Preface to the special issue: the role of geomatics in hydrogeological risk

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2019

Self Cite

<p><strong>Abstract.</strong> The goal of this work is to assess a method for supporting decisions regarding identification of most suitable areas for two types of harvesting approaches in forestry: skyline vs. forwarder. The innovative aspect consists in simulating the choices done during the planning in forestry operations. To do so, remote sensing data from an aerial laser scanner were used to create a digital terrain model (DTM) of ground surface under vegetation cover. Features extracted from the DTM are used as input for several machine learning predictors. Features are slope, distance from nearest roadside, relative height from nearest roadside and roughness index. Training and validation is done using areas defined by experts in the study area. Results show a K value of almost 0.92 for the classifier with best results, random forest. Sensibility of each feature is assessed, showing that both distance and height difference from nearest road-side are more significant than overall DTM value.</p>

Section: Introductionmentioning

confidence: 99%

Planning Harvesting Operations in Forest Environment: Remote Sensing for Decision Support

Piragnolo

Grigolato

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2019

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“…Thanks to the continuous increase of applications using geo-spatial data (Guarnieri et al 2015;Habib et al 2005;Pirotti et al 2015;Remondino, Guarnieri, and Vettore 2005), in the last decades several mobile mapping systems (MMSs) have been developed, mostly based on the use of terrestrial or airborne vehicles (Chiang, Noureldin, and El-Sheimy 2003;Kraus and Pfeifer 1998;Pirotti et al 2014;Remondino et al 2011;Toth 2001;Toth and Grejner-Brzezinska 1997), equipped with remote sensing instruments such as laser scanners and cameras.…”

Section: Introductionmentioning

confidence: 99%

Toward the use of smartphones for mobile mapping

Masiero

Fissore

Geo-spatial Information Science

et al. 2016

This paper considers the use of a low cost mobile device in order to develop a mobile mapping system (MMS), which exploits only sensors embedded in the device. The goal is to make this MMS usable and reliable even in difficult environments (e.g. emergency conditions, when also WiFi connection might not work). For this aim, a navigation system able to deal with the unavailability of the GNSS (e.g. indoors) is proposed first. Positioning is achieved by a pedestrian dead reckoning approach, i.e. a specific particle filter has been designed to enable good position estimations by a small number of particles (e.g. 100). This specific characteristic enables its real time use on the standard mobile devices. Then, 3D reconstruction of the scene can be achieved by processing multiple images acquired with the standard camera embedded in the device. As most of the vision-based 3D reconstruction systems are recently proposed in the literature, this work considers the use of structure from motion to estimate the geometrical structure of the scene. The detail level of the reconstructed scene is clearly related to the number of images processed by the reconstruction system. However, the execution of a 3D reconstruction algorithm on a mobile device imposes several restrictions due to the limited amount of available energy and computing power. This consideration motivates the search for new methods to obtain similar results with less computational cost. This paper proposes a novel method for feature matching, which allows increasing the number of correctly matched features between two images according to our simulations and can make the matching process more robust.

“…Multispectral cameras mounted on RPAS allow custom deployment for multi-band imagery over areas of interest. RPAS is therefore a suitable candidate to fill the gaps in the timeline where satellite imagery is not available (Pirotti et al, 2015), be it for long revisit times, for cloud cover or for a combination of both (Pirotti et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Vegetation Indices From Rpas and Sentinel-2 Imagery for Detecting Permanent Pastures

Piragnolo

Lusiani²,

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

2018

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Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.