Assessing, monitoring and mapping forest resources in the Blue Nile region of Sudan using an object-based imageanalysis approach
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Beschreibung
About the book
Following the hierarchical nature of forest resource management, this research focuses on the monitoring and assessment of forest cover at various abstraction levels based upon categorical land use/land cover (LU/LC) classification and change detection as well as empirical estimation of changes at local operational levels. An approach of object-based image analysis (OBIA) based on optical sensor data has been adapted and applied in the destabilized Blue Nile region of Sudan in order to gather the required spatial information in support of future forest planning and decision making. At the categorical level rules have been developed and optimal features have been extracted for each segment. Based on thematic LU/LC maps series of optimised algorithms have been created to depict the dynamics of change of LU/LC entities. Detailed change classes as well as change statistics have been produced. Moreover hot-spot areas have been investigated and aggregated to the community-level. The study utilised a well-designed questionnaire to address the factors affecting LU/LC dynamics and the possible solutions based on the perception of local communities. At the operational structural forest stand level correlation and regression analyses have been applied to identify the relations between a wide range of spectral and textural metrics and the field-derived forest attributes. Furthermore the best fitting models have been cross-validated with an independent set of field samples, which revealed a high degree of precision. The research concludes that OBIA shows a significant capability to serve as an efficient approach to gaining accurate knowledge about land-related features, whether at the level of operational structural forest attributes or at the level of categorical LU/LC classification and change detection. Moreover, the developed methodological framework exhibits a potential solution to attain precise facts and figures about the dynamics of change and their driving forces.
Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to up-date the desired information.
An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporat-ing expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes.
Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA.
Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high.
Aggregated to the community-level, social survey of household data provides a comprehensive perspective addi-tionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception.
At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attrib-ute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used af-fect the quality of the developed model this was also discussed based on the different sensors examined.
To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forces.
Autor*in
Mustafa Mahmoud El-Abbas
Mustafa M. El-Abbas is an assistant professor at the Faculty of Forestry, Department of Forest Management, University of Khartoum, where he obtained his B.Sc. (Hons.) and M.Sc. degrees (2001 and 2006 respectively). Moreover, he has got a diploma of forest ecology and forest resource management from the University of Helsinki, Finland, in 2006. Dr. El-Abbas took his doctoral degree in remote sensing at TU Dresden.
Themen in »Assessing, monitoring and mapping forest resources in the Blue Nile region of Sudan using an object-based imageanalysis approach«
LANDSAT TM RapidEye TERRA ASTER Waldressourcenmanagement forest image analysis land use