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-ofbag 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.

Afforestation monitoring through automatic analysis of 36-years Landsat Best Available Composites / Alice Cavalli, Saverio Francini, Giulia Cecili, Claudia Cocozza, Luca Congedo, Valentina Falanga, Gian Luca Spadoni, Mauro Maesano, Michele Munafò, Gherardo Chirici, Giuseppe Scarascia Mugnozza. - In: IFOREST. - ISSN 1971-7458. - ELETTRONICO. - 15:(2022), pp. 220-228.

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

Saverio Francini
;
Claudia Cocozza;Gherardo Chirici;
2022

Abstract

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-ofbag 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.
15
220
228
Alice Cavalli, Saverio Francini, Giulia Cecili, Claudia Cocozza, Luca Congedo, Valentina Falanga, Gian Luca Spadoni, Mauro Maesano, Michele Munafò, Gherardo Chirici, Giuseppe Scarascia Mugnozza
File in questo prodotto:
File Dimensione Formato  
2022_Cavalli_iForest.pdf

accesso aperto

Tipologia: Pdf editoriale (Version of record)
Licenza: Creative commons
Dimensione 1.2 MB
Formato Adobe PDF
1.2 MB Adobe PDF Visualizza/Apri

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1277179
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact