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Article

Landscape Fragmentation at Arauco Province in the Chilean Forestry Model Context (1976–2016)

by
Edilia Jaque Castillo
1,
Carolina G. Ojeda
2,3,* and
Rodrigo Fuentes Robles
4
1
Departamento de Geografía, Facultad de Arquitectura, Urbanismo y Geografía, Universidad de Concepción, Edmundo Larenas s/n, Concepción 4030000, Chile
2
Doctorado en Arquitectura y Estudios Urbanos, Facultad de Arquitectura, Diseño y Estudios Urbanos, Pontificia Universidad Católica de Chile, Lo Contador 1980, Santiago de Chile 7500000, Chile
3
Departamento de Historia, Facultad de Comunicación, Historia y Cs. Sociales, Universidad Católica de la Santísima Concepción, Alonso de Ribera 2850, Concepción 4090541, Chile
4
Laboratorio de Ecología de Paisaje (LEP), Facultad de Ciencias Forestales, Universidad de Concepción, Edmundo Larenas s/n, Concepción 4030000, Chile
*
Author to whom correspondence should be addressed.
Land 2022, 11(11), 1992; https://doi.org/10.3390/land11111992
Submission received: 23 September 2022 / Revised: 21 October 2022 / Accepted: 27 October 2022 / Published: 7 November 2022
(This article belongs to the Section Landscape Ecology)
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Abstract

:
Land Cover–Land Use Changes (LULCC) and landscape fragmentation have been a common research topic for Geographic Information Systems (GIS) scientists since the middle of the 20th century; particularly, they have helped to make accessible the spatial characteristics of land management through time. We researched LULCC and landscape fragmentation in Arauco Province in Chile using satellite image analysis (1976–2016) and FRAGSTAT software. This area is in a constant struggle for land use between agroindustry, urban sprawl, and the expansion of exotic plantations (pine-eucalyptus) subsidized by Chilean government. The main results are: (1) we obtained the surface percentages for each land cover , (2) net changes for each cover by adding and losing surface (ha), (3) the transition map that enlightens the surface transformations of LULCC by its four processes substitution, abandonment, habilitation, regeneration and degradation, (4) the native forest loss in the first half of the period (1976–2001) was 1.85%/year, meanwhile for the second half (2001–2016) it was 6.5%/year, (5) landscape fragmentation processes occurred in patches and deforestation is its main driver, (6) aggregation changed the landscape since fragmentation and deforestation processes started the substitution of native forest, and (7) the habilitation of agricultural lands and degradation of wooded masses with exotic species increased their aggregation to 90%.

1. Introduction

Two growing phenomena that could lead to global environmental conflicts in the future are indiscriminate Land Use Land Cover Changes (LULCC) and landscape fragmentation. The latter is usually caused by anthropogenic interventions such as roads, railway lines, urban sprawl, exotic plantations, highways [1], species’ habitat loss [2], dispersion of pollutants, increasing acoustic emissions, change in local climatic conditions, and lack of water balance. Landscape fragmentation is understood as a methodology used to link patterns to processes that seemed to develop naturally in forests [3,4]. In technical terms, it corresponds to the sharp contrast between vegetation patches and their surrounding matrix, where native vegetation covers typically c. 10–60% [4]. Its real affectations can be quantified in patch morphology (increased edge, reduced interior area, increased isolation of patches, increased number of patches, and decreased average patch size [5]).
In that sense, measuring fragmentation requires balancing different amounts in different metrics (e.g., interior area and isolation distance) following the steps of the seminal work of Riiters et al. with 58 metrics [6]. Nonetheless, those metrics should be consistent with the relative aspects to the landscape patterns to track the patch’s dynamics, which is not always clear, as said in the remarkable genealogy of landscape fragmentation approaches of Fischer and Lindenmayer [7]. Later, Jaeger introduced three new measures of fragmentation from a geometric perspective: degree of landscape division (D), splitting index (S), and effective mesh size (m) [8].
On the other hand, LULCC are complex processes that have large constellations of actors that could intervene at them [9]. In that sense, land cover refers to the biophysical earth surface and land use is shaped by human, socio-economic and political drivers [10]. GIS technologies usually measure LULCC and its dynamics, temporally and spatially, with the help of satellite image analysis. Until now, LULCC change modelling is a holistic methodology that understands those changes; furthermore, they deal with two main questions: where the land-use changes will be (location of change) and at what rates the changes will be developed (quantity of change) [11].
In Southern Chile, LULCC and landscape fragmentation processes were historically modelled by different actors and have been fueled by the uncertainty of climate change dynamics. Since the 1970s, the studies in that region were made with LANDSAT satellite image analysis [12,13,14], which demonstrated that the major changes were due to replacing the native forest with exotic plantations for forestry [15], agricultural activities [16], and the urban/industrial surface. In the result, that cover replacement fomented rapid landscape fragmentation [17,18], landscape homogenization [19,20], high deforestation rates [21], loss of native forest [22], and habitat loss [23]. However, different voices are found in the work of Toro and Gessel [24] and Wright et al. [25] showing that the “forestry activities have produced a profound and positive change in the social and economic environment”.
The abovementioned changes produced by LULCC in southern Chile that have combined the public financing and national/international private capital have created territories highly dependents of those economic movements. As a result, the native vegetation cover has been continually replaced with exotic species characterized by their quick growth, trying to imitate the Nordic [26] and North American forestry industries [27,28]. Overall, those processes are propitiating accumulation by dispossession [29], the liberalization of economy at a large scale, displacement of indigenous communities [30,31], and precarious employment at small scales [32]. Furthermore, this represents a constant source of conflict in a water scarcity context [33,34], increasing soil erosion , and the constant presence of wildfires [35,36,37].
In this article, the LULCC and landscape fragmentation will be analyzed through satellite analysis over a forty-year period (1976–2016) in Arauco province. This Chilean province was selected to study the consistent land changes associated to the forestry industry [12] fomented for the entire period by government subsidies and the socioterritorial conflict with indigenous Mapuche communities due to land reclamations [30].

2. Materials and Methods

2.1. Pre-Processing and Processing of Satellite Images to Obtain LULCC

Landsat satellite images (Path 233, Row 086) were used to analyze the LULCC in Arauco Province with a spatial resolution of 30 m and a revisit of 16 days (about 2 and a half weeks) [38]: 1976 (Sensor MSS), 2001 (Sensor TM), and 2016 (Sensor OLI). To be selected they should not present cloudiness and the capture date should be closest to spring or summer in the Southern Hemisphere, both necessary conditions to fully appreciate the deciduous forests. Those images were pre-processed in three aspects: geometric correction, radiometric–atmospheric correction, and topographic correction.
The first correction was realized in Arc GIS 9.3 using a polynomial mathematical model of the third order, reducing the correction error to 0.01–0.1-pixel image (0.3–3 m) by selecting 90–120 random control points in roads and rivers. For that process, a cartographic base layer from Public Works Ministry (MOP in Spanish) and its corresponding satellite image were used. The second correction diminished the atmospheric effects in satellite images that came from sensor degradation, Earth-Sun distance variance and incidence angle [39,40,41,42]. In that sense, the digital values of the pixels were transformed into radiance values using a formula (1):
L λ = G rescale * DN + B rescale
where Lλ is the radiance in W m−2 sr−1 µm−1 for the band λ, Grescale and Brescale are the scalar factors specific for each band.
Following that, the radiance values (1) were transformed into reflectance using the data of the basin head and the radiometric calibration coefficients [39,40,43,44] using formula (2):
ρ p = π * L λ * d 2 E S U N λ *   cos θ s
where ρp is the reflectance value in the band p, Lλ is the radiance for the band λ, d is the Earth-Sun distance, ESUNλ corresponds to the exoatmospheric solar radiance for the band λ and θS is the zenith solar angle (grades).
The third correction eliminated the projected shadows in topography using a Factor C Method [45,46,47], which corresponds to a semi empiric adjustment of the quotient between the zenith solar angle and the incidence solar angle in the image, which relies on solar angles and slope of the terrain [38]. This process was made in IDRISI [48] using a formula (3):
ρ h , i = ρ i C o s   θ i + C k C o s   y i + C k
where ρ h , i . is the reflectivity of a pixel in a horizontal way, ρi is the reflectivity of a pixel in the slope, θi corresponds to the zenith solar angle over the plane, yi is the angle of incidence of the sun over the scene, and Ck is the empiric constant for each k band (related to the average rugosity of that band).
After those pre-processes, to enhance the vegetation cover the images were processed through a supervised classification using two ENVI 4.7 tools: maximum verisimilitude statistics and training points 1 [38,49]. Furthermore, they were processed through false colors to distinguish between the exotic forestry plantations and native forests in selected bands combination (4-5-3). The resulting LULCC for Arauco Province were native forest (adult, secondary, and stunted of the same class), exotic forest plantations (Pines and Eucalyptus monocultures), shrubland, agricultural land (crops and grasslands), bare soil, water bodies, wetlands, and urban land [50,51]. Those covers were studied in two periods (1976–2001 and 2001–2016) in IDRISI [48] looking for net changes for each one revealing the surface’s addition and loss (ha). In addition, the accuracy of each map was evaluated with confusion matrices constructed using validation points obtained from three main sources (Table 1).

2.2. Landscape Fragmentation with FRAGSTAT Analysis

The analysis started by observing the change in adult native forest in Arauco Province, which was studied in ArcGIS 9.3. The yearly deforestation rate was spotted using the formula of [17,51] (4):
P = [ ( A 2 A 1 ) 1 ( t 2 − t 1 ) − 1 ] * 100
where A1 − A2 corresponds to the native forest land cover surface (ha) in the initial year (t1) and the final year of the analysis (t2). P is the % of native forest loss/year.
After that, the analysis for the years 1976, 2001, and 2016 was carried out in the software FRAGSTAT 3.3 [17,52]. This helped to evaluate the changes of native forest configuration at class level and its interaction with the rest of land cover in a landscape level through the calculus of selected metrics [53]:
(a)
the total patch areas measured in ha,
(b)
the patch number,
(c)
medium proximity index, which is the difference between the size and the proximity of all patches within 200 m (about 656.17 ft),
(d)
the bigger patch area, which is the percentage of landscape covered by the biggest patch,
(e)
the patch density, which is the patch number observed in 100 ha,
(f)
the aggregation index, which is the percentage of adjacency between pixels of distinct types of land cover,
(g)
the adjacency index, which corresponds to the edge length between the adult native forest and the other types of land cover measured in km.

2.3. Description of the Study Area

Arauco Province (5464 km2) is part of the Biobío region in Chile (14.7% of regional surface) (Figure 1). The outstanding Nahuelbuta mountain range have modelled the three major units of physical landscape, two of them under ecological conservation: (1) the Contulmo Natural Park, (2) the Nahuelbuta mountain range and its National Park, and (3) the wide coastal plains, which are water bodies of high ecological and cultural significance within the ancestral Lafkenche territory composed of the Nahuelbuta, Lleu Lleu and Lanalhue lakes. From a morphogenetic point of view, these coastal lakes had a tectonic origin creating ancient coastal valleys whose drainage was obstructed in the recent Pleistocene by the rock formations of the Arauco platform due to periodic seismic crises and by the subsequent coastal dune formations. Indeed, it is a dynamic territory from both endogenous and anthropogenic geodynamic perspectives, which transforms it into a territory of high interest for spatial analysis.
Its predominant climate is temperate with Mediterranean influence corresponding to their presence in the region with an average temperature of 13.3 °C, a temperature oscillation between −1 °C in winter to 9 °C in summer and an average annual precipitation of 1500–2500 mm (about 8.2 ft) [54].
Based on the management standard guidelines proposed by CONAF [55] and previous studies [56,57,58], the most representative species of native forest at its two natural reserves (Contulmo Natural Park and National Park) correspond to Oak (Nothofagus obliqua), Raulí (N. alpina), and Coigüe (N. dombeyi). Additionally, there is the significant presence of an endangered conifer of the temperate forests called Monkey Puzzle Tree (Araucaria araucana), which is pressured by cattle and timber production [16,59]. Lastly, those forests are considered a vanishing refugee center of biodiversity and have endemic species since old times [60] such as Eraina Clarke (Lepidoptera: Autostichidae); however, they have been fragmented by logging, manufactured wildfires, and land clearance since European colonization in the 16th century [12,36,61].

3. Results

3.1. Accuracy Assessment of LULCC Classifications

The overall accuracy for the 1976 classification of LULCC was 84.5%, in which the highest values were reached at the zones with agricultural land (96.2%) and native forest (86.1%), due to the large extension covered by these LULCC in that year. On the other hand, the lowest accuracy value for that year corresponded to shrublands (65.4%) and urban areas (68.8%). For 2001 classification, the overall accuracy was 85.3%, and like the 1976 classification, the highest values corresponded to the agricultural land and native forest (90.0% and 85.1%, respectively). The lowest values for that year corresponded to shrubland (81.8%) and bare soil (83.3%). Lastly, the accuracy for the 2016 classification was 89.4%, where the highest values were reached by the areas with bare soils (96.2%) and agricultural lands (91.7%); on the contrary, the lowest accuracy value corresponded to shrublands (88.1%) and wetlands (82.1%).

3.2. LULCC Analysis for 1976–2001 and 2001–2016 in Arauco Province

The satellite analysis made for the surface of Arauco province over a period of 40 years from 1976 to 2016 was divided into half intervals. The first results are the percentages of surface for every land cover analyzed (Figure 2), the second results are the net changes for every cover revealing the results of adding and losing surface (ha), and the third results are the transition map enlightening the surface transformations of LULCC: substitution, abandonment, habilitation, regeneration, and degradation. In general, it was observed that the constant diminishing of native forests in the period from 37% of total surface in 1976 to 8% in 2016. Also, another cover that lost surface is agricultural land with 10% lost over the entire period. In contrast, exotic plantations characterized by pines and eucalyptus plantations increased their total surface from 50% in 1976 to 63% by 2016 (Figure 2)
The net change in the study area describes the sum of all changes over a specific period counting loss and gain of each LULCC describing quantitatively the patterns that changed, both in general and in detail (Table 2). In this case, the major changes observed came from exotic plantations (adding 153,000 ha), native forest (losing 74,000 ha), agricultural land (losing 54,000 ha), and shrubland (losing 29,000 ha). For the second half, from 2001 to 2016, the net change is like the first period, which means that the most observable net changes occurred in exotic plantations (added 110,000 ha), native forests (loss of 78,000 ha), shrublands (loss of 27,000 ha), and the explosive augment of urban areas (added 3000 ha) (Table 2). It is worth mentioning that wetland cover lost over 50% of its surface after the 2010 earthquake, which made an uprising of the Arauco Gulf coastal area that was affected at the level of water table. Also, other factors that have affected Arauco’s wetlands are the conversion into agricultural lands (270 ha) or exotic plantations (368 ha) and the constant precipitation scarcity in the last decades [62].
The transition map showed where the major transformations over 5000 ha have occurred (Figure 3a,b). From 1976 to 2001, the regeneration of native forest (10.8% of total surface) occurred in the province’s highlands at the north of Nahuelbuta National Park corresponding to conservation zones of Caramávida and Trongol Alto owned by Bosques Arauco and Forestal Mininco, respectively. On the other hand, the degradation of native forest (5.2% of total surface) came from two main sources: its habilitation to make new agricultural lands (13% of total surface), and its substitution for exotic plantations (29.7% of total surface), which corresponded to highlands in the north-eastern cities of Carampangue, Curanilahue and Cañete (Figure 1). Also, at the end of the period, new land (27.3% of total surface) and abandonedagricultural land was added to the forest plantation cover (13.9% of total surface).
In the second period (2001–2016) (Figure 4a,b), the transition maps showed a notorious pattern in the province highlands near Nahuelbuta National Park due to the degradation of native forest into shrublands. The most dominant process of LULCC was the substitution of native forest with 33.9% of the total surface, followed by the forestation with exotic species with 28.1% of total surface, habilitation for agricultural lands (22.6%), degradation of native forests (7.8%), and abandonment of agricultural lands (7.6%). Those phenomena could be explained by the constant extraction of wood for heating by nearby populations, the expansion of exotic plantations, and clearing for cattle grazing.

3.3. Changes into Spatial Patterns of Native Forests in Arauco Province: Fragmentation Analysis of Native Forests Using Landscape Metrics

In general, the results showed that the loss of native forest in the first half of the studied period (1976–2001) was 1.85% per year; meanwhile, for the second half (2001–2016), it was 6.5% per year. This value of exchange rate for the second half is not consistent with the previous studies done in the southern center of Chile [56,57,58,60,61]; however, this could be explained by the abbreviated time of analysis of the second half, which is ten years less. For this study, the landscape metrics that evaluated the native forest were divided into patch areas, bigger patch area (%), patch density (n/100 ha), medium proximity index, aggregation index and adjacency index [3]. The patch areas for the entire period showed that the processes of landscape fragmentation and deforestation occurred systematically at the lower and higher study area (Table 3) (Figure 2).
The landscape metrics at class level coincided with the patch analysis (Table 3) showing that the dominant process in the first period (1976–2001) was the fragmentation of native forest patches: the density augmented from 4.42 to 6.59 into a surface of 100 ha, the bigger forest patch loss territory from 5% of total surface (1976) to a 2.2% of total surface (2001), and the medium proximity of the patches were reduced from 13,517 m (1976) to 1077 m (2001). This last element is a clear indicator of landscape fragmentation of continuous patches. The second half of the studied period showed that deforestation is the main driver of landscape fragmentation, because the patches of the first period begin to disappear by diminishing the patch’s density in 100 ha 6.59 (2001) to 2.7 (2016). Another clear indicator of landscape fragmentation is that the bigger patch occupies only 1.56% of the total surface analyzed, reducing its space to merely 30% compared with 2001. Lastly, the medium distance between the patches increased from 1077 m (about 3533.46 ft) to 1350 m (about 4429.13 ft).
The aggregation processes showed that the planted areas with exotic species rose to an astonishing 90% (Table 4). On the contraire, the native forest diminished from 76% in 1976 to 68% in 2001 due to the fragmentation that suffered the relict forest patches. Likewise, agricultural land diminished from 83% to 63%, which was caused by exotic species planted in the previous period (1976–2001) that presented higher spatial aggregation (83%), if we discount the analysis of less dynamic areas in the exchange term (water, bare soil, and wetlands). In that same year, the exotic plantations (pines and eucalyptus) had a low aggregation (77%) in comparison to other land covers. Lastly, the lower aggregation values were observed in natural shrublands, explained by their location in separated ravines.
On the landscape scale, the adjacency index (Table 5) showed that the contact between borders of native forest patches has been changed through time. In the first year of study (1976) the native forest shared the major border quantity (960 km) with natural shrublands. However, this contact is loose, ending with 150 km (about 93.21 mi) 40 years later. On counterpart, the exotic plantations were not in contact at their borders with native forest at the beginning of 1976 (579 km) to the end of 2001 with 1200 km (about 745.65 mi) of contact longitude. This process could be explained after the replacement of native forest with those exotic plantations, enclosing them. Lastly, the agricultural lands showed the lower contact longitude with native forest, mainly because those fragments are in the highlands, which naturally do not favor the development of croplands and grasslands.

4. Discussion

According to Heylmar et al. [63], the exotic plantations have been directly displacing native forests in Central–Southern Chile, especially during the 1986–2001 period. Through a historical review, they have concluded that existed multiple forest transitions throughout Chile’s history, which mostly correspond to the massive plantations of pines (Pinus radiata) and eucalyptus (Eucalyptus nitens y Eucalyptus globulus Labill). However, not all the researchers agree with those results, putting in doubt if some species (Pinus radiata) are effectively invading the native vegetation in that area [59]. Those exotic plantations were promoted in Chile after the military coup of 1973 within a public subsidy (DL701) and they are still being financed by the following governments. These extractive activities are known today as the Chilean Forestry Model (CFM) [64,65], which has been expanding its action surface through southern Chile [66] with an estimated surface of 2,414,208 ha at national level in 2016 [67]. In addition, this extractive model has been exported through southern Latin American countries [25,68,69].
In that sense, this study presents the four stages of LULCC: substitution, abandonment, habilitation, regeneration, and degradation, from 1976 to 2016, which allowed to observe the changes directly and indirectly promoted by the CFM in the Arauco Province. Firstly, there was a significant reduction of traditional covers such as native forests (50%) and agricultural land (10%), in favor of of exotic plantations, the latter expanded by 50%. This systematic change has been pushed by the neoliberal economy present in the country crystallized in the CFM, and, by the public policies of subsidized monocultures of forestry and agriculture [58,64,65].
Examples of this are observed in the countries that were under the Soviet orbit. According to Bucala-Hrabia [70], who worked with remote sensing and GIS analyzing the LULCC in the European Carpathians, the change of the communist economy systems to a free market (1989) fomented a decrease in agricultural production and an increase in the exotic plantation area. On the contrary, public policies can contribute to a reverse process, as pointed out by Lira et al. [71], who studied the land use changes in Brazil, where a reinforcement of the Brazilian Forest Act led to an increase in native forest regeneration between 1980 and 2000:from 10% to 50%.
Thus, the dominant processes observed in Arauco Province were the fragmentation of native forest patches (1976–2001) and deforestation (2001–2016). Both were the most significant drivers of landscape fragmentation of the area, which points to an entirely anthropogenic landscape transformation. This rapid change in the vegetal composition could increase the damages of hydrometeorological hazards caused by the mega drought that have been affecting central-south Chile since 2010 [62], augmenting the damage that could cause the wildfires, and increasing the problems that could be caused by extreme weather events in winter (water run-off) and summer (heat waves). Those abovementioned phenomena could put more pressure on the municipalities of the Arauco Province, which do not have enough material resources and trained personnel to carry out the appropriate measures to avoid fatalities.
The spatial aggregation through time changed the landscape permanently since the fragmentation and deforestation processes started a significative substitution of native forest, the habilitation of agricultural lands, and the degradation of wooded masses. Furthermore, this lack of connectivity has been damaging the local biodiversity and endemic species (e.g., Araucaria araucana forests) [60], and, consequently, diminishing the opportunities to support global carbon sequestration to tackle climate change [25]. This change is the main driver of the slow disappearance of the traditional income sources of their inhabitants, such as the recollection of fruits and medicinal leaves, small farmers, livestock owners, firewood vendors, honey producers, and ecotourism entrepreneurs [32,36]. In that sense, the consistent upward trend of environmental conflicts between the agroforestry corporations, indigenous communities, and inhabitants of the province for land and water consumption is not a surprise [72]. However, the root causes of fragility in the latter are still under discussion [34,35].

5. Conclusions

In the last 40 years, the process of land use change has severely affected the fragmentation of the native forest in the Nahuelbuta Range in Chile (37° and 38°30 “LS). In this study, which covers this period, it was observed that the loss of native forest in the first period was 50%, and 46% of this percentage corresponded to forest plantations. In the second period, native forest lost 36% of its area, but unlike the previous period, here 99% of the area was gained exclusively by forest plantations. Losses were also recognized for other land covers such as wetlands and agricultural land. These results have been translated into landscape homogenization dynamics, which can be seen in the increase in the number of patches and the decrease in their size. This had already been reported for southern central Chile [12,73].
At the beginning of the forestry model in 1974, the native forest occupied soils of greater accessibility in the intermediate depression and on the eastern slope of the coastal range between Maule (34° LS–71° W) and Araucanía (39° LS–72° W) [73]. However, the substitution of native forest by exotic plantations is not complete because this mountain range still has relics of native vegetation with high ecological value.
Thus, the state subsidy granted to afforestation alone cannot explain the speed of the LULCC in replacing native forest with exotic plantations, which would be due to physical limitations that made accessibility difficult in the mountainous massif of the Nahuelbuta Range, which has slopes of more than 20°. In the first period analyzed, although there were several transitions from one cover to another, the one that always dominated was the exotic plantation cover, which gained surface area in all the processes of land use change.
From the perspective of native forest fragmentation, in the first period of analysis, the number of patches increased by 700%. For example, the index of the largest patch that covered a large part of the province decreased its percentage, leaving only a small portion of it in the highest part of the Cordillera, i.e., the Nabuelbuta National Park.
Thus, the pressure of change was observed with greater force in the second period studied in a process of advance from northwest to southeast, which would be associated with the accessibility given by the topography, leaving a fragment of island that corresponds to the Nahuelbuta National Park. Furthermore, it is important to point out that the techniques and methodologies used for this work are easily accessible since they are economic tools. The results of both the land cover analysis from satellite images and the analysis of these results with FRAGSTAT software allow the construction of information. They are also easy to interpret for decision makers in areas where land use changes imply vegetational deterioration factors or activate other phenomena such as erosion processes, changes in river channels and phenomena associated with the risk of forest fires.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land11111992/s1.

Author Contributions

Conceptualization, methodology, software, and validation R.F.R. Writing—original draft preparation C.G.O. Writing—review and editing R.F.R., C.G.O. and E.J.C. Project administration and funding acquisition E.J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially supported by FONDECYT [grant number 1171065] (EJC), Facultad de Arquitectura, Urbanismo y Geografía and Departmento de Geografía from Universidad de Concepción (EJC), and Beca de Doctorado Nacional ANID (CO) [grant number 21200455].

Data Availability Statement

Supplemental data could be retrieved by petition from Rodrigo Fuentes rofuentes@udec.cl.

Acknowledgments

We want to acknowledge the input technical report made by Diego Miranda. Also, we want to acknowledge the contribution of the reviewers.

Conflicts of Interest

The authors declare that they have no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Note

1
For the most recent satellite image (2016), the training points were taken at fieldwork. For the oldest satellite images (1976–2001), aerial photographs and native forest surveys made by CONAF (1999) were used.

References

  1. Davidson, C. Issues in Measuring Landscape Fragmentation. Wildl. Soc. Bull. 1998, 26, 32–37. [Google Scholar]
  2. Opdam, P.; Apeldoorn, R.V.; Schotman, A.; Kalkhoven, J. Population responses to landscape fragmentation. In Landscape Ecology of a Stressed Environment; Springer: Dordrecht, The Netherlands, 1993; pp. 147–171. [Google Scholar]
  3. Nagendra, H.; Munroe, D.K.; Southworth, J. From pattern to process: Landscape fragmentation and the analysis of land use/land cover change. Agric. Ecosyst. Environ. 2004, 101, 111–115. [Google Scholar] [CrossRef]
  4. Entwisle, B.; Walsh, S.J.; Rindfuss, R.R.; Chamratrithirong, A. Land-use/land-cover and population dynamics, Nang Rong, Thailand. In People and Pixels: Linking Remote Sensing and Social Science; Liverman, D., Moran, E.F., Rindfuss, R.R., Stern, P.C., Eds.; National Academy Press: Washington, DC, USA, 1998; pp. 121–144. [Google Scholar]
  5. McIntyre, S.; Hobbs, R. A framework for conceptualizing human effects on landscapes and their relevance to management and research models. Conserv. Biol. 1999, 13, 1282–1292. [Google Scholar] [CrossRef]
  6. Riitters, K.H.; Oneill, R.V.; Hunsaker, C.T.; Wickman, J.D.; Yankee, D.H.; Timmins, S.P.; Jones, K.B.; Jackson, B.L. A factor analysis of landscape pattern and structure metrics. Landsc. Ecol. 1995, 10, 23–39. [Google Scholar] [CrossRef]
  7. Fischer, J.; Lindenmayer, D.B. Landscape modification and habitat fragmentation: A synthesis. Glob. Ecol. Biogeogr. 2007, 16, 265–280. [Google Scholar] [CrossRef]
  8. Jaeger, J.A. Landscape division, splitting index, and effective mesh size: New measures of landscape fragmentation. Landsc. Ecol. 2000, 15, 115–130. [Google Scholar] [CrossRef]
  9. Renetzeder, C.; Schindlera, S.; Peterseila, J.; Prinza, M.A.; Mücher, S.; Wrbka, T. Can we measure ecological sustainability? Landscape pattern as an indicator for naturalness and land use intensity at regional, national, and European level. Ecol. Indic. 2010, 10, 39–48. [Google Scholar] [CrossRef]
  10. Geist, H.J.; Lambin, E.F. Proximate Causes and Underlying Driving Forces of Tropical Deforestation. BioScience 2002, 52, 143–150. [Google Scholar] [CrossRef] [Green Version]
  11. Veldkampa, A.; Lambin, E.F. Predicting land-use change. Agric. Ecosyst. Environ. 2001, 85, 1–6. [Google Scholar] [CrossRef]
  12. Aguayo, M.; Pauchard, A.; Azócar, G.; Parra, O. Cambio del uso del suelo en el centro sur de Chile a fines del siglo XX: Entendiendo la dinámica espacial y temporal del paisaje. Rev. Chil. De Hist. Nat. 2009, 82, 361–374. [Google Scholar] [CrossRef] [Green Version]
  13. Fuentes, E.; Hajek, E. Patterns for landscape modification in relation to agricultural practice in central Chile. Environ. Conserv. 1979, 6, 265–271. [Google Scholar] [CrossRef]
  14. Centro De Análisis De Políticas Públicas Universidad De Chile. Informe País. Estado del Medio Ambiente en Chile. Comparación 1999–2015; Gligo, V.N., Ed.; MAVAL Impresores: Santiago de Chile, Chile, 2016; Available online: http://www.uchile.cl/documentos/informe-pais-estado-del-medio-ambiente-en-chile-comparacion-1999-2015_129586_0_1412.pdf (accessed on 23 September 2022).
  15. Nahuelhual, L.; Carmona, A.; Lara, A.; Echeverría, C.; González, M.E. Land-cover change to forest plantations: Proximate causes and implications for the landscape in south-central Chile. Landsc. Urban 2012, 107, 12–20. [Google Scholar] [CrossRef] [Green Version]
  16. Hojman, D.E. Change in the Chilean Countryside: From Pinochet to Aylwin and beyond; Springer: New York, NY, USA, 1993. [Google Scholar]
  17. Echeverría, C.; Coomes, D.; Salas, J.; Rey-Benayas, J.M.; Lara, A.; Newton, A. Rapid deforestation, and fragmentation of Chilean Temperate Forests. Biol. Conserv. 2006, 4, 481–494. [Google Scholar] [CrossRef]
  18. Becerra, P.I.; Simonetti, J.A. Patterns of exotic species richness of different taxonomic groups in a fragmented landscape of Central Chile. Bosque 2013, 34, 11e12. [Google Scholar] [CrossRef] [Green Version]
  19. Miranda, A.; Altamirano, A.; Cayuela, L.; Pincheira, F.; Lara, A. Different times, same Story: Native Forest loss and landscape homogenization in three physiographical areas of south-central of Chile. Appl. Geogr. 2015, 60, 20e28. [Google Scholar] [CrossRef]
  20. Muñoz-Pedreros, A.; Larraín, A. Impacto de la actividad silvoagropecuaria sobre la calidad del paisaje en un transecto del sur de Chile. Rev. Chil. De Hist. Nat. 2002, 75, 673–689. [Google Scholar] [CrossRef] [Green Version]
  21. Altamirano, A.; Lara, A. Deforestation in temperate ecosystems of pre- Andean range of south-central Chile. Bosque 2010, 31, 53e64. [Google Scholar]
  22. Miranda, A.; Altamirano, A.; Cayuela, L.; Lara, A.; González, M. Native Forest loss in the Chilean biodiversity hotspot: Revealing the evidence. Reg. Environ. Change 2017, 17, 285–297. [Google Scholar] [CrossRef]
  23. McFadden, T.N.; Dirzo, R. Opening the silvicultural toolbox: A new framework for conserving biodiversity in Chilean timber plantations. For. Ecol. Manag. 2018, 425, 75–84. [Google Scholar] [CrossRef]
  24. Toro, J.; Gessel, S. Radiata pine plantations in Chile. New For. 1999, 18, 33. [Google Scholar] [CrossRef]
  25. Wright, J.A.; Di Nicola, A.; Gaitan, E. Latin American Forest Plantations: Opportunities for Carbon Sequestration, Economic Development, and Financial Returns. J. For. 2000, 98, 20–23. [Google Scholar] [CrossRef]
  26. Lara, A.; Veblen, T. Forest plantations in Chile: A successful model? In Afforestation: Policies, Planning and Progress; Mather, A.S., Ed.; Belhaven Press: London, UK, 1993. [Google Scholar]
  27. Kjær, E.D.; Lobo, A.; Myking, T. The role of exotic tree species in Nordic forestry. Scand. J. For. Res. 2014, 29, 323–332. [Google Scholar] [CrossRef]
  28. Clapp, R.A. The unnatural history of the Monterey pine. Geogr. Rev. 1995, 85, 1–19. [Google Scholar] [CrossRef]
  29. Harvey, D. Spaces of Neoliberalization: Towards a Theory of Uneven Geographical Development; Franz Steiner Verlag: Berlin, Germany, 2005. [Google Scholar]
  30. Seguel, A. Modelo Forestal Chileno y Movimiento Autónomo Mapuche: Las Posiciones Irreconciliables de un Conflicto Territorial. 2005. Available online: http://www.ibcperu.org/doc/isis/5645.pdf (accessed on 23 September 2022).
  31. Schmidt, C.; Rose, J. Environmental and cultural changes under Chilean neoliberalism: An ethnography of forestry and the Mapuche in Valle Elicura. Local Environ. 2017, 22, 1019–1034. [Google Scholar] [CrossRef]
  32. Montalba-Navarro, R.; Carrasco, N.; Araya, J. Contexto Económico y Social de las Plantaciones Forestales en Chile: El Caso de la Comuna de Lumaco, Región de la Araucanía; Movimiento Mundial por los Bosques: Montevideo, Uruguay, 2005; Available online: https://wrm.org.uy/es/files/2013/03/Contexto_economico_y_social_de_las_plantaciones_forestales_en_Chile_ch.pdf (accessed on 23 September 2022).
  33. Reyes, R.; Nelson, H. A tale of two forests: Why forests and forest conflicts are both growing in Chile. Int. For. Rev. 2014. Available online: http://www.bioone.org/doi/full/10.1505/146554814813484121 (accessed on 23 September 2022). [CrossRef] [Green Version]
  34. Little, C.; Lara, A.; McPhee, J.; Urrutia, R. Revealing the impact of forest exotic plantations on water yield in large scale watersheds in south-Central Chile. J. Hydrol. 2009, 374, 162–170. [Google Scholar] [CrossRef]
  35. Huber, A.; Iroumé, A.; Bathurst, J. Effect of Pinus radiata plantations on water balance in Chile. Hydrol. Processes 2008, 22, 142–148. [Google Scholar] [CrossRef]
  36. Cid, B. Peasant economies, forestry industry and fires: Socio-natural instabilities and agriculture as means of resistance. Ambiente Soc. 2015, XVII, 93–114. [Google Scholar]
  37. Ubeda, X.; Sarricolea, P. Wildfires in Chile: A review. Glob. Planet. Change 2016, 146, 152–161. [Google Scholar] [CrossRef]
  38. Chuvieco, E. Teledetección Ambiental. La observación de la Tierra Desde el Espacio; Ariel: Barcelona, Spain, 2002. [Google Scholar]
  39. Chávez, P. Image-based atmospheric corrections. Photogramm. Eng. Remote Sens. 1996, 62, 1025–1036. [Google Scholar]
  40. Vicente-Serrano, S.M.; Pérez-Cabello, F.; Lasanta, T. Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sens. Environ. 2008, 112, 3916–3934. [Google Scholar] [CrossRef]
  41. Matricardi, E.A.T.; Skole, D.L.; Pedlowski, M.A.; Chomentowski, W.; Fernandes, L.C. Assessment of tropical forest degradation by selective logging and fire using Landsat imagery. Remote Sens. Environ. 2010, 114, 1117–1129. [Google Scholar] [CrossRef]
  42. Chander, G.; Markham, B. Revised Landsat-5 Radiometric Calibration Procedures and Post calibration Dynamic Ranges. IEEE Trans. Geosci. Remote Sens. 2003, 41, 2.674–2.677. [Google Scholar] [CrossRef] [Green Version]
  43. Vagen, T.-G. Remote sensing of complex land use change trajectories-a case study from the highlands of Madagascar. Agric. Ecosyst. Environ. 2006, 115, 219–228. [Google Scholar] [CrossRef]
  44. Chander, G.; Markham, B.L.; Helder, D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113, 893–903. [Google Scholar] [CrossRef]
  45. Teillet, P.M.; Guidon, B.; Goodenough, D.G. On the slope-aspect correction of multispectral scanner data. Can. J. Remote Sens. 1982, 8, 84–106. [Google Scholar] [CrossRef] [Green Version]
  46. Hantson, S.; Chuvieco, E. Evaluation of different topographic correction methods for Landsat imagery. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 691–700. [Google Scholar] [CrossRef]
  47. Reese, H.; Olsson, H. C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter. Remote Sens. Environ. 2011, 115, 1387–1400. [Google Scholar] [CrossRef] [Green Version]
  48. Clark, L. IDRISI Andes. Guide to GIS and Image Processing; Clark University: New York, NY, USA, 2006; p. 327. [Google Scholar]
  49. Segura, R.; Trincado, G. Cartografía digital de la Reserva Nacional Valdivia a partir de imágenes satelitales Landsat TM. Rev. Bosque 2003, 24, 43–52. [Google Scholar] [CrossRef]
  50. CONAF, CONAMA, BIRF, UA CHILE, PUC Chile, UC Temuco. Catastro y Evaluación de los Recursos Vegetacionales Nativos de Chile; CONAF: Santiago, Chile, 1999. [Google Scholar]
  51. FAO. Forest Resources Assessment 1990; Global Synthesis: Rome, Italy, 1995. [Google Scholar]
  52. Mcgarigal, K.; Cushman, S.A.; Neel, M.C.; Ene, E. Software FRAGSTATS Spatial Pattern Analysis Program for Categorical Maps; University of Massachusetts: Amherst, MA, USA, 2002; Available online: http://www.umass.edu/landeco/research/fragstats/fragstats.html (accessed on 2 November 2022).
  53. Balej, M. Landscape metrics as indicators of the structural landscape changes—Two case studies from the Czech Republic after 1948. J. Land Use Sci. 2012, 7, 443–458. [Google Scholar] [CrossRef]
  54. Sarricolea, P.; Herrera, M.J.; Meseguer-Ruiz, O. Climatic regionalization of continental Chile. J. Maps 2016, 17, 114–124. [Google Scholar]
  55. Lara, A.; Donoso, C.; Donoso, P.; Núñez, P.; Cavieres, A. Normas de manejo para raleo de renovales del tipo forestal roble-raulí-coigüe. In Silvicultura de los Bosques Nativos de Chile; Donoso, C., Lara, A., Eds.; Editorial Universitaria: Santiago, Chile, 1998; pp. 129–144. [Google Scholar]
  56. Echeverría, C.; Lara, A. Growth patterns of secondary Nothofagus obliqua -N. alpina forests in southern Chile. For. Ecol. Manag. 2004, 195, 29–43. [Google Scholar] [CrossRef]
  57. Gezan, S.A.; Ortega, A.; Andenmatten, E. Diagramas de manejo de densidad para renovales de roble, raulí y coigüe en Chile. Bosque 2007, 28, 97–105. [Google Scholar] [CrossRef]
  58. Morales, F. Los bosques nativos chilenos y la "política forestal" en la primera mitad del siglo XX. Cuadernos De Historia 2007, 26, 135–167, Disponible en. Available online: https://cuadernosdehistoria.uchile.cl/index.php/CDH/article/view/47150 (accessed on 23 September 2022).
  59. Bustamante, R.O.; Simonetti, J.A. Is Pinus radiata invading the native vegetation in central Chile? Demographic responses in a fragmented forest. Biol. Invasions 2005, 7, 243–249. [Google Scholar] [CrossRef]
  60. Smith-Ramírez, C.; Squeo, F.A. (Eds.) Biodiversidad y Ecología de los Bosques Costeros de Chile; Editorial Universidad de Los Lagos: Puerto Montt, Chile, 2019. [Google Scholar]
  61. Armesto, J.; Villagrán, C.; Donoso, C. Desde la era glacial a la industrial: La historia del bosque templado chileno. Ambiente Y Desarro. 1994, 66–72. Available online: https://www.pichimahuida.info/publications_files/La%20historia%20del%20Bq%20templado%20de%20Chile_Armesto_1994.pdf (accessed on 2 November 2022).
  62. Garreaud, R.D.; Boisier, J.P.; Rondanelli, R.; Montecinos, A.; Sepúlveda, H.H.; Veloso-Aguila, D. The Central Chile mega drought (2010–2018): A climate dynamics perspective. Int. J. Climatol. 2020, 40, 421–439. [Google Scholar] [CrossRef]
  63. Heilmayr, R.; Echeverría, C.; Fuentes, R.; Lambin, E.F. A plantation-dominated forest transition in Chile. Appl. Geogr. 2016, 75, 71–82. [Google Scholar] [CrossRef] [Green Version]
  64. Heilmayr, R.; Lambin, E.F. Impacts of nonstate, market-driven governance on Chilean forests. Proc. Natl. Acad. Sci. USA 2016, 113, 2910e2915. [Google Scholar] [CrossRef] [Green Version]
  65. Frêne Conget, C.; Núñez Ávila, M. Hacia un nuevo Modelo Forestal en Chile. Rev. Bosque Nativ. 2010, 47, 25–35. Available online: http://www.sendadarwin.cl/espanol/wp-content/uploads/2011/06/frene-y-nunez1.pdf (accessed on 23 September 2022).
  66. Niklitschek, M.E. Trade liberalization and land use Changes: Explaining the expansion of afforested land in Chile. For. Sci. 2007, 53, 385–394. [Google Scholar] [CrossRef]
  67. Instituto Nacional Forestal (INFOR). Informe Forestal 2018. Available online: http://wef.infor.cl (accessed on 23 September 2022).
  68. Simberloff, D.; Nuñez, M.A.; Ledgard, N.J.; Pauchard, A.; Richardson, D.M.; Sarasola, M.; Ziller, S.R. Spread and impact of introduced conifers in South America: Lessons from other southern hemisphere regions. Austral Ecol. 2010, 35, 489–504. [Google Scholar] [CrossRef]
  69. Licata, J.A.; Gyenge, J.E.; Fernández, M.E.; Schlichter, T.M.; Bond, B.J. Increased water uses by ponderosa pine plantations in northwestern Patagonia, Argentina compared with native forest vegetation. For. Ecol. Manag. 2008, 255, 753–764. [Google Scholar] [CrossRef]
  70. Bucała-Hrabia, A. Long-term impact of socio-economic changes on agricultural land use in the Polish Carpathians. Land Use Policy 2017, 64, 391–404. [Google Scholar] [CrossRef]
  71. Lira, P.K.; Tambosi, L.R.; Ewers, R.M.; Metzger, J.P. Land-use and land-cover change in Atlantic Forest landscapes. For. Ecol. Manag. 2012, 278, 80–89. [Google Scholar] [CrossRef]
  72. Instituto Nacional de Derechos Humanos (INDH). Mapa de Conflictos Socioambientales en Chile; INDH: Santiago, Chile, 2021; Available online: https://mapaconflictos.indh.cl/#/ (accessed on 23 September 2022).
  73. Uribe, S.V.; García, N.; Estades, C.F. Effect of Land Use History on Biodiversity of Pine Plantations. Front. Ecol. Evol. 2021, 9, 609627. [Google Scholar] [CrossRef]
Land 11 01992 g001 550
Figure 1. Map of the Arauco province in Chile.
Figure 1. Map of the Arauco province in Chile.
Land 11 01992 g001
Land 11 01992 g002 550
Figure 2. LULCC at Arauco province in 1976, 2001 and 2016: (a) In 1976 there were few exotic plantations and urban sprawl; (b) After almost 30 years of government subsidization the exotic plantations grew exponentially; (c) For 2016 the exotic plantations were the most common land use in the entire province enclosing the native forest cover around Nahuelbuta National Park. Figures with better resolution are in supplemental materials.
Figure 2. LULCC at Arauco province in 1976, 2001 and 2016: (a) In 1976 there were few exotic plantations and urban sprawl; (b) After almost 30 years of government subsidization the exotic plantations grew exponentially; (c) For 2016 the exotic plantations were the most common land use in the entire province enclosing the native forest cover around Nahuelbuta National Park. Figures with better resolution are in supplemental materials.
Land 11 01992 g002
Land 11 01992 g003 550
Figure 3. (a) LULCC processes and transitions at Arauco Province from 1976 to 2001; (b) LULCC processes at Arauco province from 1976 to 2001: substitution, abandonment, habilitation, regeneration, and degradation. Figures with better resolution are in supplemental materials.
Figure 3. (a) LULCC processes and transitions at Arauco Province from 1976 to 2001; (b) LULCC processes at Arauco province from 1976 to 2001: substitution, abandonment, habilitation, regeneration, and degradation. Figures with better resolution are in supplemental materials.
Land 11 01992 g003
Land 11 01992 g004 550
Figure 4. (a) LULCC processes and transitions at Arauco Province from 2001 to 2016; (b) LULCC processes at Arauco province from 2001 to 2016: substitution, abandonment, habilitation, regeneration, and degradation. Figures with better resolution are in supplemental materials.
Figure 4. (a) LULCC processes and transitions at Arauco Province from 2001 to 2016; (b) LULCC processes at Arauco province from 2001 to 2016: substitution, abandonment, habilitation, regeneration, and degradation. Figures with better resolution are in supplemental materials.
Land 11 01992 g004
Table
Table 1. Validation point methodologies used for LULCC analysis in Arauco Province (1976–2016).
Table 1. Validation point methodologies used for LULCC analysis in Arauco Province (1976–2016).
YearNumber of Validation PointsSource and Methodology for Assessment
1976200Digitized panchromatic aerial photographs.
2001300Native forest cadaster developed by CONAF [50], which corresponds to maps in vector format with a scale of 1:50,000.
2016350Fieldwork and Google Earth(c) maps for the areas of difficult access.
Table
Table 2. Major net changes observed from land cover/land use (LULCC) analysis in Arauco Province (1976–2016).
Table 2. Major net changes observed from land cover/land use (LULCC) analysis in Arauco Province (1976–2016).
PeriodLand Cover/Land UseNet Change DetailOverall Trend
1976–2001Native forestSubstituted by exotic plantations (77,000 ha)🔽
Degraded into grassland or shrublands (14,000 ha)🔽
Habilitated into agricultural lands (10,000 ha)🔽
Exotic plantations (pines and eucalyptus)Added surface from native forest (77,000 ha), agricultural lands (37,000 ha), shrublands (33,000 ha), and bare soil (4000 ha)🔼
Agricultural landsLoss of surface due to forestry (38,000 ha) and abandonment which convert them into shrublands (19,000 ha)🔽
Added surface from native forest (10,000 ha)🔼
Urban areasAdded surface in the populated areas and roads, accumulating surface from shrubland (577 ha), agricultural lands (477 ha), bare soil (127 ha) and urbanization (85 ha)🔼
2001–2016Native forestSubstitution of 58,000 ha for exotic plantations 🔽
Degradation of 11,000 ha of native forest into shrublands🔽
Converted into agricultural lands (8000 ha) 🔽
Exotic plantations (pines and eucalyptus)Added 59,000 ha converted from the native forest, 27,000 ha from shrublands, 22,000 ha from agricultural lands, and 720 ha from urban areas🔼
Agricultural landsHabilitated surface from shrublands (11,000 ha) and native forests (8000 ha) 🔼
Converted into exotic plantations (22,000 ha) and urban areas (500 ha)🔽
Urban areasExpanded its surface with housing development over shrublands (1146 ha), agricultural lands (466 ha), bare soil (518 ha), and wetlands (21 ha)🔼
Table Note: 🔼 means that the overall trend was upward, on the opposite, 🔽 the overall trend was downward.
Table
Table 3. Patch areas of native forest observed between 1976 and 2016 in Arauco Province between 1976–2016.
Table 3. Patch areas of native forest observed between 1976 and 2016 in Arauco Province between 1976–2016.
Patch Characteristics 1976 2001 2016 Overall Trend
Structure Typical structure of inverted J, which means that a dominance of small patches of > 1 ha. existedTypical structure of an inverted J. The increase of small patches in the period suggests the existence of native forest fragmentation processes. Typical structure of an inverted J. The decrease of small patches in the period suggests the existence of native forest deforestation. processes. 🔽
Number of patches between 1–100 ha7500 ha8500 ha6500 ha🔽
Number of patches between 100–1000 ha05 ha4 ha🔽
Number of patches between 5000–10,000 ha 3 ha1 ha0 🔽
Number of patches over 10,0003 ha0 0🔽
Table Note: 🔼 means that the overall trend was upward, on the opposite, 🔽 the overall trend was downward.
Table
Table 4. Indicators of spatial aggregation (%) of the LULCC observed in Arauco Province (1976–2016).
Table 4. Indicators of spatial aggregation (%) of the LULCC observed in Arauco Province (1976–2016).
LULCC 1976 2001 2016 Overall Trend
Bare soil 91.3%69.6%60.9%🔽
Shrubland 67.9%56.4%48.2%🔽
Agricultural lands 83.2%72.8%64.5%🔽
Native forest 76.5%73.0%68.0%🔽
Urban areas 77.4%82.4%83.8%🔼
Exotic plantations 77.7%81.3%90.3%🔼
Water bodies 95.7%94.3%95.0%=
Wetlands 93.6%81.3%59.3%🔽
Table Note: 🔼 means that the overall trend was upward, on the opposite, 🔽 the overall trend was downward, and = means that the trend remains the same.
Table
Table 5. The Adjacency Index of contact measured in ha comparing Native Forest and other LULCC patches from 1976 to 2016 for Arauco province.
Table 5. The Adjacency Index of contact measured in ha comparing Native Forest and other LULCC patches from 1976 to 2016 for Arauco province.
LULCC 1976 2001 2016 Overall Trend
Exotic plantations 578.9 1209.5 350.5 🔼
Shrubland 966.5 277.6 150.5 🔽
Agricultural lands 509.7 266.3 44.4 🔽
Urban areas 0.1 0.2 0.0 =
Bare soil 10.4 3.2 0.6 🔽
Wetlands 0.2 0.9 0.0 🔽
Water bodies 5.8 3.7 0.0 🔽
Table Note: 🔼 means that the overall trend was upward, on the opposite, 🔽 the overall trend was downward, and = means that the trend remains the same.
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Jaque Castillo, E.; Ojeda, C.G.; Fuentes Robles, R. Landscape Fragmentation at Arauco Province in the Chilean Forestry Model Context (1976–2016). Land 2022, 11, 1992. https://doi.org/10.3390/land11111992

AMA Style

Jaque Castillo E, Ojeda CG, Fuentes Robles R. Landscape Fragmentation at Arauco Province in the Chilean Forestry Model Context (1976–2016). Land. 2022; 11(11):1992. https://doi.org/10.3390/land11111992

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Jaque Castillo, Edilia, Carolina G. Ojeda, and Rodrigo Fuentes Robles. 2022. "Landscape Fragmentation at Arauco Province in the Chilean Forestry Model Context (1976–2016)" Land 11, no. 11: 1992. https://doi.org/10.3390/land11111992

APA Style

Jaque Castillo, E., Ojeda, C. G., & Fuentes Robles, R. (2022). Landscape Fragmentation at Arauco Province in the Chilean Forestry Model Context (1976–2016). Land, 11(11), 1992. https://doi.org/10.3390/land11111992

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