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iForest - Biogeosciences and Forestry

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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

Research Articles


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 (OAcv), and further using out-of-bag independent data (OAoob). Results illustrate that the distinction of afforestation/forest reaches the largest OAcv (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)
Alice Cavalli 0000-0002-5460-1245
Mauro Maesano 0000-0002-4325-951X
Giuseppe Scarascia Mugnozza 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)
Saverio Francini 0000-0001-6991-0289
Gherardo Chirici 0000-0002-0669-5726
Fondazione per il futuro delle città, Firenze (Italy)
(3)
Saverio Francini 0000-0001-6991-0289
Claudia Cocozza 0000-0002-0167-8863
Gherardo Chirici 0000-0002-0669-5726
Department of Agricultural, Food and Forestry Systems, University of Florence (Italy)
(4)
Giulia Cecili 0000-0002-8199-7660
Valentina Falanga 0000-0003-0454-8850
Gian Luca Spadoni 0000-0001-6083-6051
Dept. of Biosciences and Territory, University of Molise, c.da Fonte Lappone, I-86090 Pesche, IS (Italy)
(5)
Luca Congedo 0000-0001-7283-116X
Michele Munafò 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

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(1)
Agrillo E, Filipponi F, Pezzarossa A, Casella L, Smiraglia D, Orasi A, Attorre F, Taramelli A (2021)
Earth observation and biodiversity big data for forest habitat types classification and mapping. Remote Sensing 13 (7): 1231.
CrossRef | Gscholar
(2)
Angelucci G (2011)
Dinamica di vegetazione in aree di post-abbandono della pianura padana. Report annuale [Vegetation dynamics in post-abandonment areas of the Po Valley. Annual report]. PhD thesis, Archivio Istituzionale della Ricerca (AIR), Università di Milano, Italy, pp. 115. [in Italian]
Gscholar
(3)
Arnold S, Kosztra B, Banko G, Smith G, Hazeu G, Bock M (2013)
The EAGLE concept - A vision of a future European Land Monitoring Framework. In: “EARSeL Symposium Proceedings 2013, Towards Horizon 2020”. Matera (Italy) 3-6 June 2013. EARSeL and CNR, Rome, Italy, pp. 551-568.
Gscholar
(4)
Belgiu M, Dragu L (2016)
Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114: 24-31.
CrossRef | Gscholar
(5)
Bergstra J, Bengio Y (2012)
Random search for hyper-parameter optimization. Journal of Machine Learning Research 13: 281-305.
Online | Gscholar
(6)
Breiman L, Cutler A (2001)
Random Forest, machine learning. Statistics Department, University of California, Berkely, CA, USA, pp. 33.
Gscholar
(7)
Brovelli MA, Sun Y, Yordanov V (2020)
Monitoring forest change in the Amazon using multi-temporal remote sensing data and machine learning classification on Google Earth Engine. ISPRS International Journal of Geo-Information 9 (10): 1-21.
CrossRef | Gscholar
(8)
Czimczik CI, Mund M, Schulze ED, Wirth C (2005)
Effects of reforestation, deforestation, and afforestation on carbon storage in soils. SEB Experimental Biology Series, Taylor and Francis, Abingdon-on-Thames, UK, pp. 319-330.
CrossRef | Gscholar
(9)
De Fioravante P, Luti T, Cavalli A, Giuliani C, Dichicco P, Marchetti M, Chirici G, Congedo L, Munafò M (2021)
Multispectral sentinel-2 and sar sentinel-1 integration for automatic land cover classification. Land 10 (6): 1-35.
CrossRef | Gscholar
(10)
FAO (2001)
Global forest resources assessment 2000: main report. Food and Agriculture Organization, Rome, Italy.
Online | Gscholar
(11)
FAO (2020)
Global forest resources assessment 2020: main report. Food and Agriculture Organization, Rome, Italy, pp. 184.
CrossRef | Gscholar
(12)
Filipponi F, Valentini E, Xuan AN, Guerra Wolf C F, Andrzejak M, Taramelli A (2018)
Global MODIS fraction of green vegetation cover for monitoring abrupt and gradual vegetation changes. Remote Sensing 10 (4): 653.
CrossRef | Gscholar
(13)
Forzieri G, Girardello M, Ceccherini G, Spinoni J, Feyen L, Hartmann H, Beck PSA, Camps-Valls G, Chirici G, Mauri A, Cescatti A (2021)
Emergent vulnerability to climate-driven disturbances in European forests. Nature Communications 12 (1): 1-12.
CrossRef | Gscholar
(14)
Francini S (2021)
BAP-GEE - A Google Earth Engine application for Best Available Pixel composites calculation, visualization, calibration, and download. Web site.
Online | Gscholar
(15)
Francini S, McRoberts RE, Giannetti F, Mencucci M, Marchetti M, Scarascia Mugnozza G, Chirici G (2020)
Near-real time forest change detection using PlanetScope imagery. European Journal of Remote Sensing 53 (1): 233-244.
CrossRef | Gscholar
(16)
Francini S, Hermosilla T, Coops N, White J, Wulder M, Chirici G (2021a)
A Google Earth Engine application for Best Available Pixel composites calculation, visualization, calibration, and download. Web site.
Online | Gscholar
(17)
Francini S, McRoberts RE, Giannetti F, Marchetti M, Scarascia Mugnozza G, Chirici G (2021b)
The Three Indices Three Dimensions (3I3D) algorithm: a new method for forest disturbance mapping and area estimation based on optical remotely sensed imagery. International Journal of Remote Sensing 42 (12): 4697-4715.
CrossRef | Gscholar
(18)
Giannetti F, Pegna R, Francini S, McRoberts RE, Travaglini D, Marchetti M, Mugnozza GS, Chirici G (2020)
A new method for automated clearcut disturbance detection in Mediterranean coppice forests using Landsat time series. Remote Sensing 12 (22): 1-23.
CrossRef | Gscholar
(19)
Gorelick N, Hancher M, Dixon M, Ilyuschenko S, Thau D, Moore R (2017)
Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 2020: 18-27.
CrossRef | Gscholar
(20)
Griffiths P, Van Der Linden S, Kuemmerle T, Hostert P (2013)
Erratum: a pixel-based landsat compositing algorithm for large area land cover mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (5): 2088-2101.
CrossRef | Gscholar
(21)
Gálos B, Hagemann S, Hänsler A, Kindermann G, Rechid D, Sieck K, Teichmann C, Jacob D (2013)
Case study for the assessment of the biogeophysical effects of a potential afforestation in Europe. Carbon Balance and Management 8 (1): 1-12.
CrossRef | Gscholar
(22)
Hansen MC, Potapov P, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman S, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013)
High-resolution global maps of 21st-century forest cover change. Science 342 (6160): 850-853.
CrossRef | Gscholar
(23)
Hawrylo P, Francini S, Chirici G, Giannetti F, Parkitna K, Krok G, Mitelsztedt K, Lisanczuk M, Sterenczak K, Ciesielski M, Wezyk P, Socha J (2020)
The use of remotely sensed data and Polish NFI plots for prediction of growing stock volume using different predictive methods. Remote Sensing 12 (20): 1-20.
CrossRef | Gscholar
(24)
Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW (2015a)
An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158: 220-234.
CrossRef | Gscholar
(25)
Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW (2015b)
Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170: 121-132.
CrossRef | Gscholar
(26)
Huang H, Chen Y, Clinton N, Wang J, Wang X, Liu C, Gong P, Yang J, Bai Y, Zheng Y, Zhu Z (2017)
Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing of Environment 202: 166-176.
CrossRef | Gscholar
(27)
Jönsson P, Cai Z, Melaas E, Friedl MA, Eklundh L (2018)
A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data. Remote Sensing 10 (4): 635.
CrossRef | Gscholar
(28)
Kim DH, Sexton JO, Noojipady P, Huang C, Anand A, Channan S, Feng M, Townshend JR (2014)
Global, Landsat-based forest-cover change from 1990 to 2000. Remote Sensing of Environment 155: 178-193.
CrossRef | Gscholar
(29)
Koch B (1999)
The contribution of remote sensing for afforestation and forest biodiversity assessment. In: Proceedings of the conference “Remote Sensing and Forest Monitoring”. Rogow (Poland) 1-3 June 1999. T. Zawila-Niedzwiecki, M. Brach, Poland, pp. 3-13.
Gscholar
(30)
Laurin GV, Francini S, Luti T, Chirici G, Pirotti F, Papale D (2021)
Satellite open data to monitor forest damage caused by extreme climate-induced events: a case study of the Vaia storm in Northern Italy. Forestry 94 (3): 407-416.
CrossRef | Gscholar
(31)
Luti T, De Fioravante P, Marinosci I, Strollo A, Riitano N, Falanga V, Mariani L, Congedo L, Munafò M (2021)
Land consumption monitoring with SAR data and multispectral indices. Remote Sensing 13 (8): 1-26.
CrossRef | Gscholar
(32)
Marchetti M, Bertani R, Corona P, Valentini R (2012)
Changes of forest coverage and land uses as assessed by the inventory of land uses in Italy. Forest@ - Journal of Silviculture and Forest Ecology 9 (4): 170-184. [in Italian with English summary]
CrossRef | Gscholar
(33)
Mather AS (2000)
Afforestation: progress, trends and policies. In: Proceedings of the “Scientific Symposium”. Freiburg (Germany) 26-17 February 2000. European Forest Institute Proceedings no. 35, pp. 11-19.
Gscholar
(34)
Munafò M (2018)
Territorio: processi e trasformazioni in Italia [Territory: processes and transformations in Italy]. Report 296/2018, Istituto Superiore per la Protezione e la ricerca Ambientale (ISPRA), Sistema Nazionale per la Protezione dell’Ambiente (SNPA), Rome, Itay, pp. 88. [in Italian]
Gscholar
(35)
Nguyen HTT, Doan TM, Tomppo E, McRoberts RE (2020)
Land use / land cover mapping using multitemporal Sentinel-2 imagery and four classification. Remote Sensing 12: 1-27.
Gscholar
(36)
Nicodemus KK (2011)
Letter to the editor: on the stability and ranking of predictors from random forest variable importance measures. Briefings in Bioinformatics 12 (4): 369-373.
CrossRef | Gscholar
(37)
Palmero-Iniesta M, Espelta JM, Gordillo J, Pino J (2020)
Changes in forest landscape patterns resulting from recent afforestation in Europe (1990-2012): defragmentation of pre-existing forest versus new patch proliferation. Annals of Forest Science 77 (2): 971.
CrossRef | Gscholar
(38)
Persson M, Lindberg E, Reese H (2018)
Tree species classification with multi-temporal Sentinel-2 data. Remote Sensing 10 (11): 1-17.
CrossRef | Gscholar
(39)
Qiu B, Zou F, Chen C, Tang Z, Zhong J, Yan X (2018)
Automatic mapping afforestation, cropland reclamation and variations in cropping intensity in central east China during 2001-2016. Ecological Indicators 91: 490-502.
CrossRef | Gscholar
(40)
RAF (2019)
RaFITALIA 2017-2018. Rapporto sullo stato delle foreste e del settore forestale in Italia [Report on the state of forests and the forestry sector in Italy]. Compagnia delle Foreste, Arezzo, Italy, pp. 284. [in Italian]
Gscholar
(41)
Raha AK, Mishra AV, Das S, Zaman S, Ghatak S, Bhattacharjeef S, Raha S, Mitra A (2010)
Time series analysis of forest and tree cover of West Bengal from 1988 to 2010, using RS/GIS, for monitoring afforestation programmes. The Journal of Ecology 108: 255-265.
Gscholar
(42)
Sallustio L, Munafò M, Riitano N, Lasserre B, Fattorini L, Marchetti M (2016)
Integration of land use and land cover inventories for landscape management and planning in Italy. Environmental Monitoring and Assessment 188 (1): 1-20.
CrossRef | Gscholar
(43)
Schmidt G, Jenkerson C, Masek J, Vermote E, Gao F (2013)
Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description. Report 2013-1057, USGS Science for a Changing World, US Geological Survey, Washington, DC, USA, pp. 17.
Gscholar
(44)
Shen W, Li M, Huang C, Tao X, Li S, Wei A (2019)
Mapping annual forest change due to afforestation in Guangdong Province of China using active and passive remote sensing data. Remote Sensing 11 (5): 1-21.
CrossRef | Gscholar
(45)
Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa R, Lucas R (2014)
New global forest/non-forest maps from ALOS PALSAR data (2007-2010). Remote Sensing of Environment 155: 13-31.
CrossRef | Gscholar
(46)
Smiraglia D, Filipponi F, Mandrone S, Tornato A, Taramelli A (2020)
Agreement index for burned area mapping: integration of multiple spectral indices using Sentinel-2 satellite images. Remote Sensing 12 (11): 1862.
CrossRef | Gscholar
(47)
Spadoni GL, Cavalli A, Congedo L, Munafò M (2020)
Analysis of Normalized Difference Vegetation Index (NDVI) multi-temporal series for the production of forest cartography. Remote Sensing Applications: Society and Environment 20: 100419.
CrossRef | Gscholar
(48)
Spinsi A, Botarelli L, Marletto V (2012)
Indici vegetazionali da satellite per il monitoraggio in continuo del territorio [Satellite-based vegetation indices for continuous land monitoring]. Italian Journal of Agrometeorology 3: 49-55. [in Italian]
Gscholar
(49)
Townshend JR, Masek JG, Huang C, Vermote EF, Gao F, Channan S, Sexton JO, Feng M, Narasimhan R, Kim D, Song K, Song D, Song XP, Noojipady P, Tan B, Hansen MC, Li M, Wolfe RE (2012)
Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. International Journal of Digital Earth 5 (5): 373-397.
CrossRef | Gscholar
(50)
UN-FCCC (2013)
Afforestation and reforestation projects under the clean development mechanism. A reference manual. United Nations Framework Convention on Climate Change, Bonn, Germany, pp. 3-63.
Gscholar
(51)
White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW (2017)
A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment 194: 303-321.
CrossRef | Gscholar
(52)
Whittaker G, Confesor R, Di Luzio M, Arnold JG (2010)
Detection of overparameterization and overfitting in an automatic calibration of SWAT. Transactions of the ASABE 53 (5): 1487-1499.
CrossRef | Gscholar
 

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