Afforestation monitoring through automatic analysis of 36-years Landsat Best Available Composites

doi:10.3832/ifor4043-015

Afforestation monitoring through automatic analysis of 36-years Landsat Best Available Composites

Alice Cavalli⁽¹⁾, Saverio Francini^(2-3) , Giulia Cecili⁽⁴⁾, Claudia Cocozza⁽³⁾, Luca Congedo⁽⁵⁾, Valentina Falanga⁽⁴⁾, Gian Luca Spadoni⁽⁴⁾, Mauro Maesano⁽¹⁾, Michele Munafò⁽⁵⁾, Gherardo Chirici^(3-2), Giuseppe Scarascia Mugnozza⁽¹⁾

iForest - Biogeosciences and Forestry, Volume 15, Issue 4, Pages 220-228 (2022)
doi: https://doi.org/10.3832/ifor4043-015
Published: Jul 12, 2022 - Copyright © 2022 SISEF

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The study of afforestation is crucial to monitor land transformations and represents a central topic in sustainable development procedures, in terms of climate change, ecosystem services monitoring, and planning policies activities. Although surveying afforestation is important, the assessment of the growing forests is difficult, since land cover has different durations depending on the species. In this context, remote sensing can be a valid instrument to evaluate the afforestation process. Nevertheless, while a vast literature on forest disturbance exists, only a few studies focus on afforestation and almost none directly exploits remote sensing data. This study aims to automatically classify non-forest, afforestation, and forest areas using remote sensing data. To this purpose, we constructed a reference dataset of 61 polygons that suffered a change from non-forest to forest in the period 1988-2020. The reference data were constructed with the Land Use Inventory of Italy and through photointerpretation of orthophotos (1988-2012, spatial resolution 50 × 50 cm) and very high-resolution images (2012-2020, spatial resolution 30 × 30 cm). Using Landsat Best Available Pixel composites time-series (1984-2020) we calculated 52 temporal predictors: four temporal metrics (median, standard deviation, Pearson’s correlation coefficient R, and slope) calculated for 13 different bands (the six Landsat spectral bands, three Spectral Vegetation Indices, and four Tasseled Cap Indices). To verify the possibility of distinguishing afforestation from non-forest and forest, given the differences between them can be minimal, we tested four different models aiming at classifying the following categories: (i) non-forest/afforestation, (ii) afforestation/forest, (iii) non-forest/forest and (iv) non-forest/afforestation/forest. Temporal predictors were used with random forest which was calibrated using random search, validated using k-fold Cross-Validation Overall Accuracy (OA_cv), and further using out-of-bag independent data (OA_oob). Results illustrate that the distinction of afforestation/forest reaches the largest OA_cv (87%), followed by non-forest/forest (83%), non-forest/afforestation (75%) and non-forest/afforestation/forest (72%). The different OA values confirm that the difference in photosynthetic activity between forest and afforestation can be analysed through remote sensing to distinguish them. Although remote sensing data are currently not exploited to monitor afforestation areas our results suggest it may be a valid support for country-level monitoring and reporting.

Keywords

Afforestation, Remote Sensing, Land Cover Monitoring, Random Forest

Authors’ address

(1)

0000-0002-5460-1245
0000-0002-4325-951X
0000-0003-0357-4360
Department of Innovation in Biology, Agri-Food and Forest systems - DIBAF, University of Tuscia, v. San Camillo de’ Lellis snc, I-01100 Viterbo (Italy)

(2)

0000-0001-6991-0289
0000-0002-0669-5726
Fondazione per il futuro delle città, Firenze (Italy)

(3)

0000-0001-6991-0289
0000-0002-0167-8863
0000-0002-0669-5726
Department of Agricultural, Food and Forestry Systems, University of Florence (Italy)

(4)

0000-0002-8199-7660
0000-0003-0454-8850
0000-0001-6083-6051
Dept. of Biosciences and Territory, University of Molise, c.da Fonte Lappone, I-86090 Pesche, IS (Italy)

(5)

0000-0001-7283-116X
0000-0002-3415-6105
Italian Institute for Environmental Protection and Research - ISPRA, v. Vitaliano Brancati 48, I-00144 Rome (Italy)

Corresponding author

Saverio Francini
saverio.francini@unifi.it

Citation

Cavalli A, Francini S, Cecili G, Cocozza C, Congedo L, Falanga V, Spadoni GL, Maesano M, Munafò M, Chirici G, Scarascia Mugnozza G (2022). Afforestation monitoring through automatic analysis of 36-years Landsat Best Available Composites. iForest 15: 220-228. - doi: 10.3832/ifor4043-015

Academic Editor

Agostino Ferrara

Paper history

Received: Dec 20, 2021
Accepted: May 06, 2022

First online: Jul 12, 2022
Publication Date: Aug 31, 2022
Publication Time: 2.23 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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