Understanding Land Changes for Sustainable Environmental Management: The Case of Basilicata Region (Southern Italy)

Abstract

Sustainable land management is one of the key actions for the achievement of objectives set by the 2030 Agenda for Sustainable Development. In particular, land represents a fundamental resource to address issues of climate change, biodiversity preservation, maintaining ecosystem services, and at the same time ensuring shared prosperity and well-being. Therefore, it is necessary to activate strategies to monitor changes in land use and land cover in order to evaluate strategies for proper management. To do this, the new open source geospatial analysis tools and the increasing availability of remote sensed open data can allow the activation of methodologies for monitoring changes in land use and land cover in order to provide data usable in other research areas or, for example, to implement a decision support system for environmental sustainability. In this study, a GIS approach based on open remote sensing data has been used to perform a spatial analysis of land cover changes within the Basilicata region (Southern Italy) that is spatially expeditious yet accurate. The results showed a very evident land transformation with important repercussions on the environmental components. The ease of use of techniques makes this methodology replicable in other territory and can be used as a preliminary approach to sustainable development model.

1. Introduction

Sustainable Development Goal n.15 of the 2030 Agenda aims to “protect, restore, and promote the sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, halt and reverse land degradation, and halt biodiversity loss.” [1]. Integrated planning and management of land resources is an issue that had already emerged in Chapter 10 of Agenda 21 [2], which addresses the cross-cutting aspects of decision making for the sustainable use and development of natural resources, including soils, minerals, water, and the life that the earth comprises. This broad integrative view of land resources, which are essential to the life-support systems and productive capacity of the environment [3,4], underlies the discussions of Member States expressing the challenges posed to sustainable development by land consumption, desertification, and drought [5,6,7]. In this context, it is essential to activate all actions to monitor the land changes in order to prevent phenomena such as land degradation, which in many areas of the planet earth and in particular in the Mediterranean region, is causing enormous environmental and social impacts [8]. Therefore, for a sustainable environmental planning that allows land to become a renewable resource and to fight land consumption and degradation, it is important to use a multidisciplinary and holistic approach in which one of the preliminary analyses is based on the assessment of land system dynamics [9].

Land use (LU) and land cover (LC) data are powerful resources for various surveys and investigations, such as measuring physical and environmental impacts, setting well-being indices, assessing human impacts, monitoring habitats and their ecosystem ser-vices, landscape changes, etc. [10]. Updated data on land characterization and its distribution are essential for an efficient management of agricultural and forestry resources because their relationship contributes to identifying the link between the physical and socio-economic environment. In fact, LULC data are fundamental in environmental studies, decision making, land use planning and design, and the definition of natural resource management policies. These land cover data can be used as inputs to a variety of models (meteorological, ecological, biochemical, economic, hydrological, etc.) to monitor biodiversity, run climate models, and assess environmental changes such as deforestation, desertification, and urban sprawl [11]. In addition, LC maps are one of the most powerful geospatial products that can measure the 17 indicators of the Sustainable Development Goals (SDGs), defined in the 2030 Agenda for Sustainable Development [1,12].

However, all types of land cover and land use data sources have positive and negative aspects, so it is important for those involved in land and environmental studies to be aware of all the characteristics in order to find ways to integrate the positive and minimize errors [13,14]. Currently, the concession and use of these data has passed from a system of payment or temporary consultation with public bodies, to a system almost exclusively free and/or open. In particular, the current context confirms that openness has changed the way geospatial data are collected, processed, analysed, and visualized, and this has also had a positive impact on spatial and landscape analysis [15].

Free satellite imagery, such as Landsat, MODIS, or Sentinel, is increasingly used also because it offers a wide range of options and possible applications; from integrated spatial studies [16,17] to air quality assessment [18]. In addition, consistently providing imagery with the same spatial and temporal resolution ensures highly relevant standard processing in spatial and ecological studies [19]. However, the use of these data and methodologies is often limited to the academic or national domain and little is done at the local level. This happens because there is a lack of technical expertise but also because the methodologies are often too complex and time consuming to be exploited practically in regional or local planning [20].

In this context, an approach based on freely available online remotely sensed data and open source tools was applied to a case study at regional scale to demonstrate the usefulness, speed, and ease of use of these methodologies for the quantitative assessment of land cover changes that occurred over a nearly 20-year time frame.

Using some datasets that are freely available online (MODIS Land Cover Products), analyses on the transformations occurred in Basilicata Region (Southern Italy) in the period between 2001 and 2019 were carried out in order to evaluate the usability of these data for a continuous land monitoring. Moreover, the purpose of this manuscript is to verify the ease of application of these open source and open data techniques in order to transfer them outside of the research environment for practical planning aims. Finally, in terms of the methodology, having been applied to a study area with different environmental problems, the work also wants to define the specific land dynamics in an area with different environmental problems. Indeed, the environmental balance of Basilicata is fragile and highly vulnerable to perturbations; consequently, natural ecosystems are expected to be more sensitive to changes currently affecting the entire Mediterranean basin.

2. Materials and Methods

2.1. Basilicata Region Study Area

The Basilicata region (Southern Italy) covers an area of 1,007,332 ha (Figure 1). It presents a territory predominantly mountainous and hilly, and only a small portion has flat areas. This has influenced the orography as it is extremely varied.

The whole region is part of the Mediterranean biogeographic area and there is a climatic variability depending by the extreme orographic and morphological diversity of the territory which varies enormously even in a few kilometers. In fact, the south-western area has an oceanic/sub-oceanic climatic profile with precipitation values around 1500 mm of annual rainfall, which decreases almost to 300 mm going towards the internal and eastern areas that have continental profiles with areas classified as sub-arid.

The Basilicata Region land in recent decades has undergone major transformations due mainly to land degradation [21], land abandonment [22], and increased oil production [23]. Moreover, the demographic trend shows an aging and shrinking of the population, with emigration of the workforce, as well as intra-regional migration to more urbanized areas [24].

The high land diversity and the low anthropic pressure (except in a few industrial areas) make the Basilicata region a hotspot for Mediterranean biodiversity which, however, needs new programming and planning strategies according to more stringent criteria of sustainability to make the territory more resilient.

2.2. MODIS Land Cover Database

MODIS is NASA’s first mission to provide daily global coverage at a variable resolution starting at 250 m. The MODIS Moderate-resolution Imaging Spectroradiometer acronym consists of two satellites Earth and Water. The satellites are composed of a 36-band multispectral sensor and provide images of the entire Earth’s surface every 1 and 2 days at different spatial resolutions 250 m, 500 m, 1 km, depending on the type of processing required and the level of correction needed. The two satellites Earth and Water were launched in December 1999 and May 2002 respectively. The spectral resolution is between 0.4 and 14.385 µm.

For this analysis the product “MODIS Land Cover Type Combined” Earth + Water collection V6 was used, for the expeditious monitoring of land cover at a global scale with annual observation time intervals from 2001 to 2019 and with spatial resolution of about 500 m [25,26]. MODIS land cover products are a dataset widely used in the scientific community for several kinds of investigations [27,28].

The overall accuracy of the product is about 75% correctly classified, but the range in category-specific accuracies is great [26]. Within the product there are multiple layers, each describing the classification of land cover and land use according to different methodological schemes. In particular, for this work the FAO (Food and Agriculture Organization) Land Cover Classification System land cover (LCCS1) and FAO Land Cover Classification System land use (LCCS2) classifications have been used.

The LCCS was defined to globally standardize land cover and land use classes. It is based on supervised classification and the use of ancillary data for post classification. In

Table 1. Legend FAO LCCS Classification 1—Land Cover as reported in [25].

Category	Description
Barren	At least 60% of area is non-vegetated barren (sand, rock, soil) or permanent snow/ice with less than 10% vegetation.
Permanent Snow and Ice	At least 60% of area is covered by snow and ice for at least 10 months of the year.
Water Bodies	At least 60% of area is covered by permanent water bodies.
Evergreen Needleleaf Forests	Dominated by evergreen conifer trees (>2 m). Tree cover > 60%.
Evergreen Broadleaf Forests	Dominated by evergreen broadleaf and palmate trees (>2 m). Tree cover > 60%.
Deciduous Needleleaf Forests	Dominated by deciduous needleleaf (larch) trees (>2 m). Tree cover > 60%.
Deciduous Broadleaf Forests	Dominated by deciduous broadleaf trees (>2 m). Tree cover > 60%.
Mixed Broadleaf/Needleleaf Forests	Co-dominated (40–60%) by broadleaf deciduous and evergreen needleleaf tree (>2 m) types. Tree cover > 60%.
Mixed Broadleaf Evergreen/Deciduous Forests	Co-dominated (40–60%) by broadleaf evergreen and deciduous tree (>2 m) types.
>60%.
Open Forests	Tree cover 30–60% (canopy >2 m).
Sparse Forests	Tree cover 10–30% (canopy >2 m).
Dense Herbaceous	Dominated by herbaceous annuals (<2 m) at least 60% cover.
Sparse Herbaceous	Dominated by herbaceous annuals (<2 m) 10–60% cover.
Dense Shrublands	Dominated by woody perennials (1–2 m) >60% cover.
Shrubland/Grassland Mosaics	Dominated by woody perennials (1–2 m) 10–60% cover with dense herbaceous annual understory.
Sparse Shrublands	Dominated by woody perennials (1–2 m) 10–60% cover with minimal herbaceous understory.
Unclassified	Has not received a map label because of missing inputs.

, the legend for the LCCS1 top-level classification that identifies land cover is reported and in

Table 2. Legend FAO LCCS Classification 2—Land Use as reported in [25].

Name	Description
Barren	At least 60% of area is non-vegetated barren (sand, rock, soil) or permanent snow/ice with less than 10% vegetation.
Permanent Snow and Ice	At least 60% of area is covered by snow and ice for at least 10 months of the year.
Water Bodies	At least 60% of area is covered by permanent water bodies.
Urban and Built-up Lands	At least 30% of area is made up of impervious sur- faces including building materials, asphalt, and vehicles.
Dense Forests	Tree cover > 60% (canopy > 2 m).
Open Forests	Tree cover 10–60% (canopy > 2 m).
Forest/Cropland Mosaics	Mosaics of small-scale cultivation 40–60% with
	>10% natural tree cover.
Natural Herbaceous	Dominated by herbaceous annuals (<2 m).
Natural Herbaceous/Croplands Mo- saics	Mosaics of small-scale cultivation 40–60% with natural shrub or herbaceous vegetation.
Herbaceous Croplands	Dominated by herbaceous annuals (<2 m).
Shrublands	Shrub cover >60% (1–2 m).
Unclassified	Has not received a map label because of missing inputs.

the legend for the land use classification resulting from the top-level analysis is reported. The two classifications, even if they have classes in common, allow for the characterization of the territory in a different way so as to be able to provide all the fundamental aspects for land planning.

2.3. LULC Changes: How to Detect

The “land cover” (LC) and “land use” (LU) terms are frequently improperly used as synonyms [29]. According to FAO [30], land cover is that portion of the globe’s surface defined in space and recognized in terms of characteristics and properties included by features of the biosphere that are reasonably immutable or cyclically predictable. Land use, on the other hand, can be interpreted as the way space is occupied by human society, i.e., related to human activity and directly to the exploitation of land and its use by the population [31].

Thanks to the databases used in this study, it was possible to perform a two-stage transformation assessment immediately by taking advantage of common techniques for evaluating the dynamics of land changes [32]. All operations were performed with the open source software QGIS 3.16 [33].

In the first phase, the transformations of territory in terms of LU and LC have been evaluated through a comparison of the differences in quantitative terms of the surface area associated with each category of the LCCS1 and LCCS2. This first approach has been realized through simple operations: downloading the dataset from the official repository, clipping on administrative boundaries of Basilicata Region, realizing the layout maps, and computing the surfaces in hectares for each class of LU and LC. A year-by-year analysis was carried out in order to identify in more detail the trends of variations within each class. On the other hand, the second phase allows a detailed evaluation of the transformations. Using change detection techniques [34], a quantitative spatial analysis of the LULC data was performed. Using LULC maps from 2001 and 2019, it is possible to map changes and identify precisely how much of a given cover class has transformed into something else.

From a sustainable environmental planning perspective, the two approaches are indispensable and basic to effectively understand how and where LU and LC transformations have occurred.

3. Results

The mapping operations of land use and land cover with MODIS data have allowed for the realization of an expeditious assessment of the dynamics of transformation occurring in the Basilicata Region so as to draw some indications of criticalities useful for sustainable environmental planning.

3.1. Year-to-Year Quantitative Assessment of LULC

The first phase involved the quantitative year-by-year assessment of changes in LULC. The use of geographic tools coupled with an open source spreadsheet allowed one primarily to map and then to export the data in table format and then to graph them.

Table A1. LCCS1 FAO (Food and Agriculture Organization)- Land Cover Classification System land cover from 2001 to 2009 years. Values expressed in hectares.

LCCS1 FAO Land Cover Classification	2001	2002	2003	2004	2005	2006	2007	2008	2009
Barren	112.68	94.2	74.84	37	55.48	55.48	55.48	55.48	37
Water Bodies	948.32	948.32	1359.4	1472.96	1529.28	1548.64	1492.28	1416.6	1472.92
Evergreen Needleleaf Forests	1954.52	2100.72	2416.2	2527.72	2547.08	2750.48	3008.48	3174.04	3438
Evergreen Needleleaf Forests	578.92	559.56	502.36	546.56	528.92	621.36	639	733.2	770.16
Deciduous Broadleaf Forests	58,543.2	62,770	64,121	66,232.8	66,616	67,664.88	66,682.28	68,883.44	71,472.32
Mixed Broadleaf/Needleleaf Forests	1351.88	1517.4	1498.96	1613.36	1637.4	1618.04	1686.44	1785.28	1955.12
Mixed Broadleaf Evergreen/Deciduous Forests	0	18.48	18.48	18.48	18.48	0	0	0	0
Open Forests	112,531.6	113,215.2	111,717.9	111,412.6	109,913.9	109,603.6	107,447.3	106,280.2	107,327.4
Sparse Forests	200,917.6	205,488.6	210,025.2	208,493.7	215,132.3	218,783.2	225,841.5	235,837.1	246,447.5
Dense Herbaceous	618,775.4	608,930.8	603,828.8	603,152.6	597,643.4	592,977	588,996.4	577,810.6	563,009.5
Sparse Herbaceous	2541.88	2626.48	2661.48	2753.04	2656.24	2655.68	2391.6	2338.72	2509.24
Dense Shrublands	19.36	19.36	124.48	124.48	124.48	124.48	162.32	126.2	58.04
Shrubland/Grassland Mosaics	0	0	0	0	0	0	0	0	0
Sparse Shrublands	352.28	338.32	278.32	242.2	224.56	224.56	224.44	186.6	130.28
Total ha	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5

and

Table A2. LCCS1 FAO Land Cover Classification from 2010 to 2019. Values expressed in hectares.

LCCS1 FAO Land Cover Classification	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Barren	37	37	0	0	0	0	0	72.24	36.96	18.12
Water Bodies	1546.88	1546.88	1546.88	1546.88	1565.4	1546.04	1546.04	1417.48	1360.28	1398.12
Evergreen Needleleaf Forests	3480.12	3402.72	3290.2	3721.84	3922.04	3939.72	4219.6	3240	3310.52	2857.68
Evergreen Needleleaf Forests	915.44	933.08	861.68	857.72	819.04	966.96	1044.4	725.48	804.04	737.48
Deciduous Broadleaf Forests	73,946.84	74,658.72	75,213.92	78,732	80,920.08	83,260.76	84,433.44	75,394.12	82,626.64	84,858.88
Mixed Broadleaf/Needleleaf Forests	2138.28	2157.6	2176.92	2325.68	2399.64	2437.48	2531.68	2587.16	2817.68	3043
Mixed Broadleaf Evergreen/Deciduous Forests	0	0	0	0	0	0	0	0	0	0
Open Forests	108,107.7	108,750.2	106,949.3	107,226.9	107,991	107,975.6	110,473	113,571.2	123,155.4	115,352.8
Sparse Forests	252,090.5	255,866.2	257,172.9	258,011.4	261,518.9	271,047.5	274,272.2	287,065.6	280,804.2	280,479.8
Dense Herbaceous	553,835.1	548,699.3	548,881.2	543,763.8	537,078.1	525,169.8	517,828	512,387.7	501,595.2	507,961.2
Sparse Herbaceous	2378.28	2426.12	2385.64	2270.96	2260.64	2117.16	2130.32	2019.44	1948.4	1752.32
Dense Shrublands	39.56	37.84	18.48	22.32	22.32	18.48	18.48	18.48	18.48	18.48
Shrubland/Grassland Mosaics	0	0	0	0	0	0	0	0	0	0
Sparse Shrublands	111.8	111.8	130.28	147.92	130.28	147.92	130.48	128.56	149.64	149.64
Total ha	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5

show the year-by-year data from the LCCS1 FAO Land Cover Classification. Their analysis showed that the Deciduous Broadleaf Forests, Open Forests, Sparse Forests, and Dense Herbaceous classes are those quantitatively most representative of the study area and in which the dynamics of transformation are most evident. Their year-to-year trends are shown in Figure 2.

From the analysis of the data obtained from the classification that concerns the land cover (Figure 3), it is shown in particular that the Dense Herbaceous class, in which a vegetation cover with annual coverage of 60% is dominant, has suffered a substantial loss from 2001 to 2019 of about 110,000 hectares while the Deciduous Broadleaf Forest class is the one that has shown a substantial growth from 2001 to 2019 equal to 26,300 hectares. The Sparse Forests class shows a major increase of almost 80,000 hectares as well.

An identical analysis was performed for the land use assessment based on LCCS2. MODIS data were tabulated for all years from 2001 to 2019 (

Table A3. LCCS2 FAO Land use Classification from 2001 to 2009. Values expressed in hectares.

LCCS2 Class FAO Land Use Classification	2001	2002	2003	2004	2005	2006	2007	2008	2009
Barren	112.68	94.2	74.84	37	55.48	55.48	55.48	55.48	37
Water Bodies	948.32	948.32	1359.4	1472.96	1529.28	1548.64	1492.28	1416.6	1472.92
Urban and Built-up Lands	13,853.84	13,853.84	13,853.84	13,853.84	13,853.84	13,853.84	13,853.84	13,853.84	13,853.84
Dense Forests	62,428.52	66,966.16	68,557	70,938.92	71,347.88	72,637.12	71,998.56	74,558.32	77,617.96
Open Forests	304,700.2	309,294.2	311,948.8	309,318	313,914.3	316,053	319,913.8	327,386.6	337,947.8
Forest/Cropland Mosaics	1816.48	2061.24	2276.36	2998.96	3487.92	4690.84	5685.92	6778.44	7689.12
Natural Herbaceous	114,761.6	119,396.6	127,987.3	127,535	133,435.8	133,776.4	131,611.3	127,017.6	123,119.7
Herbaceous Croplands	499,838.8	485,859.4	472,335.2	472,274.2	460,804.4	455,813.6	453,780.1	447,360.5	436,757.2
Shrublands	167.4	153.44	234.68	198.56	198.56	198.56	236.28	200.16	132
Total ha	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5

and

Table A4. LCCS2 FAO Land use Classification from 2010 to 2019. Values expressed in hectares.

Class FAO Land Use Classification	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Barren	37	37	0	0	0	0	0	72.24	36.96	18.12
Water Bodies	1546.88	1546.88	1546.88	1546.88	1565.4	1546.04	1546.04	1417.48	1360.28	1398.12
Urban and Built-up Lands	13,853.84	13,853.84	13,873.2	13,892.56	13,892.56	13,892.56	13,892.56	13,892.56	13,911.04	13,911.04
Dense Forests	80,463.04	81,134.48	81,525.08	85,619.6	88,043.16	90,587.28	92,211.48	81,929.12	89,541.24	91,479.4
Open Forests	343,136.5	346,825.2	345,448.2	345,399.5	348,816.4	356,910	360,547.4	376,516.6	373,563.3	364,824.2
Forest/Cropland Mosaics	8701.08	9336.56	10,219.4	11,327.92	12,086.68	13,172.68	15,039.88	14,506.24	20,847.52	21,572.16
Natural Herbaceous	121,777.4	123,534.9	125,697.4	123,953.8	121,984.6	120,098.7	115,763.8	119,012.9	107,851.6	104,483.6
Herbaceous Croplands	428,998.2	422,246.8	420,206.3	416,772.5	412,123.9	402,309.2	399,515.4	391,188.8	391,386.1	400,829.9
Shrublands	113.52	111.8	110.92	114.76	114.76	110.92	110.92	91.56	129.4	110.92
Total ha	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5	998,627.5

). Land use classification allows for an analysis that is equally useful for sustainable planning purposes as it considers the presence of anthropic activities on the land. Analyzing the graphs shown in Figure 4 and Figure 5, the decrease of the class Herbaceous Cropland class, which identifies cultivated areas with an area lost of about 100,000 hectares in the last 20 years and a growth of the forested areas identified by the Dense Forests and Open Forests classes, is immediately evident. In addition, an increase in areas classified as Urban and Built-Up should be noted, demonstrating a land consumption that should be evaluated in more detail.

Both analyses show that some classes, for some years, are not present in terms of area. This is due to the classification algorithms used that, for those years, did not find some classes.

3.2. LULC 2001–2019 Dynamics Mapping

As reported earlier, a mapping operation was performed for each annual MODIS dataset from LCCS1 and LCCS2, resulting in maps such as those shown in Figure 6.

Using the raw raster map allowed for a change detection to spatialize the transformations. This operation is critical to understanding trajectories of change, i.e., identifying how different LULC categories have evolved over the years (Figure 7 and Figure 8).

These data, even if very complex, can be aggregated and used to calculate changes (

Table A5. LCCS1 FAO Land Cover Classification for dinamiche nel from 2001 to 2009. Values expressed in hectares.

LCCS1 Dynamics	Hectares (Ha)
No changes	790,339.836
From Barren to Water Bodies	19.369
From Barren to Sparse Forests	37.853
From Barren to Dense Herbaceous	18.491
From Barren to Sparse Herbaceous	37.015
From Water Bodies to Dense Herbaceous	18.489
From Evergreen Needleleaf Forests to Evergreen Broadleaf Forests	37.859
From Evergreen Needleleaf Forests to Deciduous Broadleaf Forests	17.654
From Evergreen Needleleaf Forests to Mixed Broadleaf/Needleleaf Forests	241.335
From Evergreen Needleleaf Forests to Open Forests	150.07
From Evergreen Needleleaf Forests to Dense Herbaceous	205.226
From Evergreen Broadleaf Forests to Evergreen Needleleaf Forests	90.768
From Evergreen Broadleaf Forests to Mixed Broadleaf/Needleleaf Forests	27.215
From Evergreen Broadleaf Forests to Dense Herbaceous	93.361
From Deciduous Broadleaf Forests to Evergreen Needleleaf Forests	18.495
From Deciduous Broadleaf Forests to Mixed Broadleaf/Needleleaf Forests	524.909
From Deciduous Broadleaf Forests to Open Forests	1823.329
From Mixed Broadleaf/Needleleaf Forests to Deciduous Broadleaf Forests	167.392
From Mixed Broadleaf/Needleleaf Forests to Open Forests	49.958
From Open Forests to Evergreen Needleleaf Forests	821.006
From Open Forests to Evergreen Broadleaf Forests	91.667
From Open Forests to Deciduous Broadleaf Forests	24,327.13
From Open Forests to Mixed Broadleaf/Needleleaf Forests	983.941
From Open Forests to Sparse Forests	9910.89
From Open Forests to Dense Herbaceous	32.503
From Open Forests to Sparse Herbaceous	5.404
From Sparse Forests to Evergreen Needleleaf Forests	367.452
From Sparse Forests to Evergreen Broadleaf Forests	166.524
From Sparse Forests to Deciduous Broadleaf Forests	3762.949
From Sparse Forests to Mixed Broadleaf/Needleleaf Forests	131.255
From Sparse Forests to Open Forests	25,988.271
From Sparse Forests to Dense Herbaceous	12,582.242
From Sparse Forests to Sparse Herbaceous	161.467
From Sparse Forests to Dense Shrublands	18.495
From Dense Herbaceous to Water Bodies	111.862
From Dense Herbaceous to Evergreen Needleleaf Forests	258.088
From Dense Herbaceous to Evergreen Broadleaf Forests	73.948
From Dense Herbaceous to Deciduous Broadleaf Forests	425.624
From Dense Herbaceous to Open Forests	10,963.766
From Dense Herbaceous to Sparse Forests	111,841.977
From Dense Herbaceous to Sparse Herbaceous	425.434
From Sparse Herbaceous to Barren	18.131
From Sparse Herbaceous to Water Bodies	337.298
From Sparse Herbaceous to Sparse Forests	837.554
From Sparse Herbaceous to Dense Herbaceous	244.87
From Sparse Herbaceous to Sparse Shrublands	18.488
From Dense Shrublands to Open Forests	19.372
From Dense Shrublands to Sparse Forests	147.987
From Dense Shrublands to Dense Herbaceous	36.142
From Dense Shrublands to Sparse Herbaceous	37.102
Total ha	999,067.5

and

Table A6. LCCS2 FAO Land Use Classification dynamic from 2001 to 2009. Values expressed in hectares.

LCCS2 Dynamics	Hectares (Ha)
No changes	769,206.8
From Barren to Water Bodies	19.368
From Barren to Open Forests	37.85
From Barren to Natural Herbaceous	55.502
From Natural Herbaceous to Water Bodies	930.211
From Dense Forests to Open Forests	2023.931
From Dense Forests to Natural Herbaceous	298.566
From Open Forests to Dense Forests	30,633.88
From Open Forests to Forest/Cropland Mosaics	3906.066
From Open Forests to Natural Herbaceous	5485.824
From Open Forests to Herbaceous Croplands	6672.287
From Open Forests to Shrublands	18.493
From Forest/Cropland Mosaics to Open Forests	633.134
From Forest/Cropland Mosaics to Herbaceous Croplands	73.999
From Natural Herbaceous to Water Bodies	449.126
From Natural Herbaceous to Urban and Built-up Lands	57.222
From Natural Herbaceous to Dense Forests	757.608
From Natural Herbaceous to Open Forests	62,898.49
From Natural Herbaceous to Forest/Cropland Mosaics	727.139
From Natural Herbaceous to Herbaceous Croplands	3952.81
From Natural Herbaceous to Shrublands	18.487
From Herbaceous Croplands to Barren	18.13
From Herbaceous Croplands to Open Forests	40,876.07
From Herbaceous Croplands to Forest/Cropland Mosaics	15,842.99
From Herbaceous Croplands to Natural Herbaceous	52,940.09
From Shrublands to Open Forests	93.362
From Shrublands to Natural Herbaceous	0.12
TOT	998,627.5

). Moreover, they are a source of ancillary geodata that can be exploited for multidisciplinary surveys in a perspective of integrated spatial planning. All operations are easily implemented within common open source GIS applications without excessive time consumption and high performance tools.

The extrapolated data show the usefulness of both types of LUCL datasets. In fact, although there are similarities, different aspects of land transformation dynamics emerge from their analysis. The areas that have not changed are almost the same in terms of hectares, but from the analysis of LCCS1 we see that the most evident change in percentage terms is that between Dense Herbaceous to Sparse Forests. This information is generic but it is not possible to define how much of this change affected natural grasslands and how much affected cereal agricultural areas. Deepening the analysis with the LCCS2 dataset, it is possible to differentiate between Natural Herbaceous and Herbaceus Cropland classes by evaluating how much of the two classes has been transformed into the different forest classes. Both analyses show that some classes, for some years, are not present in terms of surface. This is due to the classification algorithms used which, for those years, did not find some classes.

4. Discussion

4.1. LULC Changes in Basilicata Region: The Land Abandonment Issue

The main dynamic that emerges from Basilicata region survey is that of abandonment of agricultural areas (mostly arable land and pastures characterized by herbaceous cover) and their subsequent reforestation through natural processes of secondary succession. Agricultural abandonment processes started in the 1950s but especially today we hear more about them as the rates of increase have increased greatly [35]. The causes are multiple in that this phenomenon is extremely complex and is very evident in the mountainous inland areas. The causes can be different and interconnected: abandonment due to low profitability of land, soil conditions unfavorable to agriculture, depopulation, lack of innovation, socio-economic problems at local or regional level, and wrong agricultural policies [36,37]. This phenomenon has several positive and negative impacts, with trade-offs largely dependent on the specific spatial context [38]. One of the typical effects, especially in the Mediterranean context, is that of natural reforestation with secondary positive and negative consequences that need further study and investigation [39].

In Basilicata these processes are currently underway [40] and have also emerged from this study, but compared to other areas it seems to be proceeding faster with evident ecological and socio-economic problems [41].

In this area, as in other similar areas in southern Italy, following an increase in cereal cultivation favored by the “Agrarian Reforms for Southern Italy” and an increase in mechanization, there was an increase in the agricultural area used even in marginal areas with little vocation for cereal cultivation. In the following decades, with the changes in socio-economic conditions, the crises of the agricultural sector that have made economically unfavorable, for many areas, cereal farming and some agrarian reforms related to the Common Agricultural Policy (in particular the “set-aside” REG. CEE 1272/88), there has been a steady abandonment of agricultural activity [42]. These areas give rise to different landscapes in relation to climatic conditions, age of abandonment, management before and after abandonment, and disturbances that are triggered successively [43]. In some areas of Basilicata, the continuous process of abandonment could lead to significant problems of land degradation that should be investigated [44,45].

Finally, the survey shows that abandoned areas are subject to natural renaturalization and afforestation that make the study of territorial dynamics even more intricate because, even for this phenomenon, the impacts can be positive or negative in relation to the territorial context. [46,47]. All this information, obtained in this expeditious manner, can be easily obtained and subsequently incorporated within planning actions of sustainable models aimed at improving the resilience of territory. In this specific study, the abandonment of agricultural areas and the consequent renaturalization of some areas can determine positive or negative impacts related to the consumption of natural resources (soil, water etc.) and the CO₂ balance. Therefore, the spatialization of land use and land cover is the basis for all subsequent analyses.

4.2. How to Use LULC Data in Sustainable Environmental Planning

Appropriate LULC data have a key role in several sectors of planning as for example in natural resource management, food security, hydrology modelling, etc.

The problem of land transformation is one of those research fields that needs LULC geodata that are as up-to-date as possible and at the same time allow a standardized and repeated survey over time [48,49].

This is because the data considered are often raster or vector cartography of different origins, scales, reference systems, etc. [50,51]. In addition, replicating the study, even for the interest of monitoring the phenomenon, becomes problematic since the data used are not available in time and in the same way.

Many of the problems can be solved using a remote sensing approach and online data made freely available according to well-defined standards [52]. This ensures that it is possible to monitor the phenomenon and replicate studies over time and in different spatial contexts. In particular, the open source tools discussed here can be used to support the implementation of appropriate techniques to represent the dynamics of land transformation and at the same time be used for landscape monitoring and planning [16].

In view of this, in this case study the applicability of the MODIS LCSS1 and LCSS2 datasets were verified for the analysis of land transformations both from the point of view of land cover and land use and to evaluate the replicability of use in a perspective of implementation of sustainable environmental planning models.

The MODIS products, which can be freely used, provide an important support for assessments and preliminary quantifications of environmental and territorial transformations. Indeed, different types of data are available for a myriad of different applications [53,54]. This work considers the land cover products classified according to FAO nomenclature which allows for the differentiation of the land use from the land cover. In fact, this aspect is fundamental to understanding the actual transformation of territory not only from a physical point of view but also from a socio-economic one.

Compared to other products found online with similar accuracy, the LCCS MODIS data allows for annual mapping of the last two decades in an expeditious manner. This also allows their use as ancillary data for supervised classifications with other higher resolution remote sensing data. Moreover, since they are already classified, they can be easily used outside the scientific research environment. Finally, the lightness in terms of bytes of data allows analysis even with low performance hardware. The limits of applicability of this methodology are related to the resolution of data used which often does not allow for the identification of local dynamics that frequently have impacts that affect a larger scale.

5. Conclusions

In view of the achievement of some of the Sustainable Development Goals of the 2030 Agenda, environmental planning assumes a fundamental relevance as it allows for confronting territorial problems, such as the abandonment of agricultural areas in Basilicata, which are causing high negative impacts. Sustainable environmental planning is a process that aims to integrate ecological with the socio-economic principles for an overview of land management for present and future generations. In order to implement strategies for environmental planning, it is critical to collect, edit, and distribute timely and accurate data and to apply advanced land assessment and technologies to create scientific knowledge for suitable decision support systems. It is essential to implement tools that can allow an overview of the territory and that can ensure an effective and dynamic decision support system, thus meeting the needs of public decision makers to verify past management policies and developing new strategies. One of the basic approaches to developing more complex investigations and studies is the one illustrated in this work, which allows for the extrapolation of remote sensing data on land cover and land use and carries out analysis in an expeditious way both in research and in local actions.