Rabia Hammad, Ahmed Mohamed Harb (2013) SOIL SEALING AND LAND USE CHANGE DETECTION APPLYING GEOGRAPHIC OBJECT BASED IMAGE ANALYSIS (GEOBIA) TECHNIQUE. [Tesi di dottorato]

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Abstract

Land use and land cover change analysis is now a mature area of study but it is still important to monitor these changes and their subsequent impacts on ecosystem functions. The rate of Land use and land cover change is much larger than ever recorded previously, with quick changes to ecosystems taking place at local to global scales. The functions of an ecosystem can be significantly impacted by changes in land use and land cover, which in turn critically affect the provision, regulation and supporting services of the ecosystem. Therefore, land use land cover change interventions and strategic planning can contribute to the health and sustainability of an ecosystem and its land use in the future. In order to make appropriate land cover and land use decisions, accurate assessments of change are needed. The health and sustainability of an ecosystem is critically connected to land use and land covers interventions and Strategic planning. Precise estimations of Land use and land cover change are needed in order to identify crucial zones of environmental vulnerability or those which provide valuable ecosystem services. Given that land change detection is greatly dependent on the accuracy of the historical input data, improving historical data accuracy is likely to improve the final land change detection result. In an ecosystem, there is need to establish the quantity and quality of resources and their suitability for a certain range of land uses in order to assure its future productivity and sustainability of biodiversity. Land suitability evaluation is an important process for assessing the value and proficiency of the land and helps in planning for future sustainability of land resources. Accurate assessment methods give better results and consequently facilitate establishment of improved management plans. Soil presents a large number of functions that are essential for human life. In addition to providing biomass, food and raw materials, soil performs also various services such as being a habitat host and a gene pool. The soil also has the functions of processing, filtering and storage in addition to cultural and social functions. Therefore, the soil plays a key role in regulating natural and socio-economic processes that are necessary for human survival, as the water cycle and climate system. One of the most critical threats to the soils and, in general, the ecosystem, is soil sealing. Soil sealing is the result of new roads, buildings and parking places but also other private and public space, and it involves covering of the soil surface with impermeable materials such as stone and concrete. Urbanization and soil sealing are still growing rapidly, even more than the population growth rate in some cases. Due to this hasty soil sealing process, more fertile soils are being sealed and getting out of the agriculture and food production systems. That is why the soil scientific community as well as the environmental scientists should give more attention to soil losses and try to face this problem. Based on the foregoing, a study was conducted to evaluation the losses in soil functions due to soil sealing actions. The work was divided into three major objectives. The first objective was to perform long term detection for land use and land cover change for the period from 1954 to 2009 in order to understand the history, rates and trends of the soil sealing in the study area. Then, the second objective was to develop a novel method for automatic LULC classification of the 1954 aerial photographs using geographic object based image analysis (GEOBIA) technique. The reason behind this objective is the assumption that improving the quality of the classification for old land use and land cover maps will improve the final results of the change detection analysis. Consequently, the quantification of the lost biomass production by soil sealing will be improved. Finally, the third objective, was to carry out a modeling of soil function loss by soil sealing to quantify the losses in one of the soil functions i.e., biomass production. The study area was chosen in Telesina Valley (Valle Telesina), located in Benevento in the Campania region of central Italy. To fulfill the first objective, four maps of land use and land cover (LULC) were obtained for Telesina Valley from the years 1954, 1990, 2000 and 2009. Land use and land cover change analysis was performed using the four maps and finally three change maps were created. Land use changes were defined and classified as the changes in a land use class that occurred in a given area and time. These classes identify the typology of changes by assigning a land use change code to each intersection created by the overlay of successive land use maps, allowing a thematic representation of the spatial distribution of changes. The results showed that, in the first time period from 1954 to 1990, only thirteen change types have been found while the change types stabilization and degradation didn’t appear in this interval. In contrast, in the second time period from 1990 to 2000, only nine change types appeared in the study area while six change types were absent. The missing change types in this time period are stabilization, degradation, exceptionality, agriculture intensification, abandonment and agriculture extensive conversion. This shows that during the time period from 1990 to 2000 there was only slight LULC change as urban intensification whilst the rest of the study area were represented by persistence change types such as agriculture persistence, forest persistence, persistence and urban persistence. This is likely to be related to the fact that the compared LULC maps in this case are the corine LULC maps for the years 1990 and 2000 which have exactly the same legends, unlike the other maps which have different legends. Finally, in the third time period from 2000 to 2009, all fifteen change types were present with large afforestation and deforestation activities. The study focused on three important land changes types, deforestation, agriculture development and urbanization. The results demonstrated that the forest area has increased in the last fifty years although that the deforestation process was greater than afforestation in the last thirty years. On the contrary, Agriculture area has decreased greatly in the same period. The total agricultural area reduced by 6% during the first period (1954-1990), 1% during the second period (1990-2000) and 3.5% during the third period (2000-2009). Approximately 1200 hectares of agricultural land have been lost during the period from 1954 to 2009.On the other hand; urbanization had a progressive trend during the last five decades. The urban area increased more than four times during this time period (1954-2009). It can be concluded that urbanization in the study area is an ongoing problem that requires active management strategies for controlling the quantity and the direction of the sprawl in the future. These data revealed one of the problems occurring in the region, i.e. soil sealing and soil loss due to urbanization. Regarding the second objective of the study, using the object-oriented eCognition software, the LULC of the study area for the year 1954 was reclassified using aerial photographs and GEOBIA technique. It was possible to extract land cover data from the aerial photographs using different features such as the tone, brightness, border contrast, roundness and many other features available in the software. Then, the idea was to compare the original 1954 map with the reclassified LULC map to determine whether the reclassified map will improve land change estimates. The results showed that, visually overlapping the different filters on the original gray scale image in a Red-Green-Blue composition (RGB) augmented the vision quality of the images and consequently the capabilities of classifiers. The multi-resolution segmentation algorithm was selected as the main segmentation algorithm through the entire classification process. Regarding the scale parameter, a scale of 90 has been chosen as the optimal scale for all the segmentation processes except for the urbanization class, which was 60. Similarly, the weight assigned to the shape criterion was 0.7 in all the segmentation processes except for the urbanization class, which was assigned weight of 0.2. On the other hand, for the compactness criterion, the suitable weight was 0.3 in most of the classes except for the classes Olives, Vineyards, Mixed Olives-Vineyards and urbanization for which the weight 0.5 was more suitable. Ten land use and land cover classes were recognized during the classification progression which are urban, water bodies, tree lines, woodland, pasture, bare soil, agriculture fields, olives, vineyards and mixed vine-olives. Different features and values were used for the recognition of classes during the classification process. To generate the final LULC map, the classified tiles were exported to a GIS environment in a polygon shapfile form and then went through mosaicing process to form one polygon layer with all classes. The calculated overall map accuracy is 77% with a kappa value of 0.73 which are both within ranges of fair accuracy. The producer’s accuracy states how well the map producer recognized a land cover type on the map from the remote sensing imagery data. Results show that the highest producer’s accuracy was for pasture class (94%) while the lowest was for the vineyards class (44%). Comparing the old and the improved (GEOBIA) maps of LULC shows that, regarding the agricultural area, 50% of the detected change using the original data was misclassified compared with the improved classification of aerial photographs. The results revealed that the urban area was underestimated in the old LULC map of 1954. This leads to an important finding of this research that modeling results are greatly correlated to the historical input data accuracy, and that changing or improving the data accuracy will improve the final modeling results. The reason behind attempting to improve the old classification of LULC using GEOBIA technique on aerial photographs was to enhance the land change detection, which consequentially will help to advance studying the land change impacts on the environment and this is rather important also for evaluating the effect of soil sealing on soils. Finally, regarding the third study objective, a novel methodology was proposed consists of different sequential stages starting with data collection arriving to a quantification of the biomass production loss by soil sealing. The methodology is mainly based on conducting a land suitability evaluation for wheat production. Then, using the wheat production statistic averages from statistical reports, it is possible to assign a production rate for different suitability classes and generate a land productivity map for wheat. Wheat crop is used here as a standard international land productivity measure and a reference crop for land productivity evaluation. However, any other crop is suitable for the proposed method. Next, using the generated land productivity map along with soil sealing map (i.e. map of urbanization in the study area) it is possible to quantify the lost biomass production due to soil sealing. A new parametric concept “equation” of land suitability evaluation was proposed to improve results of land suitability evaluation. Land suitability assessment for wheat production was conducted in order to compare results of the suggest method with classical parametric methods. Organic matter, CaCO3, pH, Slope, texture, drainage, depth, EC and altitude were recognized as factors affecting land suitability for wheat production in the study area. Comparing results of the three parametric methods (Storie, Square root, Rabia) used showed that the proposed equation gave higher suitability index values than classical methods. Great correlation has been found between results of the three methods. Organic matter, topology and pH were found to be the limiting factors for wheat production in the study area. Generally, the proposed equation may improve land suitability assessment process and gives better realistic results. Results showed that in all the land units in the study area, land suitability index was higher in case of Rabia method. However, correlation analysis exposed a high correlation between all the three methods. This can be explained that, the value of final suitability index of the equation was based principally on the factor that has the maximum influence on land use suitability with regard to the other factors. So, in Rabia equation, the value of suitability index in addition to the suitability class is likely to be more representative of the real situation, which makes this equation superior to the Storie and Square root equations. Regarding the land suitability classes of Storie method, only few land units were suitable for wheat production (i.e. approximately 11% of the study area). In case of square root method, more land units (i.e. approximately 36% of the study area) were evaluated as suitable for wheat production. However, in case of Rabia method, the total area of suitable land units for wheat production was approximately 53% of the study area. It is clear from the results that biomass production losses increased significantly and progressively over time. The biomass production loss increased more than 7 folds from approximately 790 metric tons in 1954 up to 5797 metric tons in 2011. Regarding the total area sealed by urbanization activities, the area of soil sealing also increased more than 7 folds from 1954 to 2011. This gives an indication about the high correlation (0.99) between the soil sealing inverts and the biomass production decrease with almost equal rate.

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