Morgan De Dapper

Bio: Morgan De Dapper is an academic researcher from Ghent University. The author has contributed to research in topics: Geoarchaeology & Soil salinity. The author has an hindex of 12, co-authored 64 publications receiving 839 citations.

A century of tree line changes in sub-Arctic Sweden shows local and regional variability and only a minor influence of 20th century climate warming

Abstract: Aim Models project that climate warming will cause the tree line to move to higher elevations in alpine areas and more northerly latitudes in Arctic environments. We aimed to document changes or stability of the tree line in a sub-Arctic model area at different temporal and spatial scales, and particularly to clarify the ambiguity that currently exists about tree line dynamics and their causes. Location The study was conducted in the Tornetrask area in northern Sweden where climate warmed by 2.5 degrees C between 1913 and 2006. Mountain birch (Betula pubescens ssp. czerepanovii) sets the alpine tree line. Methods We used repeat photography, dendrochronological analysis, field observations along elevational transects and historical documents to study tree line dynamics. Results Since 1912, only four out of eight tree line sites had advanced: on average the tree line had shifted 24 m upslope (+0.2 m year-1 assuming linear shifts). Maximum tree line advance was +145 m (+1.5 m year-1 in elevation and +2.7 m year-1 in actual distance), whereas maximum retreat was 120 m downslope. Counter-intuitively, tree line advance was most pronounced during the cooler late 1960s and 1970s. Tree establishment and tree line advance were significantly correlated with periods of low reindeer (Rangifer tarandus) population numbers. A decreased anthropozoogenic impact since the early 20th century was found to be the main factor shaping the current tree line ecotone and its dynamics. In addition, episodic disturbances by moth outbreaks and geomorphological processes resulted in descent and long-term stability of the tree line position, respectively. Main conclusions In contrast to what is generally stated in the literature, this study shows that in a period of climate warming, disturbance may not only determine when tree line advance will occur but if tree line advance will occur at all. In the case of non-climatic climax tree lines, such as those in our study area, both climate-driven model projections of future tree line positions and the use of the tree line position for bioclimatic monitoring should be used with caution.

Object-oriented change detection for the city of Harare, Zimbabwe

Abstract: Object building and the extraction of homogeneous landscape units on which spatial statistics can be applied is useful in assessing land use and land cover change. Object-oriented processing techniques are becoming more popular compared to traditional pixel-based image analysis. A hierarchical image segmentation approach was adopted to extract the objects from multi-temporal Landsat images over Zimbabwe. The spatial arrangement of t"0 and t"1 objects was independent as the segmentation process was independently applied, although object change of t"1 was based on t"0 boundaries. We applied a Standardized, Object Oriented, Automatic Classification (SOOAC) method based on fuzzy logic. The error matrix for the TM image had an overall accuracy of 95.6% and a KIA value of 94.7%, the ETM showed slightly lower overall accuracy. Various LULC changes were identified over the 13year period per object and also per class, mainly vegetation decrease. Object-oriented change information is necessary in decision support systems and uncertainty management strategies. This approach addresses some of the major issues in object-based GIS change analysis as it is based on stable object geometry.

An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization

Abstract: Numerous segmentation algorithms have been developed, many of them highly specific and only applicable to a reduced class of problems and image data. Without an additional source of knowledge, automatic image segmentation based on low level image features seemed unlikely to succeed in extracting semantic objects in generic images. A new region-merging segmentation technique has recently been developed which incorporates the spectral and textural properties of the objects to be detected and also their different size and behaviour at different stages of scale, respectively. Linking this technique with the FAO Land Cover Land Use classification system resulted in the development of an automated, standardized classification methodology. Testing on Landsat and Aster images resulted in mutually exclusive classes with clear and unambiguous class definitions. The error matrix based on field samples showed overall accuracy values of 92% for Aster image and 89% for Landsat. The KIA values were 88% for Aster images and 84% for the Landsat image.

First evidence of Pleistocene rock art in North Africa. Securing the age of the Qurta petroglyphs (Egypt) through OSL dating

Abstract: Long doubted, the existence of Pleistocene rock art in North Africa is here proven through the dating of petroglyph panels displaying aurochs and other animals at Qurta in the Upper Egyptian Nile Valley. The method used was optically stimulated luminescence (OSL) applied to deposits of wind-blown sediment covering the images. This gave a minimum age of ~15 000 calendar years making the rock engravings at Qurta the oldest so far found in North Africa.

Object based image analysis for remote sensing

Abstract: Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

Abstract: We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper TM and Enhanced Thematic Mapper Plus ETM+ data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T orthorectified. Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index MODIS EVI time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization FAO land-cover classification system as well as the International Geosphere-Biosphere Programme IGBP system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy OCA of 64.9% assessed with our test samples, with RF 59.8%, J4.8 57.9%, and MLC 53.9% ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples 8629 each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

Change detection from remotely sensed images: From pixel-based to object-based approaches

Abstract: The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

The effects of land uses on soil erosion in Spain: A review

Abstract: Soil erosion is a key factor in Mediterranean environments, and is not only closely related to geoecological factors (lithology, topography, and climatology) but also to land-use and plant cover changes. The long history of human activity in Spain explains the development of erosion landscapes and sedimentary structures (recent alluvial plains, alluvial fans, deltas and flat valleys infilled of sediment). For example, the expansion of cereal agriculture and transhumant livestock between the 16th and 19th centuries resulted in episodes of extensive soil erosion. During the 20th century farmland abandonment prevailed in mountain areas, resulting in a reduction of soil erosion due to vegetation recolonization whereas sheet-wash erosion, piping and gullying affected abandoned fields in semi-arid environments. The EU Agrarian Policy and the strengthening of national and international markets encouraged the expansion of almond and olive orchards into marginal lands, including steep, stony hill slopes. Vineyards also expanded to steep slopes, sometimes on new unstable bench terraces, thus leading to increased soil erosion particularly during intense rainstorms. The expansion of irrigated areas, partially on salty and poorly structured soils, resulted in piping development and salinization of effluents and the fluvial network. The trend towards larger fields and farms in both dry farming and irrigated systems has resulted in a relaxation of soil conservation practices.