Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

Abstract

Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 "forest" points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km 2 in Tuscany, central Italy. Author Contributions: Conceptualization and Methodology N.P.; Formal Analysis N.P. and M.B.; Writing-Original Draft Preparation, Review and Editing, N.P. and M.B. Funding: This research received no external funding. Conflicts of Interest:The authors declare no conflicts of interest.

Continuous Detection of Small-Scale Changes in Scots Pine Dominated Stands Using Dense Sentinel-2 Time Series

“…Our results are in line with other studies on the Sentinel-2 time series in forest disturbances monitoring, which number, however, is still limited. In forest disturbance studies using the Sentinel-2 data, Puletti and Bascietto [49] used the NDVI series to separate cut from uncut forests, with high accuracy. The potential of the Sentinel-2 series was confirmed in the study of Navarro et al [51] in the monitoring of cork oak decline.…”

“…Furthermore, the Sentinel-2 imagery is available for free, which, in combination with the variety of open-source software, creates opportunities relating to the successful application of this approach in operational forest health monitoring. So far, the Sentinel-2 time series have been used for the assessment of forest cuttings [49] and treefall gaps [50], as well as the monitoring of cork oak decline [51]. Sentinel-2 was also used in the mapping of bark beetle green attack stages [30], Scots pine stands defoliation [31], and red-edge bands were assessed in forest decline [26].…”

Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control

“…However, as pointed out by Vuolo et al [56], the widespread use of NDVI in remote sensing as a tool for monitoring forest areas has stimulated the development of models that reduce the noise caused by clouds. Puletti and Bascietto [57] conclude that due to the high resolution of the data provided by Sentinel-2, spectral variability over short time periods (5 days) could be considered negligible. Moreover, this information would be more accurate than that provided by Landsat, whose resolution is lower (30 m) and temporal resolution is higher (16 days) [58].…”

Efficient Estimation of Biomass from Residual Agroforestry

“…An example of yearly NDVI trajectories for an olive grove and a broad-leaved forest in the study area is given in Figure 4. NDVI variability between successive values largely accounted for discrepancies between the spatial resolutions of the cloud mask (60 m) and the spectral bands [34], particularly evident at the boundary between the cloud and non-cloud mask. The raw NDVI trajectory of the broad-leaved forest appears to have been more severely affected by this issue, probably due to the higher frequency of cloudy conditions over mountain ranges where this land cover is prevalent.…”