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
According to Food and Agriculture Organization (FAO) definition, the forest is any “territory with arboreal coverage greater than 10% compared to an extension greater than 0.5 ha, where the trees reach a minimum height of 5 m when ripe”, including also: “[…] windbreak barriers and wooded strips of a width exceeding 20m, rubber tree plantations, and cork trees” (
The Italian forest heritage is one of the most important in Europe - 11.8 million ha or 40% of the Italian land (
Forest change processes are territorial transformations of primary importance, and have a central role in the definition of policies aimed to preserve forest resources and its ecosystem services provision. International organizations and other institutions at different levels have undertaken forest changes issues, giving definitions to frame this phenomenon.
The United Nations Food and Agriculture Organization Forestry Department established a series of definitions related to changes between forest and other land use classes, including the loss of forest cover. According to these definitions, it is possible to distinguish two different processes of tree cover growth: (i)
Forest changes occur heterogeneously around the globe, mainly because of different socio-economic backgrounds. While in Africa and South America and Eastern Europe deforestation trends up, in the European Mediterranean areas afforestation, generally resulting from agricultural to forest land-use changes, represents broadly the dominant process (
For the above reason, afforestation represents a process of increasing importance, which certainly needs to be characterized to make the most of its potential and to correctly drive its development in line with forest sustainable management principles. As on one hand, afforestation certainly involves all the benefits connected to forest ecosystem services, on the other hand, if not well managed, it can cause land fragmentation and other related issues (
Since the afforestation process has a significant effect on the land cover dynamics, it is necessary to monitor its evolution, and to this purpose many different independent data in national and European environments are available, both inventory and cartographic. One of the most important is the Land Use Inventory of Italy (IUTI), an inventory data based on a sampling system and the classification in 6 macro-categories (
In this context, Remote Sensing (RS) certainly represents a high potential instrument (
Information about land cover characteristics can be evaluated using the different bands of multispectral data, which can be summarised in single values, called Spectral Indices, or, when they are related to vegetated land cover, Spectral Vegetation Indices (SVI -
Landsat and Sentinel images have different applications related to their intrinsic characteristics. Landsat data are available since the 1970s, thus providing very long time data series at medium spatial resolution (30 m) which are useful to analyse land cover trends and evolution. Instead, Sentinel is available since 2015, providing images with high spatial resolution (10 meters) and short revisiting time (in Italy 2/3 days), which make it a tool of great utility for applications where high precision is needed. Sentinel-1 data and Sentinel-2 images, thanks to their spatial and temporal resolutions, have been recently used in many studies on the analysis of forest disturbances and land cover change detection. In
Other research activities have taken advantage of the high temporal resolution of some satellite images to monitor land cover and land cover changes at the annual or sub-annual level.
The present research aims to illustrate a new methodology that exploits the Landsat time series to demonstrate that RS can provide valuable support not only for monitoring stable land cover classes (
In the first section of this paper, we describe the training data and Landsat images (1984-2020). Then we explain the use of the input data to assess the temporal trend difference of photosynthetic activity of the
In the last part of the paper, we discuss the results and how they support the use of remote sensing data in the distinction between
We selected sixty-one areas distributed along the Italian peninsula, especially on the mountains range (Alps and Appennines) where afforestation occurred in the period 1988-2020. Forty-two sites are located in areas with elevation higher than 600 m a.s.l., and twenty-six of them are located in areas above 1000 m a.s.l; the slope of almost 3/4 of the points is higher than 10% (
Land Use Inventory of Italy IUTI was realized by the Italian Ministry of Environment and Protection of Land and Sea in 1990 (updated in 2000, 2008, 2013, and 2016) in the framework of the Extraordinary Plan of Environmental Remote Sensing. It is composed of 1,217,032 randomly selected points, which have been classified through photointerpretation, considering a minimum mapping unit of 5000 m2 and a minimum width of 20 m. The classification system is based on the Intergovernal Panel on Climate Change guidelines, and it is composed of three hierarchical levels, which aim to identify the land cover categories which are important for the Kyoto Protocol and to integrate the National Inventory on Forest and Carbon Pools (INFC) results on woods and other woody areas categories, based on the FAO definition (
In this study, the Landsat surface reflectance data available (for Landsat mission 5-7-8) in the Google Earth Engine® (GEE) archive were used. We used Landsat missions 5, 7 and 8, because the product of the previous missions had a lower spatial and temporal resolution. Moreover, we analysed data starting from 1985 because they are comparable with the training dataset. Landsat images are composed of six bands: three visible (blue, green, and red), one near-infrared (nir), and two short-wave infrared (swir1 and swir2). All of them are already processed to orthorectify surface and brightness (the thermal infrared) reflectance, atmospherically corrected, and cloud masked. Landsat images were used to create Italian annual composites from 1984 to 2020, from June 1st until August 31st, using the Best Available Pixel (BAP) procedure (
The BAP aims to fill the final image mosaic with the composite best available pixel surface reflectance value. The selection of the best pixel of the images collection takes into account four different criteria: (i) sensor score, to penalize Landsat 7 images where the Scan Line Corrector malfunction (SLC-off) is present; (ii) target day score, to preferably select the images acquired close to a defined acquisition day (in this case 15th of August); (iii) distance to cloud/cloud shadow score, to decrease the scores of those pixels which are in the proximity the cloud cover; (iv) opacity score (calculated using opacity band produced by LEDAPS -
The methodology presented here employed Landsat images to define the feasibility of the assessment of afforestation areas in the last thirty-six years through remote sensing. The analysis of many years allows verifying the evolution of the
Using 61 training areas consisting of polygons defined using IUTI elements, orthophotos and very high resolution images to identify the afforestation, a preliminary analysis of photosynthetic activity in
The orthophotos used for the photointerpretation process are available as Web Map Services (WMS) of the Italian Ministry of Environment, Land and Sea, and they are referred to 1988-1989, 1994-1998 (black and white) and to 2000, 2006, 2012 (colour), with a spatial resolution of 50 cm. The more recent Very High Resolution images (30 cm spatial resolution, available since early 2000s) are freely available thanks to the web service of QGIS and Google Earth Pro®.
The reference dataset (
Orthophotos and IUTI datasets were used to define the temporal range in which the afforestation process occurred. More specifically, two years were identified: (i) the year corresponding to the transition between
The indices calculation for each composite BAP and the analysis of the resulting temporal series were carried out to calculate the temporal statistics
For each polygon and for each of the
To classify
Max features and max depth hyperparameters were calibrated using the procedure named random search (
Cross Validation serves to train and validate the model on different data and it is useful to reduce the overfitting (
Although the
To identify temporal predictors that mostly contributed to increasing the accuracy of the model we analysed the variable importance ranking outputted by random forest. The variable importance was expressed in terms of the Mean Decrease of the Gini index (MDG -
Random forest validation using Out-Of-Bag data (OAoob) was performed on all the temporal predictors of the best iteration of 5-fold CV (
For each class of the four classification models, omission (omissionsoob) and commission (commissionsoob) have been calculated (
The large random forest OAoob values were reached thanks to the use of the hyperparameters with the largest OAcv, calculated using random search and 5-fold CV on the classification models, to identify the couple of hyperparameters which allow to reach the best results.
The importance ranking of temporal predictors is illustrated in
Afforestation monitoring is important because it influences ecosystem services but it can also cause some issues, as land fragmentation and habitat loss. In this research we aimed to demonstrate the efficacy of remote sensing data in the assessment of the difference in photosynthetic activity and in the automatic classification of
The methodology by using Landsat images allows analysing long time series of images, which is essential to assess forest evolution, which is generally slow. Moreover, both the first analysis on temporal predictors, and the random forest algorithm are easily replicable, also using different input images. To limit the overfitting due to the use of neighbouring pixels as training and validation data the data were elaborated at polygon level, considering the training data boundaries. Furthermore, random search and
The analysis of the temporal predictors importance allowed us to identify those variables which were more significant to separate the three classification periods. The median value of the green and the blue bands was part of the main predictors in almost every distinction considered; this probably concerned the fact that the blue band is associated with soil moisture, while the green band is associated with the reflectance value of the vegetation: the different water content and the different reflectance value are therefore two important factors to examine to classify
These are important information to set the best combination of parameters to obtain the maximum accuracy in the distinction of
There are few studies on the use of remote sensing to assess afforestation areas, and most of them evaluate the forest gain by comparing different land cover maps. For example,
One of the limits of the method proposed is the medium spatial resolution of images: in fact, Landsat images resolution is 30 meters, and this could cause the omission of the smaller elements. This could be overcome by integrating other data, like radar, lidar, or other multispectral images. Another issue could occur by increasing the area of study outside the training areas; in fact, it will be necessary to consider potential commission or omission errors which could be due for example to the orography, which could reduce the accuracy because of slope and shadows.
The results of the present research offer the possibility to deepen the existing analysis approaches, directly highlighting the afforestation, a land cover class which is not stable through time, but represent a transition from non-forest to forest cover. Furthermore, it will be possible to elaborate an afforestation areas map, at national or European level. The new cartographic data could satisfy the requirements proposed by European legislation for monitoring activities, being aligned to the EIONET Action Group on Land monitoring in Europe (EAGLE) Concept. EAGLE is based on a classification system which considers the distinction between land cover and land use and it is composed of three descriptors (land cover components, land use attributes and further characteristics), combined to define a classification system suitable to be integrated to existing classes, maintaining the three components independent (
The afforestation area assessment can be important to analyze the forest growth trend, allowing to make forecasts on the forest evolution, both in quantitative and qualitative terms. Moreover, the integration between the map and other data will allow to study the evolution of the forest areas to assess the forest ecosystem services.
Conceptualization: SF, AC, MMa, GSM, GC, MMu; methodology: SF; statistical analysis and results elaborations: AC and SF; writing-original draft preparation: SF, AC, VF, GC, GLS, CC, LC, MMa, GC, GSM, MMu. All authors have read and agreed to the published version of the manuscript.
Study area and distribution of reference dataset. On the right, an example of afforestation occurred between 1990-2000 is displayed: in the upper image (1988) there was no forest cover, in the centre (2000) the forest partially cover the area, in the last image (2019) the forest cover is complete.
Workflow of the applied methodology. The steps to distinguish among
Example of NDVI series in an afforestation pixel. For each classification period the trend line was obtained and used to calculate the Pearson’s correlation coefficient (
Five-fold cross validation overall accuracy (Oacv). The value “max” is referred to the best iteration. Each OAcv of every couple of hyperparameters for each classification models is displayed, grouped by accuracy.
Importance ranking of temporal predictors. The importance value (Mean Decrease Gini) of each temporal predictors in every classification model is reported.
Density distribution plots. The most important temporal predictors of
Classification models overall accuracy of random forest (OAoob), considering all the temporal predictors.
Classification models | max features | max depth | OAoob |
---|---|---|---|
non-forest/afforestation | 24 | 21 | 0.70 |
afforestation/forest | 7 | 5 | 0.80 |
non-forest/forest | 52 | 30 | 0.78 |
non-forest/afforestation/forest | 24 | 28 | 0.67 |
Omission and commission error for each classification model.
ErrorType (%) | Classification model | non-forest | afforestation | forest |
---|---|---|---|---|
Omissionsoob | non-forest / afforestation | 26.2 | 34.4 | - |
afforestation / forest | - | 27.9 | 11.5 | |
non-forest / forest | 23.0 | - | 21.3 | |
non-forest / afforestation / forest | 36.1 | 39.3 | 23.0 | |
Commissionsoob | non-forest / afforestation | 31.8 | 28.6 | - |
afforestation / forest | - | 13.7 | 23.9 | |
non-forest / forest | 21.7 | - | 22.6 | |
non-forest / afforestation / forest | 40.0 | 32.7 | 25.4 |