Cities host more than half of the world’s population and due to global warming and land use change their vulnerability to deadly heat waves has increased. A healthy vegetated landscape can abate heat wave severity and diminish the related urban heat island through the process of evapotranspiration. This research aimed to develop a methodology for cities to use publicly available Copernicus land cover maps within the i-Tree Cool Air water and energy balance model to map air temperature and humidity. The manuscript presents proof of concept using Naples, Italy with its Mediterranean climate characterized by limited soil water for cooling via evapotranspiration. The approach achieved strong correlations between predicted and observed air temperatures across the city (r ≥ 0.89). During the warm season of 2020, forested land cover was 5°C cooler than land cover dominated by impervious cover. Simulated land cover change, limited to a 10% increase or decrease in tree cover, generated an inverse change of 0.2°C in maximum hourly air temperature, with more trees obtaining cooler air. Soil water limited the cooling, with the generally wetter spring season enabling greater cooling of air temperatures, and summer droughts without irrigation had constrained cooling. Sustainable urban design will likely require an increase in plant cover along with a reduction of impervious surfaces that absorb and reradiate heat in order to improve community resilience to heat waves.

This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images’ T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10⁻⁵. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape.

The normalized difference vegetation index (NDVI) is commonly used to detect spatiotemporal changes of vegetation cover. This study modeled the spatiotemporal changes of land cover on Pianosa Island, Italy, in the period 1999–2015, using the multi-temporal Landsat images. Since the end of the 1990s, the natural vegetation has been re-colonizing an area of abandoned agricultural land and the island is undergoing a process of re-naturalization in harsh (drought and hot) environmental conditions. Hence, it is an ideal test site to monitor the effects of anthropogenic and climatic stressors on vegetation dynamics under Mediterranean climate. In this work, we proposed a new statistical approach based on a pixel-by-pixel analysis of multi-temporal Landsat images. Mean (µ) and standard deviation (σ) values of the NDVI images taken in 2015 were used for the determination of the pixel thresholds (µ ± 3σ). The evaluation of land cover change was carried out by comparing the µ value of a single NDVI pixel for 2015 with the same pixel of different years of the study period. The results indicate that surface reflectance (SR) Landsat images are more suitable in detecting the vegetation dynamics on the island than the top of atmosphere (TOA) ones and highlight an increasing trend of vegetation cover on Pianosa Island, mainly during the early seven years following the land abandonment in all the main land cover classes: abandoned crops and pastures, Mediterranean macchia, and woodland. However, the abandoned agricultural and pasture areas showed a higher increase in the vegetation cover and a shift in the shape of the normalized frequency distribution of the SR NDVI data during the study period, suggesting that a colonization process from other vegetation classes is occurring (i.e., Mediterranean macchia and trees are colonizing the abandoned land, partly replacing herbaceous species). Our data highlight that the statistical approach applied in this study is suitable for detecting vegetation cover changes associated with anthropogenic and climatic drivers in a typical Mediterranean environment and could be proposed as a new methodological approach in several other land monitoring studies.

In this study, the sensitivity of multi-polarization synthetic aperture radar (SAR) features to vegetation cover is investigated over a test case of environmental importance: the Coiba National Park, Panama. Single-polarization intensity features and polarimetric features derived from the eigenvalue/eigenvector decomposition are analysed and their classification performance, evaluated against a reference land-cover map using a simple clustering algorithm, is contrasted with conventional optical features. Experiments, undertaken using actual L-band full-polarimetric SAR and Landsat data, show that (a) polarimetric information plays a key role in improving the classification accuracy with some polarimetric features performing better than single-polarization and optical ones, (b) classification performance of radar features is significantly affected by incidence angles, and (c) a joint use of different radar features is expected to increase classification accuracy.

The characterization and mapping of fuel types is one of the most important factors to consider in the development of accurate fire behavior models. This study introduces a new methodology for generating a fuel map that can be easily updated on an annual basis. The method involves identifying associations between the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover MCD12Q1 classes and the fuel-type classes categorized by the Canadian Fire Behavior Prediction System (FBP). For this purpose, MCD12Q1 Land Cover Type 1 data (MODIS LCM) were collected for the Canadian region. Concurrently, the Canadian fuel-type map implemented in the Fire Behavior Prediction System (FBP FTM) served as the reference dataset. Both MODIS LCM and FBP FTM were reclassified into a new Canadian FTM (NC-FTM) based on seven fuel-type classes. The method involves three key steps: (1) adapting MODIS LCM and FBP FTM for the classification of the Canadian region, (2) removing ambiguity, and (3) characterizing and assessing the accuracy of the new fuel-type classification using a confusion matrix classification algorithm. The achieved accuracy for the new classification exceeds 85%, highlighting the effectiveness of the approach. The use of MODIS LCM offers a cost-effective method for the annual characterization and mapping of fuel types, providing a practical improvement to the FBP model for Canada. Furthermore, with the proposed methodology, a fuel-type map can be generated for other specific areas of interest in the boreal region.