SPATIAL ANALYSES AND REMOTE SENSING FOR LAND COVER CHANGE DYNAMICS: ASSESSING IN A SPATIAL PLANNING

ABSTRACT (EN) Spatial planning is a crucial discipline for the identification and implementation of sustainable development strategies that take into account the environmental impacts on the soil. In recent years, the significant development of technology, like remote sensing and GIS software, has significantly increased the understanding of environmental components, highlighting their peculiarities and criticalities. Geographically referenced information on environmental and socio-economic components represents a fundamental database for identifying and monitoring vulnerable areas, also distinguishing different levels of vulnerability. This is even more relevant considering the increasingly significant impact of land transformation processes, consisting of rapid and frequent changes in land use patterns. In order to achieve some of the Sustainable Development Goals of the 2030 Agenda, the role of environmental planning is crucial in addressing spatial problems, such as agricultural land abandonment and land take, which cause negative impacts on ecosystems. Remote sensing, and in general all Earth Observation techniques, play a key role in achieving SDG 11.3 and 15.3 of Agenda 2030. Through a series of applications and investigations in different areas of Basilicata, it has been demonstrated how the extensive use of remote sensing and spatial analysis in a GIS environment provide a substantial contribution to the results of the SDGs, enabling an informed decisionmaking process and enabling monitoring of the results expected, ensuring data reliability and directly contributing to the calculation of SDG objectives and indicators by facilitating local administrations approaches to work in different development and sustainability sectors. In this thesis have been analyse the dynamics of land transformation in terms of land take and soil erosion in sample areas of the Basilicata Region, which represents an interesting case example for the study of land use land cover change (LULCC). The socio-demographic evolutionary trends and the study of marginality and territorial fragility are fundamental aspects in the context of territorial planning, since they are important drivers of the LULCC and territorial transformation processes. In fact, in Basilicata, settlement dynamics over the years have occurred in an uncontrolled and unregulated manner, leading to a constant consumption of land not accompanied by adequate demographic and economic growth. To better understand the evolution and dynamics of the LULCCs and provide useful tools for formulating territorial planning policies and strategies aimed at a sustainable use of the territory, the socio-economic aspects of the Region were investigated. A first phase involved the creation of a database and the study and identification of essential services in the area as a fundamental parameter against which to evaluate the quality of life in a specific area. The supply of essential services can be understood as an assessment of the lack of minimum requirements with reference to the urban functions exercised by each territorial unit. From a territorial point of view, the level of peripherality of the territories with respect to the network of urban centres profoundly influences the quality of life of citizens and the level of social inclusion. In these, the presence of essential services can act as an attractor capable of generating discrete catchment areas. The purpose of this first part of the work was above all to create a dataset of data useful for the calculation of various socio-economic indicators, in order to frame the demographic evolution and the evolution of the stock of public and private services. The first methodological approach was to reconstruct the offer of essential services through the use of open data in a GIS environment and subsequently estimate the peripherality of each municipality by estimating the accessibility to essential services. The study envisaged the use of territorial analysis techniques aimed at describing the distribution of essential services on the regional territory. It is essential to understand the role of demographic dynamics as a driver of urban land use change such as, for example, the increase in demand for artificial surfaces that occurs locally. Social and economic analyses are important in the spatial planning process. Comparison of socio-economic analyses with land use and land cover change can highlight the need to modify existing policies or implement new ones. A particular land use can degrade and thereby destroy other land resources. If the economic analysis shows that the use is beneficial from the point of view of the land user, it is likely to continue, regardless of whether the process is environmentally friendly. It is important to understand and investigate which drivers have been and will be in the future the most decisive in these dynamics that intrinsically contribute to land take, agricultural abandonment and the consequent processes of land degradation and to define policies or thresholds to mitigate and monitor the effects of these processes. Subsequently, the issues of land take and abandonment of agricultural land were analysed by applying models and techniques of remote sensing, GIS and territorial analysis for the identification and monitoring of abandoned agricultural areas and sealed areas. The classic remote sensing methods have also been integrated by some geostatistical analyses which have provided more information on the investigated phenomenon. The aim was the creation of a quick methodology that would allow to describe the monitoring and analysis activities of the development trends of soil consumption and the monitoring and identification of degraded areas. The first methodology proposed allowed the automatic and rapid detection of detailed LULCC and Land Take maps with an overall accuracy of more than 90%, reducing costs and processing times. The identification of abandoned agricultural areas in degradation is among the most complicated LULCC and Land Degradation processes to identify and monitor as it is driven by a multiplicity of anthropic and natural factors. The model used to estimate soil erosion as a degradation phenomenon is the Revised Universal Soil Loss Equation (RUSLE). To identify potentially degraded areas, two factors of the RUSLE have been correlated: Factor C which describes the vegetation cover of the soil and Factor A which represents the amount of potential soil erosion. Through statistical correlation analysis with the RUSLE factors, on the basis of the deviations from the average RUSLE values and mapping of the areas of vegetation degradation, relating to arable land, through statistical correlation with the vegetation factor C, the areas were identified and mapped that are susceptible to soil degradation. The results obtained allowed the creation of a database and a map of the degraded areas to be paid attention to

Assessment on recent landslide susceptibility mapping methods: A review

Landslide is a destructive natural hazard that causes severe property loss and loss of lives. Numerous researchers have developed landslide susceptibility maps in order to forecast its occurrence, particularly in hill-site development. Various quantitative approaches are used in landslide susceptibility map production, which can be classified into three categories; statistical data mining, machine learning and deterministic approach. In this paper, we choose two regular models in each category, which are Weight of Evidence (WoE) and Frequency Ratio (FR), Artificial Neutral Networks (ANN) and Support Vector Machines (SVM), Shallow Landsliding Stability Model (SHALSTAB) and YonSei-Slope (YS-Slope). Discussion and assessment on these models are based on relevant literature

Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables

Forests fires in northern Iran have always been common, but the number of forest fires has been growing over the last decade. It is believed, but not proven, that this growth can be attributed to the increasing temperatures and droughts. In general, the vulnerability to forest fire depends on infrastructural and social factors whereby the latter determine where and to what extent people and their properties are affected. In this paper, a forest fire susceptibility index and a social/infrastructural vulnerability index were developed using a machine learning (ML) method and a geographic information system multi-criteria decision making (GIS-MCDM), respectively. First, a forest fire inventory database was created from an extensive field survey and the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product for 2012 to 2017. A forest fire susceptibility map was generated using 16 environmental variables and a k-fold cross-validation (CV) approach. The infrastructural vulnerability index was derived with emphasis on different types of construction and land use, such as residential, industrial, and recreation areas. This dataset also incorporated social vulnerability indicators, e.g., population, age, gender, and family information. Then, GIS-MCDM was used to assess risk areas considering the forest fire susceptibility and the social/infrastructural vulnerability maps. As a result, most high fire susceptibility areas exhibit minor social/infrastructural vulnerability. The resulting forest fire risk map reveals that 729.61 ha, which is almost 1.14% of the study areas, is categorized in the high forest fire risk class. The methodology is transferable to other regions by localisation of the input data and the social indicators and contributes to forest fire mitigation and prevention planning

Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables

Forests fires in northern Iran have always been common, but the number of forest fires has been growing over the last decade. It is believed, but not proven, that this growth can be attributed to the increasing temperatures and droughts. In general, the vulnerability to forest fire depends on infrastructural and social factors whereby the latter determine where and to what extent people and their properties are affected. In this paper, a forest fire susceptibility index and a social/infrastructural vulnerability index were developed using a machine learning (ML) method and a geographic information system multi-criteria decision making (GIS-MCDM), respectively. First, a forest fire inventory database was created from an extensive field survey and the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product for 2012 to 2017. A forest fire susceptibility map was generated using 16 environmental variables and a k-fold cross-validation (CV) approach. The infrastructural vulnerability index was derived with emphasis on different types of construction and land use, such as residential, industrial, and recreation areas. This dataset also incorporated social vulnerability indicators, e.g., population, age, gender, and family information. Then, GIS-MCDM was used to assess risk areas considering the forest fire susceptibility and the social/infrastructural vulnerability maps. As a result, most high fire susceptibility areas exhibit minor social/infrastructural vulnerability. The resulting forest fire risk map reveals that 729.61 ha, which is almost 1.14% of the study areas, is categorized in the high forest fire risk class. The methodology is transferable to other regions by localisation of the input data and the social indicators and contributes to forest fire mitigation and prevention planning

Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador

Although the Andean region is one of the most landslide-susceptible areas in the world, limited attention has been devoted to the topic in terms of research, risk reduction practice, and urban policy. Based on the collection of early landslide data for the Andean city of Quito, Ecuador, this article aims to explore the predictive power of a binary logistic regression model (LOGIT) to test secondary data and an official multicriteria evaluation model for landslide susceptibility in this urban area. Cell size resampling scenarios were explored as a parameter, as the inclusion of new “urban” factors. Furthermore, two types of sensitivity analysis (SA), univariate and Monte Carlo methods, were applied to improve the calibration of the LOGIT model. A Kolmogorov–Smirnov (K-S) test was included to measure the classification power of the models. Charts of the three SA methods helped to visualize the sensitivity of factors in the models. The Area Under the Curve (AUC) was a common metric for validation in this research. Among the ten factors included in the model to help explain landslide susceptibility in the context of Quito, results showed that population and street/road density, as novel “urban factors”, have relevant predicting power for high landslide susceptibility in urban areas when adopting data standardization based on weights assigned by experts. The LOGIT was validated with an AUC of 0.79. Sensitivity analyses suggested that calibrations of the best-performance reference model would improve its AUC by up to 0.53%. Further experimentation regarding other methods of data pre-processing and a finer level of disaggregation of input data are suggested. In terms of policy design, the LOGIT model coefficient values suggest the need for deep analysis of the impacts of urban features, such as population, road density, building footprint, and floor area, at a household scale, on the generation of landslide susceptibility in Andean cities such as Quito. This would help improve the zoning for landslide risk reduction, considering the safety, social and economic impacts that this practice may produce.Land Use Planning and Management for Landslide Risk Reduction - Conditions and Potentials in Andean Citie

A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy

Landslide Susceptibility Mapping Using Artificial Neural Network in the Urban Area of Senise and San Costantino Albanese (Basilicata, Southern Italy)

Landslides are significant natural hazards in many areas of the world. Mapping the areas that are susceptible to landslides is essential for a wise territorial approach and should become a standard tool to support land-use management. A landslide susceptibility map indicates landslide-prone areas by considering the predisposing factors of slope failures in the past. In the presented work, we evaluate the landslide susceptibility of the urban area of Senise and San Costantino Albanese towns (Basilicata, southern Italy) using an Artificial Neural Network (ANN). In order, this method has required the definition of appropriate thematic layers, which parameterize the area under study. To evaluate and validate landslide susceptibility, the landslides have been randomly divided into two groups, each representing the 50% of the total area subject to instability. The results of this research show that most of the investigated area is characterized by a high landslide hazard