Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.

Landslides have been observed on several planets and minor bodies of the solar System, including the Moon. Notwithstanding different types of slope failures have been studied on the Moon, a detailed lunar landslide inventory is still pending. Undoubtedly, such will be in a benefit for future geological and morphological studies, as well in hazard, risk and suscept- ibility assessments. A preliminary survey of lunar landslides in impact craters has been done using visual inspection on images and digital elevation model (DEM) (Brunetti et al. 2015) but this method suffers from subjective interpretation. A new methodology based on polynomial interpolation of crater cross-sections extracted from global lunar DEMs is presented in this paper. Because of their properties, Chebyshev polynomials were already exploited for para- metric classification of different crater morphologies (Mahanti et al., 2014). Here, their use has been extended to the discrimination of slumps in simple impact craters. Two criteria for recognition have provided the best results: one based on fixing an empirical absolute thresholding and a second based on statistical adaptive thresholding. The application of both criteria to a data set made up of 204 lunar craters’ cross-sections has demonstrated that the former criterion provides the best recognition

The impressive success of Structure-from-Motion Photogrammetry (SfM) has spread out the application of image-based 3D reconstruction to a larger community. In the field of Archeological Heritage documentation, this has opened the possibility of training local people to accomplish photogrammetric data acquisition in those remote regions where the organization of 3D surveying missions from outside may be difficult, costly or even impossible. On one side, SfM along with low-cost cameras makes this solution viable. On the other, the achievement of high-quality photogrammetric outputs requires a correct image acquisition stage, being this the only stage that necessarily has to be accomplished locally. This paper starts from the analysis of the well-know "3x3 Rules" proposed in 1994 when photogrammetry with amateur camera was the state-of-the art approach and revises those guidelines to adapt to SfM. Three aspects of data acquisition are considered: geometry (control information, photogrammetric network), imaging (camera/lens selection and setup, illumination), and organization. These guidelines are compared to a real case study focused on Ziggurat Chogha Zanbil (Iran), where four blocks from ground stations and drone were collected with the purpose of 3D modelling.

<p><strong>Abstract.</strong> This paper describes the use of some tool to help training of photogrammetry for applications in the field of landslide and slope stability assessment and monitoring. These tools have been used in classes of the MSc on Civil Eng. for Risk Mitigation at Politecnico di Milano university, Lecco (Italy). The first tools are hardware facilities. The first one consists of a ‘Landslide Simulator,’ where shallow landslides may be reproduced at small scale. Simulations are also used here for active-learning purpose. In particular, here the use of digital images to obtain multi-temporal information is presented. The second tool is a ‘Rock face 3D Modelling Simulator.’ This is used by students to learn how a photogrammetric block should be designed in order to reconstruct rock slopes using Structure-from-Motion photogrammetry. The last to tools are software packages (CloudCompare and LIME) devoted to point cloud analysis (including change detection/ deformation analysis) and advanced visualization, respectively. The combination of these tools together with datasets from either lab and the real field, has been successfully tested to provide efficient training to students in an active-learning fashion.</p>

Abstract. Landslides are one of the most diffused hazard events in the world, they can occur in different locations under different triggering factors. As such, they are also one of the most studied hazards, while the mechanism of an event is known to the scholars, more difficulties are found in forecasting the location and time of the following event. However, scholars are putting great effort into modelling the phenomena through various tools, as such susceptibility mapping is one of the initial and key steps in the hazard assessment. While effort is put on producing such maps, less is put on the evaluation of those outcomes. The current work aims to analyse the behaviour of two validation metrics – Receiver Operating Characteristics (ROC) and Precision Recall Curve (PRC). The former is widely used in susceptibility modelling, while the latter not so much utilized. However, scholars are highlighting a drawback of the ROC – it is not able to discriminate imbalanced datasets and is providing unreliable outcomes, and as an alternative is proposed the PRC which does not exhibit such flaws. In order to test the performance of both metrics, they were applied to three susceptibility models produced using Statistical Index, Logistic Regression and Random Forest for the area of Val Tartano, Northern Italy. As a result, it was determined that when the metrics are applied to balanced datasets they exhibit similar behaviour; on the contrary when imbalanced classes are introduced PRC is depicting the model performance in a more precise manner.

Abstract. Natural hazards such as landslides, whether they are driven by meteorologic or seismic processes, are constantly shaping Earth’s surface. In large percentage of the slope failures, they are also causing huge human and economic losses. As the problem is complex in its nature, proper mitigation and prevention strategies are not straightforward to implement. One important step in the correct direction is the integration of different fields; as such, in this work, we are providing a general overview of approaches and techniques which are adopted and integrated for landslide monitoring and mapping, as both activities are important in the risk prevention strategies. Detailed landslide inventory is important for providing the correct information of the phenomena suitable for further modelling, analysing and implementing suitable mitigation measures. On the other hand, timely monitoring of active landslides could provide priceless insights which can be sufficient for reducing damages. Therefore, in this work popular methods are discussed that use remotely-sensed datasets with a particular focus on the implementation of machine learning into landslide detection, susceptibility modelling and its implementation in early-warning systems. Moreover, it is reviewed how Citizen Science is adopted by scholars for providing valuable landslide-specific information, as well as couple of well-known platforms for Volunteered Geographic Information which have the potential to contribute and be used also in the landslide studies. In addition to proving an overview of the most popular techniques, this paper aims to highlight the importance of implementing interdisciplinary approaches.

Commission VI, WG VI/1 KEY WORDS: Planetary mapping, Impact craters, Landslides, Chebyshev polynomials ABSTRACT:Geological slope failure processes have been observed on the Moon surface for decades, nevertheless a detailed and exhaustive lunar landslide inventory has not been produced yet. For a preliminary survey, WAC images and DEM maps from LROC at 100 m/pixels have been exploited in combination with the criteria applied by Brunetti et al. (2015) to detect the landslides. These criteria are based on the visual analysis of optical images to recognize mass wasting features. In the literature, Chebyshev polynomials have been applied to interpolate crater cross-sections in order to obtain a parametric characterization useful for classification into different morphological shapes. Here a new implementation of Chebyshev polynomial approximation is proposed, taking into account some statistical testing of the results obtained during Least-squares estimation. The presence of landslides in lunar craters is then investigated by analyzing the absolute values off odd coefficients of estimated Chebyshev polynomials. A case study on the Cassini A crater has demonstrated the key-points of the proposed methodology and outlined the required future development to carry out.

Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations.