Sviluppo di una procedura innovativa per la cubatura del volume della massa legnosa di fustaie di latifoglie mediante LiDAR

La tecnica di scansione aerea con strumentazione LiDAR (Light Detection and Ranging) è sempre più presa in considerazione ai fini di analisi e monitoraggio degli attributi dendrometrici dei popolamenti forestali, ma anche per stimare attributi complessi quali il volume della massa legnosa e la biomassa legnosa. Il presente contributo propone una procedura innovativa per la stima del volume di massa legnosa di soprassuoli forestali mediante elaborazione di dati LiDAR in ambiente GIS, in particolare per i popolamenti a prevalenza di latifoglie dove i convenzionali metodi di stima basati sulle misurazioni LiDAR dell’altezza dei singoli fusti arborei conducono in genere a risultati relativamente insoddisfacenti.L'articolo è disponibile sul sito dell'editore http://www.asita.i

rasterdiv ‐ an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—rasterdiv—to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms

Supervised methods of image segmentation accuracy assessment in land cover mapping

Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the basic spatial unit of analysis. As it is typically desirable to know the accuracy with which the objects have been delimited prior to undertaking the classification, numerous methods have been used for accuracy assessment. This paper reviews the state-of-the-art of image segmentation accuracy assessment in land cover mapping applications. First the literature published in three major remote sensing journals during 2014–2015 is reviewed to provide an overview of the field. This revealed that qualitative assessment based on visual interpretation was a widely-used method, but a range of quantitative approaches is available. In particular, the empirical discrepancy or supervised methods that use reference data for assessment are thoroughly reviewed as they were the most frequently used approach in the literature surveyed. Supervised methods are grouped into two main categories, geometric and non-geometric, and are translated here to a common notation which enables them to be coherently and unambiguously described. Some key considerations on method selection for land cover mapping applications are provided, and some research needs are discussed

Estimating Understory Temperatures Using MODIS LST in Mixed Cordilleran Forests

Satellite remote sensing provides a rapid and broad-scale means for monitoring vegetation phenology and its relationship with fluctuations in air temperature. Investigating the response of plant communities to climate change is needed to gain insight into the potentially detrimental effects on ecosystem processes. While many studies have used satellite-derived land surface temperature (LST) as a proxy for air temperature, few studies have attempted to create and validate models of forest understory temperature (Tust), as it is obscured from these space-borne observations. This study worked to predict instantaneous values of Tust using daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST data over a 99,000 km2 study area located in the Rocky Mountains of western Alberta, Canada. Specifically, we aimed to identify the forest characteristics that improve estimates of Tust over using LST alone. Our top model predicted Tust to within a mean absolute error (MAE) of 1.4 °C with an overall model fit of R2 = 0.89 over two growing seasons. Canopy closure and the LiDAR-derived standard deviation of canopy height metric were found to significantly improve estimations of Tust over MODIS LST alone. These findings demonstrate that canopy structure and forest stand-type function to differentiate understory air temperatures from ambient canopy temperature as seen by the sensor overhead

Airborne Laser Scanning of Forest Resources: an overview of research in Italy as a commentary case study

This article reviews the recent literature concerning airborne laser scanning for forestry purposes in Italy, and presents the current methodologies used to extract forest characteristics from discrete return ALS (Airborne Laser Scanning) data. Increasing interest in ALS data is currently being shown, especially for remote sensing-based forest inventories in Italy; the driving force for this interest is the possibility of reducing costs and providing more accurate and efficient estimation of forest characteristics. This review covers a period of approximately ten years, from the first application of laser scanning for forestry purposes in 2003 to the present day, and shows that there are numerous ongoing research activities which use these technologies for the assessment of forest attributes (e.g., number of trees, mean tree height, stem volume) and ecological issues (e.g., gap identification, fuel model detection). The basic approaches – such as single tree detection and area-based modeling – have been widely examined and commented in order to explore the trend of methods in these technologies, including their applicability and performance. Finally this paper outlines and comments some of the common problems encountered in operational use of laser scanning in Italy, offering potentially useful guidelines and solutions for other countries with similar conditions, under a rather variable environmental framework comprising Alpine, temperate and Mediterranean forest ecosystems