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Fires represents one of the main causes of environmental degradation and have an important negative impact on the landscape. Fires, in fact, strongly influenced ecological processes and compromise the ecosystems. Measurements of the post-fire damage levels over burned areas are important to quantify fire's impact on landscapes. Remote sensing and geo-statistical approaches are useful tools for the monitoring and analysis of burned areas on a regional scale, because provides reliable and rapid diagnosis of burned areas. Spatial autocorrelation statistics, such as Moran's I and Getis-Ord Local Gi index, were also used to measure and analyze dependency degree among spectral features of burned areas. This approach improves characterization of a burnt area and improves the estimate of the severity of the fire. This paper provides an application of fire severity studies describing post-fire spectral responses of fire affected vegetation to obtain a burned area map. The aim of this work is to implement a procedure, using ESA Sentinel 2 data and spatial autocorrelation statistics in a GIS open-source environment, a graphical model that analyzes the change detection of the potential burned area, as case of study Northern part of Apulia Region (Italy) was used. The burned area was delineated using the spectral indices calculated using Sentinel two images in the period July-August 2020 and using also the land use map of the area.

Soil degradation is a phenomenon that describes the degradation of soil quality due to which agricultural land in particular is unproductive as a consequence of the loss of ability to produce crops and biomass. The causes are many but, especially in the inland areas of the Mediterranean regions, some dynamics related to agriculture have particularly influenced the grading process. Specifically, agricultural over exploitation with unsustainable practices and land abandonment are causing ecological alterations that require contextual analysis to assess the medium and long-term effects. The aim of this work is to investigate the role of some factors that make up the RUSLE index have in the detection and monitoring of potentially degraded areas. In particular, the areas cultivated with arable crops were chosen as the area to be analyzed, because the average annual rate of soil erosion (A factor in RUSLE equation) is high despite the presence of vegetation cover and shown evident problems due to the phenomenon of degradation. In order to identify the potential degraded areas, two factor of RUSLE index have been correlated: C factor that describes the vegetation cover of the soil and A factor which represent the amount of potential soil erosion. All methodologies have been applied in a rural area in the northern part of Basilicata Region (Italy) using GIS and remote sensing approaches, as allows the possibility to perform a series of a complex studies and can be efficiently implemented in environmental monitoring plans.

European Commission in 2009 assessed that in the period 2015–2030 about 11% of agricultural land in the EU are under high potential risk of abandonment due to factors, which has strong and known environmental and socio-economic consequences. The diverse impacts of abandonment need to be addressed via a broader set of policy instruments to alleviate the negative effects or even - reverse the trends in the early stages of the process. The clear identification of abandoned agricultural land is fundamental for a correct mapping for the future management and monitoring of the territories. In this context, this study proposes an innovative method for the detection and mapping of abandoned arable land through the use of remote sensing techniques and geo-statistical analysis. The combined use of Sentinel 2 images and the Landsat constellation, the use of NDVI index and change detection analysis made it possible to identify the change in agricultural use and/or abandonment of land in the eastern part of the Basilicata region in the period 1990–2020. (Italy). All process has been developed integrating Remote Sensing and Geographic Information System (GIS), using open-source software.

Soil erosion in fired areas is one of the main environmental problem involves degrading the quality of the soil and reducing the productivity of the affected lands. The aim of this work is to implement a procedure that analyzes the change detection of the potential soil eroded in a burned area, and discriminate the amount of potential soil loss. As part of the MESARIP project (in agreement with the Regional Civil Protection) in order to implement the analyses of soil erosion pre and post fire event, using Sentinel 2 data and with the RUSLE (Revised Universal Soil Loss Equation) method in a GIS open source environment, a graphical model has been developed. The application of the RUSLE requires a series of consequential spatial analysis elaborations and, according to this scheme, the model has been developed with the Graphical Modeler. QGIS contains in a single environment a multiplicity of tools and algorithms native to other open source GIS software, such as, for example, SAGA GIS and GRASS GIS. The user interface is very simple and requires basic and thematic input data such as DEM, MASK areas or vegetation indices etc. The advantages in the construction of the model can be identified in the standardization of map algebra operations and also in the speed of execution of the steps. Currently the model has been tested in some burned areas in 2019 located in the northern part of the Apulia Region and will be tested in operational mode during the 2020 summer season.

Soil consumption represent an important indicator of soil management, in last few years the European States have been promoted the use and installation of renewable energy sources, with a consequent soil consumption increase. The aim of this work is to implement a procedure that analyzes the change detection of the soil consumption and discriminate those related to soil consumption due to installation of renewable energy sources from that due to built-up areas. The select test site is the Municipality of Melfi (Southern Italy) because is highly significant because is characterized by fragmented and various environments. The increase of urbanization is due to the growth of built-up areas and the exponential development of renewable sources installation. The work herein presented concerns an application study on these processes with the images of Sentinel-2 satellite. In order to produce a synthetic map of soil consumption, the Sentinel-2 images were classified using a supervised classification. A first map of soil consumption was obtained divided the area characterized by urbanization from the area with the presence of the renewable energy sources. Eolic class have been subdivided and reclassified, divided the relevant street from the turbine pad. Eolic class have been reclassified discriminate the relevant street from the turbine pad and subdivided into other subclasses referred to the power wind turbines, in order to quantify the soil consumption related to each one. All processes have been processes developed integrating Remote Sensing and Geographic Information System (GIS), using open source software.

Remote sensing fire danger prediction model is applied to Northern China. This study was carried out in the Daxing'anling region, which is located in Heilongjiang Province and Inner Mongolia (50.5 degrees-52.25 degrees N, 122 degrees-125.5 degrees E), the northern China. The method integrated by dead fuel moisture content and relative greenness index, which is based on the fire potential index (FPI), was used to predict the fire danger level of the study area. The case that fire happened on the late June 2010 was used to validate the modified method. The results pointed out that the fire affected areas were located in high fire danger level on 26th, 27th, 28th June, 2010 respectively. The ROC analyses of the predicted accuracy on these days were 90.98 %, 73.79 % and 69.07 % respectively. Results from our investigation pointed out the reliability of the adopted method.

Up to nowadays, satellite data have become increasingly available, thus offering a low cost or even free of charge unique tool, with a great potential for operational monitoring of vegetation cover, quantitative assessment of urban expansion and urban sprawl, as well as for monitoring of land use changes and soil consumption. This growing observational capacity has also highlighted the need for research efforts aimed at exploring the potential offered by data processing methods and algorithms, in order to exploit as much as possible this invaluable space-based data source. The work herein presented concerns an application study on the monitoring of vegetation cover with the use of multi-temporal (2010-2014) satellite Modis data. The selected test site is the Iguazu park highly significant, being it one of the most threatened global conservation priorities (http://whc.unesco.org/en/list/303/). In order to produce synthetic maps of the investigated areas to monitor the status of vegetation and ongoing subtle changes, satellite data were processed using Principal Component Analysis (PCA). Results from our investigations pointed out an ongoing degradation trend.