areas. A study site was selected based on medium resolution Landsat imagery and existing topographic maps. Selection criteria included substantial damages reported, presence of woody vegetated and non‐vegetated shorelines, homogeneous bathymetry and good coverage of pre‐ and post‐Tsunami satellite imagery. The Pichawaram mangrove, Tamil Nadu, India, matched these criteria. Pre‐ and post‐Tsunami Ikonos and QuickBird images were compared through the visual interpretation of pre‐Tsunami coastal vegetation and post‐Tsunami damage. The results were validated in the field. The analysis showed that mangrove forests and coastal shelterbelts provided protection from the Tsunami. This was concluded from analysing the spatial distribution of damage relative to woody vegetation along the coast as well as transects detailing the amount of damage behind the coastline and the coastal woody vegetation.

Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: application to Denmark

Evapotranspiration rates in Denmark were estimated using Advanced Very High Resolution Radiometer (AVHRR) satellite data and weather conditions predicted by a high-resolution weather forecast model (HIRLAM). The predictions were used both for atmospheric correction of satellite data and for remote sensing based calculation of net radiation, sensible heat fluxes and evapotranspiration rates. Climate predictions at 12 GMT were used as proxies for the atmospheric conditions at the time of the afternoon satellite passage (12.30–14.30 GMT). The air temperature at the time of the satellite passage was retrieved with a rms error of 1.9°C, and the rms error of the retrieved air humidity was 204 Pa. The evapotranspiration results were significantly influenced by the spatial distribution of weather conditions. Due to the encirclement of Denmark by sea shorelines, sea breezes extending more than 30u2009km inland were responsible for the intrusion of cooler air temperatures which increased the sensible heat fluxes and suppressed the evapotranspiration rates. The predictions were linearly related to eddy-covariance flux measurements representing agricultural land, beech forest and conifer forest, but the relationships were also characterized by a large degree of scattering. The results are discussed in relation to inaccuracies and future perspectives.

Automatic detection of archaeological sites using a hybrid process of Remote Sensing, Gis techniques and a shape detection algorithm

A method is presented for the automatic identification of lost or undiscovered archaeological sites in Egypt by using shape detection techniques on satellite imagery superposed in a GIS environment. For an area of interest, the EO data available from various satellites is pre-processed and from historical plans a shape file of the archaeological structure of interest is produced. A shape detection algorithm employing a shape matched operator is applied to the EO image to produce a detection image identifying the most probable location of the archaeological structure of interest. The shape-matched operator employed is the derivative of double exponential (DODE) operator. The final product is a GIS data set assembled as a list of required features and layers, all converted and processed in the same Geographical Reference System.

Earth observation of vegetation status in a semi-arid environment: comparison of TERRA MODIS and NOAA AVHRR satellite data

MODIS data are compared to NOAA AVHRR for derivation of bio-physical parameters in the semi-arid Senegal, West Africa. The dynamic range of the two MODIS Vegetation Indices CVI and EVI is generally much larger than for the corresponding NOAA AVHRR NDVI data. Correlation analyses show that the two MODIS indices correlate better than it is the case for MODIS CVI/NOAA AVHRR NDVI. The MODIS CVI correlates better to the MODIS fAPAR than the MODIS EVI/MODIS fAPAP. This indicates that MODIS CVI is related to the bio-physical parameter fAPAR while MODIS EVI relates to LAI which is important when modelling the Net primary production.

Satellite eye for the galathea 3 ship expedition: global tour 2006-2007

Satellite Eye for Galathea 3 (www.satelliteeye.dk) contains education at the internet for secondary and upper secondary schools and the public. The Galathea 3 ship expedition circumnavigated the globe starting from Denmark 11 August 2006, visiting Greenland, Azores, South Africa, Australia, Solomon Islands, New Zealand, Antarctica, Chile, Galapagos, the Caribbean, the Northeastern USA and finishing in Denmark 25 April 2007. During the entire expedition satellite images were ordered along the ship track, downloaded, processed, archived and used for education. The satellite images are displayed in Google Earth along with 10 minute observations of air and sea parameters observed at the ship (http://galathea.oersted.dtu.dk). This material forms basis for 9 running projects along the entire route and 24 site-specific cases. Observations from several science projects onboard will be used in the site-specific cases. One of the continuous study cases is chlorophyll observed from Envisat MERIS. As an example: in the upwelling zone near Namibia a very high level of chlorophyll was observed from MERIS. The ship route consequently was changed slightly the day after in order to traverse the area with the highest amount of chlorophyll. Chlorophyll observed onboard (in-situ) two days later show high amounts. In addition, in the science project on the carbon cycle a significant emission of CO2 was observed. The students can use the data online in the classroom. Students from several classes were onboard for part of the expedition and these classes in particular used the Satellite Eye teaching material. In Google Earth satellite images of many themes are shown. These include sea ice, sea surface temperature, ocean wind, wave height, sea surface level, ozone, clouds and radar images of ocean and land. Also high spatial resolution land cover mapping of the visited harbors and their surroundings are included. The case studies uses the image processing software LEO Works developed through the ESA project EDUSPACE, the European Earth Observation web site for Secondary Schools (http://www.eduspace.esa.int). For each theme a thorough educational material has been developed in Danish and English. ESA has granted a large amount of Envisat as well as PROBA images and third mission data from SPOT and Landsat. Also data from NOAA, NASA, JAXA and QUICKBIRD were used.

Survey and Assessment of Advanced Feature Extraction Techniques and Tools for EO Applications

In addition to the substantial amounts of available Earth Observation (EO) data, there is currently an increasing trend towards the acquisition of larger and larger EO data and image quantities from single satellites or missions, with multiple, higher resolution sensors and with more frequent revisiting. More sophisticated algorithms and techniques than those largely in use today are required to exploit this rapidly growing wealth of data and images to a fuller extent. The project “Survey and Assessment of Advanced Feature Extraction Techniques and Tools for EO Applications” (SURF) funded by the European Space Agency (ESA) will address these issues. The objective of SURF is to provide an overview of the current state-of-the-art Methods within feature extraction and manipulation for EO applications and to identify scenarios and related architectures for exploitation of the most promising EO feature extraction Methods. The task is to identify the most promising Methods to extract pertinent information from EO data on environment, natural resources and security issues. SURF aims at listing existing Methods with the final goal of identifying the three most promising Methods to be implemented in prototype solutions. The work includes the development of the concept for the evaluation and rating of Methods relative to the users needs for information, the maturity and novelty of the Methods, the potential for fusing data and the operational feasibility. Special emphasis will be made regarding the exploitation of state-of-the art image processing, pattern recognition and classification techniques.

Survey and assessment of new trends in image processing for Earth observation

As more and more Earth Observation (EO) data becomes available, the need to automate at least some aspects of data processing is apparent. The SURF project was funded by the European Space Agency (ESA) to provide a survey of image-processing methods for EO and an in-depth analysis and prototyping of some of the most promising methods. The survey has included (1) a list of application areas within EO; (2) the development of criteria for the evaluation of methods; (3) a classification of image processing tasks within EO, independent of the applications; (4) single-page descriptions of a wide range of methods. Based on this background work, a dozen methods were selected for further analysis and considered for prototyping. The next stage of the project consists in prototyping four of the methods subjected to in-depth analysis. This paper presents the results of the survey and a brief review of the methods selected for prototyping.