Research papers

Detection And Characterization of Forest Harvesting In Piedmont Through Sentinel-2 Imagery: A Methodological Proposal


This study evaluated the effectiveness of Sentinel-2 (S2) as a tool for early detection and estimation of forest harvesting in the Piemonte Region, which can be used by the regional forest administration. The priority was the detection, at the regional scale, of annual forest cover changes with the following goals: i) mapping of irregular (in respect of the regional Forestry Regulation) forest cuts; ii) quantification of the intensity of the silvicultural interventions. Results are expected to support forest police controls.

The proposed procedure is based on a supervised classification approach based on Random Forest algorithm. Accuracy of harvested areas detection proved to be high (overall accuracy 98%). Characterization of the occurred forest cuts was obtained computingthe local coefficient of variationof the normalized difference vegetation index (NDVI) after harvesting, that showed to be a good predictor of forest harvesting intensity.


Forest Harvesting detection; Forest Change detection; Sentinel 2; Forest harvesting characterization

Full Text:




Banner A.V., Ahern F.J. 1995 - Forest clearcut mapping using airborne C-band SAR and simulated RADARSAT imagery. Canadian Journal of Remote Sensing 21: 124–137.

Borgogno-Mondino E., Lessio A., Gomarasca M.A. 2016 -A fast operative method for NDVI uncertainty estimation and its role in vegetation analysis. European Journal of Remote Sensing 49: 137–156.

Camerano P., Terzuolo P.G., Guiot E. Giannetti F. 2017 - La Carta Forestale del Piemonte – Aggiornamento 2016. IPLA S.p.A. – Regione Piemonte.

Cohen W.B., Fiorella M., Gray J., Helmer E., Anderson K. 1998 - An efficient and accurate method for mapping forest clearcuts in the Pacific Northwest using Landsat imagery. Photogrammetric engineering and remote sensing 64: 293–299.

Cohen W.B., Yang Z., Healey S.P., Kennedy R.E., Gorelick N. 2018 - A LandTrendr multispectral ensemble for forest disturbance detection. Remote sensing of environment 205: 131–140.

De Petris S., Boccardo P., Borgogno-Mondino E. 2019 - Detection and characterization of oil palm plantations through MODIS EVI time series. International Journal of Remote Sensing: 1–15.

dos Santos Silva M.P., Camara G., Escada M.I.S., De Souza R.C.M. 2008 - Remote-sensing image mining: detecting agents of land-use change in tropical forest areas. International Journal of Remote Sensing 29: 4803–4822.

Drieman J.A. 1994 - Forest cover typing and clearcut mapping in Newfoundland with C-band SAR. Canadian Journal of Remote Sensing/Journal Canadien de Teledetection 20: 11–16.

Espíndola R.P., Ebecken N.F.F. 2005 - On extending F-measure and G-mean metrics to multi-class problems. WIT Transactions on Information and Communication Technologies 35: 10.

Fabbio G. 2016 - Coppice forests, or the changeable aspect of things, a review. Annals of Silvicultural Research 40: 108-132.

Franklin J.F., Spies T.A., Van Pelt R., Carey A.B., Thornburgh D.A., Berg D.R., Lindenmayer D.B., Harmon M.E., Keeton W.S., Shaw D.C. 2002 - Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. Forest Ecology and Management 155: 399–423.

Franklin S.E. 2001 -Remote sensing for sustainable forest management. CRC press.

Fransson J.E.S., Walter F., Olsson H. 1999 - Identification of clear felled areas using SPOT P and Almaz-1 SAR data. International Journal of Remote Sensing 20: 3583–3593.

Gamon J.A., Field C.B., Goulden M.L., Griffin K.L., Hartley A.E., Joel G., Peñuelas J., Valentini R. 1995 -Relationships Between NDVI,Canopy Structure, and Photosynthesis in Three Californian Vegetation Types. Ecological Applications 5: 28–41.

Gerard F.F., North P.R.J. 1997- Analyzing the effect of structural variability and canopy gaps on forest BRDF using a geometric-optical model. Remote Sensing of Environment 62: 46–62.

Graham C.H., Blake J.G. 2001 - Influence of patch-and landscape-level factors on bird assemblages in a fragmented tropical landscape. Ecological Applications 11: 1709–1721.

Hall R.J., Kruger A.R., Moore W.C., Scheffer J., Titus S.J. 1989 -A statistical evaluation of Landsat TM and MSS data for mapping forest cutovers. The Forestry Chronicle 65: 441–449.

Hansen M.C., Potapov P.V., Moore R., Hancher M., Turubanova S.A.A., Tyukavina A., Thau D., Stehman S.V., Goetz S.J., Loveland T.R. 2013 -High-resolution global maps of 21st-century forest cover change. Science 342: 850–853.

Holmgren P., Thuresson T. 1998 - Satellite remote sensing for forestry planning—A review. Scandinavian Journal of Forest Research 13: 90–110.

IPLA 2000 - PFT - Piani Forestali Territoriali. Regione Piemonte.

Kennedy R., Yang Z., Gorelick N., Braaten J., Cavalcante L., Cohen W., Healey S., 2018 - Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing 10: 691.

Kennedy R.E., Cohen W.B., Schroeder T.A. 2007- Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment 110: 370–386.

Kennedy R.E., Yang Z., Cohen W.B. 2010 - Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment 114: 2897–2910.

Lessio A., Fissore V., Borgogno-Mondino E. 2017 - Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring. Journal of Imaging 3: 49.

Momeni M., Saradjian M.R. 2007- Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day/Night LST algorithm. Remote Sensing of Environment 106: 190–198.

Otukei J.R., Blaschke T. 2010 - Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation 12: S27–S31.

Rogan J., Franklin J., Roberts D.A. 2002 - A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sensing of Environment 80: 143–156.

Copyright (c) 2020 Samuele De Pretis, Roberta Berretti, Elisa Guiot, Fabio Giannetti, Renzo Motta, Enrico Borgogno-Mondino

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License

All texts reported in and all materials directly downloadable from this page are licensed under a Creative Commons Attribution 4.0 International License.