Next Article in Journal
The Impact of Green Business Ethics and Green Financing on Sustainable Business Performance of Industries in Türkiye: The Mediating Role of Corporate Social Responsibility
Previous Article in Journal
The Affordable Virtual Learning Technology of Sea Salt Farming across Multigenerational Users through Improving Fitts’ Law
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 17
10.3390/su16177866
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico

by
Rufino Sandoval-García
1,
Joel Rascón-Solano
2,*,
Eduardo Alanís-Rodríguez
3,
Samuel García-García
2,
José A. Sigala
4 and
Oscar Aguirre-Calderón
3
1
Forestry Department, Antonio Narro Autonomous Agrarian University, Calz Antonio Narro 1923, Buenavista, Saltillo 25315, CH, Mexico
2
Faculty of Agricultural and Forestry Sciences, Autonomous University of Chihuahua, 2.5 km on Delicias-Rosales Road, Delicias 33000, CI, Mexico
3
Faculty of Forestry Sciences, Autonomous University of Nuevo León, National Highway Nacional #85, km. 145, Linares 67700, NL, Mexico
4
National Institute of Forestry, Agricultural and Livestock Research, Guadiana Valley Experimental Field, km 4.5 Carretera Durango-El, Mezquital 34170, DG, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7866; https://doi.org/10.3390/su16177866
Submission received: 20 July 2024 / Revised: 29 August 2024 / Accepted: 3 September 2024 / Published: 9 September 2024
(This article belongs to the Section Sustainable Forestry)
Browse Figures
Review Reports Versions Notes

Abstract

:
This study evaluates and contrasts changes in vegetation cover over three decades in two forest areas in the State of Chihuahua in northern Mexico with different management statuses: one with sustainable forest management and the other protected as a Flora and Fauna Protection Area. The hypothesis proposed that both areas would have maintained or increased their vegetation cover since 1995. Satellite images from the periods 1995–2008, 2008–2014, 2014–2022, and 1995–2022 were analyzed. The results showed that Ejido El Largo y Anexos significantly increased forest areas and reduced grasslands due to sustainable management practices, with a notable expansion of pine–oak and pine forests. In contrast, the Tutuaca Flora and Fauna Protection Area experienced a notable loss of oak and oak–pine forests, suggesting ineffectiveness in its conservation policies. However, there was less loss in Douglas Fir forests, indicating some effective protection efforts. The comparison reveals opposing dynamics: while Ejido El Largo y Anexos demonstrates success in sustainable management, the Tutuaca Flora and Fauna Protection Area faces conservation challenges. In conclusion, this study highlights the need for active management approaches to maintain ecosystem cover and functionality.

1. Introduction

Forest loss is one of the main environmental issues threatening livelihoods worldwide [1]. Anthropogenic influence stands out as a primary cause of vegetation cover degradation [2], leading to deforestation, agricultural intensification, and urban expansion [3], resulting in significant changes in forest area, carbon sequestration, biodiversity loss, and a decline in ecosystem services [4].
Although global deforestation rates have decreased over the past decade, many localities and nations continue to experience increasing rates [1]. According to FAO (2020) [5], 420 million hectares of forest have been lost globally since 1990 due to land use change, with a deforestation rate of 10 million hectares per year between 2015 and 2020. In Mexico, original vegetation has drastically decreased, primarily due to conversion of forested lands to agricultural and livestock uses [6].
One response to forest area degradation is establishing conservation sites or protected natural areas. In this regard, Wrońska-Pilarek et al. (2023) and Nascibem et al. (2023) [7,8] suggest that integrating forest lands into conservation statuses increases woody species coverage and diversity, leading to reduced deforestation and increased carbon reservoirs. Various studies document how restoration actions and monitoring in conservation areas maintain or increase vegetation cover [9,10].
On the other hand, sustainable forest management ensures perpetual and optimal production of diverse goods and services from forest ecosystems [11]. Kucsicsa et al. (2020) [12] indicate that forest exploitation in Europe has facilitated forest species establishment and increased forest cover in previously bare areas. Moreover, Kouba et al. (2012) [13] highlight that long-term forest sustainability heavily depends on landscape conservation, as cover changes are driven by plantation introductions. Additionally, numerous studies have proposed using remote sensing data [14,15,16] and satellite image time series [17,18] to estimate forest productivity and cover dynamics.
Remote sensing and GIS techniques are widely used in the scientific community to quantitatively and qualitatively monitor and assess forest ecosystems [19,20], analyzing the spatiotemporal evolution of forested areas based on various satellite image ranges [21,22,23]. According to Hernández-Cavazos et al. (2023) [24], research evaluating land use and vegetation changes to quantify annual deforestation rates using GIS and remote sensing tools has increased in Mexico.
In Mexico, research has been carried out to evaluate land use change in temperate forests in Puebla and Michoacán, and the results indicate that there is a loss in vegetation cover [25,26]. In the case of natural protected areas and areas with forest management, research is scarce and indicates that the vegetation cover is maintained or increased [9]. However, the multitemporal dynamics of forest cover in managed and conservation-designated forests in the state of Chihuahua have not been previously evaluated. In this sense, understanding and quantifying multitemporal forest cover dynamics are crucial for revealing the interaction mechanisms between human activities and the natural environment [27,28,29]. This information will be of importance to officials and decision makers in the management and conservation of forests in Mexico, as it will provide a basis for determining the effectiveness of forests managed and designated for conservation.
In this study, we analyzed two contrasting areas according to their management. The first has been managed in natural forests since the beginning of the 20th century [30], and the second is a Flora and Fauna Protection Area, “Tutuaca”, designated in 1937, where no logging or land use change is allowed [31]. We aimed to evaluate and contrast the changes in vegetation cover over the last three decades in two forest areas with different management statuses: one with sustainable forest exploitation and the other designated for conservation. The hypothesis posits that both areas will have maintained or increased vegetation cover since 1995.

2. Materials and Methods

2.1. Study Area

The study area is located in the western part of the state of Chihuahua, Mexico, and encompasses two zones: Ejido El Largo y Anexos, situated between 28°44′55″ N, 29°57′10″ N and 108°05′50″ W, 108°42′10″ W, and the natural protected area (NPA), Tutuaca, designated as a Flora and Fauna Protection Area (FFPA), located between 28°08′40″N, 28°44′50″ N and 107°29′40″ W, 108°42′05″ W (Figure 1). The area has a sub-humid temperate climate (C(w1)x’) with an average annual temperature between 12 °C and 18 °C, eutric cambisol soils, an average annual precipitation of 616.5 mm, and elevations ranging from 1093 to 2822 m [32]. The ANP Tutuaca exhibits four types of climates across its extension, predominantly a semi-warm sub-humid climate ((A)C(wo)), with an average annual temperature above 18 °C, haplic phaeozem soils, an average annual precipitation of 775.5 mm, and altitudes varying from 783 to 2764 m [32].
In the natural protected area (NPA), a management program is implemented that includes various conservation subprograms. These subprograms encompass a wide range of interrelated activities that focus on conservation, protection, restoration, management, knowledge, and promotion of environmental culture. Each component of the management program complements and reinforces each other, establishing an integral basis for environmental policy in the ANP [31].
On the other hand, forest management in Ejido El Largo y Anexos is based on a detailed classification of the area according to the forest types present. Two main types of forest stands are identified, each managed with different silvicultural systems. The Regular Forest is managed using the Silvicultural Development Method, with a ten-year cutting cycle covering the period from 2018 to 2027. In contrast, the Irregular Forest is managed using the Control Method, with a fifteen-year cutting cycle set from 2018 to 2032. These differentiated management strategies allow for sustainable exploitation of forest resources, adapting to the specific characteristics of each forest type [30].

2.2. Justification for High-Resolution Satellite Image Classification

Most studies to determine land use change processes use Landsat (30 m/pixel) and Sentinel (10 m/pixel) satellite images, cataloged as low- and medium-resolution, respectively [33], which, by combining bands, relate the values relative to the color, tone, and intensity of radiation, grouping pixels with similar spectral characteristics present in the image, allowing to estimate the quantity, quality, and development of vegetation through the Normalized Difference Vegetation Index (NDVI), which requires stages of training, classification and precision analysis and verification of results. (See Figure 2).
However, this type of image presents a series of limitations due to the high percentage of cloud cover in certain periods, invalid data bands, and pixel overlap in two consecutive images, generating a margin of erroneous classification between 10 and 30% (Astola et al. 2019, [9,34]), while the use of high-resolution satellite images (<5 m/pixel) allows a better classification of the different land uses and vegetation because they can be manipulated in enhanced wavelet compression (ecw) format, which considerably reduces the file size while maintaining high graphic quality and a high degree of detail [35,36].

2.3. Image Acquisition

The orthophotos were obtained from the Espacios y Datos de México platform, and the high-resolution satellite images from Airbus Defence and Space, GeoEye-1, and Birdseye were obtained using SASPlanet software version 200606.10075 Stable. Both tools are open-source and allow for free visualization and image download. A total of 4 orthomosaics were generated, composed of 123 orthophotos with a resolution of 1.5 m/pixel (year 1995), 625 images from Airbus Defence and Space with a resolution of 1.14 m/pixel (year 2008), and 2632 images from GeoEye-1 and Birdseye with a resolution of 0.28 m/pixel (years 2014 and 2022). The evaluation of changes in vegetation cover was carried out starting from 1995 due to the availability of high-resolution satellite images.
The procedure described in our study follows a detailed methodology for the classification and analysis of satellite images, focused on the identification and monitoring of changes in land use and land cover. This process is developed in several stages, each with specific steps that ensure the accuracy and validity of the results.
First, high-resolution satellite images are acquired from reliable sources, such as SAS Planet and the database of the National Institute of Statistics and Geography (INEGI). These images form the basis of the analysis, providing essential visual and spatial data for the study.
Once the images are obtained, they undergo preprocessing, a crucial stage to ensure that the data are in optimal condition for subsequent classification. This preprocessing includes layer fusion, where different spectral bands are combined to improve image quality. Resolution fusion is also performed, integrating images of different spatial resolutions to obtain a final product with greater detail. Additionally, radiometric corrections are applied to adjust any distortion in pixel intensity caused by the atmosphere or the sensor. Finally, the images are cropped or subset to focus the analysis exclusively on the region of interest, optimizing resource use and improving the study’s precision.
The next step is the classification of the preprocessed images. This process is carried out using both unsupervised and supervised classification methods. In unsupervised classification, pixels are automatically grouped into similar classes based on their spectral properties without the need for training data. Then, the Normalized Difference Vegetation Index (NDVI) is calculated, a key indicator that helps identify areas of vegetation and assess their health. Subsequently, supervised classification is performed, requiring the selection of training sites. These sites are chosen with the help of topographic maps, soil maps, and tools like Google Earth Pro, allowing for more accurate pixel classification into specific categories.
To ensure the validity of the results, an accuracy assessment is conducted by comparing the classification results with reliable reference data. Additionally, field verification is carried out, where the studied areas are visited to corroborate the accuracy of the assigned categories.
Finally, a post-classification analysis is performed. This analysis includes the comparison of classified images at different points in time, allowing for the identification and quantification of changes in land use and land cover. The distribution of the different land use and land cover classes in the study region is determined, and the data are exported to a Geographic Information System (GIS) for further spatial analysis and thematic map generation. Through this analysis, changes in land use can be detected and documented, providing valuable information for land management and environmental planning.
This detailed and systematic procedure ensures that each step is carried out with precision, allowing the study to be replicated in other regions or different contexts, provided that the appropriate tools and data are available. The procedure used is shown in Figure 3.

2.4. Digitization of Images

Land use and vegetation were analyzed through an unsupervised classification using the “K-Means Cluster Analysis” (KMC) module. Subsequently, a supervised classification was performed using training sites from field data, executing a total of 10 automated classifications, which were summarized into 8 classes of land use and vegetation with the “Iterative Minimum Distance” multivariate data cluster analysis method [37]. All procedures were conducted using the QGIS 3.32 “Lima” software [38], which integrates the open-source System for Automated Geoscientific Analyses (SAGA) process toolbox under a General Public License. Information was generated for the following ecosystems: Douglas Fir forest, oak forest, oak–pine forest, pine forest, pine–oak forest, juniper forest, grassland, low deciduous forest, and areas devoid of vegetation [39]. This information was validated against historical land use data for the study area and field surveys.

2.5. Multitemporal Analysis

To calculate changes (increase or loss) in vegetation cover and land use, a cross-tabulation was generated for five different time periods: 1995–2008, 2008–2014, 2014–2021, and 1995–2022. The net change, rate of change, and relative percentage change were calculated for each type of vegetation cover or land use over time.

2.6. Determination of Losses and Gains in Coverage

The determination of loss or gain in coverage for different land uses and vegetation types was achieved by constructing four transition matrices and one change rate matrix for the periods. To determine the rate of change, the equation adapted by [40] was used.
δ n = s 2 s 1 1 n - 1   ×   100
where δ n = rate of change expressed as a percentage; s 1 = area at date 1; s 1 = area at date 2; and n = number of years between the two dates.
Net change was obtained from the difference in forest area between two points in time. When the net change result is positive, it indicates a general gain in forest area, and when it is negative, it indicates a general loss of forest area [41].
Net   change   in   forest   area   = gains   ( forest   expansion ) −   losses   ( deforestation )
For the calculation of the relative percentage change (ΔA%), the following equation was used [37]:
Δ A % = At 2 - At 1 At 1   ×   100
where ΔA% = relative percentage change; At2 A = vegetation cover or land use at the final time; and At1 is vegetation cover or land use at the initial time.

2.7. Annual Deforestation Rate

Changes in land cover were identified by comparing two sets of vegetation and land use maps, creating new maps that indicated the transitions between the study years. Using the data obtained from image processing, the annual deforestation rate was calculated, which involves comparing the land cover at the same site during two different time periods. This calculation utilized the equation proposed by [42], which is as follows:
r = 1 t 2 - t 1   ×   ln A 2 A 1   ×   100
where r = vegetation cover; A1 = vegetation cover or land use at the initial time; A2 = vegetation cover or l and use at the final time; t1 = initial period; and t2 = final period. A positive value of “r” indicates an increase in vegetation cover, while a negative value demonstrates a loss in coverage.

3. Results

3.1. Kappa Index

Maps representing land cover in the Ejido El Largo y Anexos and the Tutuaca Protected Natural Area, both located in the state of Chihuahua, were generated for the years 1995, 2008, 2014, and 2022. These maps served as baseline data for conducting a detailed analysis of the changes that occurred over this period (Figure 4). The image classification yielded average Kappa index values of 0.79, indicating a substantial level of accuracy in the process. It is worth noting that the Kappa index is a recognized indicator of image classification reliability, and a value of 0.79 is considered significant in terms of the precision achieved. This result suggests that the classification of land cover on the maps is consistent and reliable for supporting the analysis of changes over time.

3.2. Coverage of Different Ecosystems

The comparison of results between Ejido El Largo y Anexos and the Tutuaca Protected Natural Area (PNA) reveals divergent trends in the coverage of different ecosystems, highlighting both similarities and differences in land use changes and forest conservation. In Ejido El Largo y Anexos, there is a general trend of increasing forest and grassland areas. Among the main ecosystems, those showing the greatest recovery by 2022 were oak forests, which regained 9470.64 hectares (29%) of their 1995 coverage; followed by pine–oak forests, with a recovery of 8976.09 hectares (10.06%); and the pine forest, which regained 9044.57 hectares (8.98%).
This trend suggests a significant contribution from silvicultural treatments and sustainable ecosystem management. Activities within the ejido boundaries have effectively suppressed agricultural expansion, livestock grazing, urbanization, and forest degradation. Moreover, comprehensive restoration activities, continuous reforestation, and sustainable forest cultivation have reduced areas devoid of tree vegetation significantly. In contrast, the Tutuaca PNA shows a notable decrease in the coverage of certain forests, particularly the oak forest, which lost 50,627.88 hectares by 2022, representing an increase of 36.92% from its 1995 loss. This is followed by the oak–pine forest with a loss of 33,671.92 hectares (24.42%) and the pine forest with a decrease of 13,674.47 hectares (20.04%).
This reduction in coverage indicates possible degradation or inefficient conservation efforts within the protected area. However, the Douglas Fir forest exhibited a drastic expansion, reducing its annual loss from 242.17 hectares (0.27%) in 1995 to just 9.80 hectares in 2022 (0.00%), reflecting significant mitigation of degradation in this type of forest.
Comparing both territories highlights two contrasting dynamics: while Ejido El Largo y Anexos showed a trend of recovery in forest and grassland areas, the Tutuaca PNA exhibited a notable decrease in certain forests, especially oak. The increase in pine forests and grasslands in Ejido El Largo y Anexos suggests effective forest management and a decrease in overgrazing activities. On the other hand, the decrease in oak forests in Tutuaca suggests a relative failure in conservation efforts within the protected area. However, the significant mitigation of Douglas Fir forest reduction in Tutuaca indicates successful protection activities in this specific ecosystem (Table 1).
In Ejido El Largo and Anexos, the implementation of silvicultural treatments, continuous reforestation, and sustainable management practices have proven effective. Active community participation in forest conservation and restoration has been crucial in achieving these positive results. In contrast, despite focusing on conservation efforts, the decrease in vegetation cover in Tutuaca suggests that implemented strategies have not been sufficient to halt ecosystem degradation, especially in oak forests (Figure 5A).
Furthermore, the reduction in areas devoid of vegetation in Ejido El Largo y Anexos suggests effective control over agricultural, livestock, and urban expansion, allowing for the recovery of natural ecosystems (Figure 4A). Conversely, in Tutuaca ANP, the decline in forests could be related to increased anthropogenic pressures, such as illegal logging, unsustainable agriculture, and other environmental factors that have not been adequately predicted or mitigated (Figure 5B).
Continued efforts in restoration and reforestation in Ejido El Largo y Anexos have resulted in sustained recovery of forest cover, demonstrating a strong commitment to ecosystem conservation. In Tutuaca PNA, by contrast, the lack of effective reforestation and significant loss of forests like Douglas Fir indicate a need to review and strengthen conservation strategies. In 1995, the areas affected by vegetation loss in Ejido El Largo and Anexos covered 105,101.16 hectares. However, due to the implementation of integrated strategies, including sustainable forest management, sustainable soil forest management and restoration, continuous reforestation programs, and territorial development of the ejido, the affected area has decreased to 72,913.57 hectares by 2022. This reduction reflects the effectiveness of the interventions and policies adopted for the recovery of vegetation cover and mitigation of environmental degradation. Nevertheless, achieving complete restoration and preserving these ecosystems remains a priority challenge for the community.
Additionally, the degradation of unmanaged forests (Tutuaca PNA) can lead to reduced provision of ecosystem services and decreased health and productivity, impacting both nature and human well-being. Similarly, unmanaged forests are less resilient to climate change, including droughts, pest outbreaks, and wildfires. Figure 4A shows a significant loss in vegetation in the Tutuaca Protected Natural Area, starting in 1995, with a reduced vegetation cover of 88,553.85 hectares. By 2022, this loss increased substantially, reaching 203,870.10 hectares. Despite the conservation policies implemented, they have failed to halt the degradation, suggesting that the current strategies have not been effective in stopping the increasing vegetation loss in the area.

3.3. Changes in Vegetation Cover

Figure 6 illustrates the evolution of vegetation cover in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area (PNA) during the years 1995–2022. It shows a notable increase in grassland extent and a loss in oak forests and oak–pine mixed forests, especially in the Tutuaca ANP. In 1995, Ejido El Largo y Anexos was predominantly covered by pine and pine–oak forests, with a significant presence of temperate ecosystems. By 2008, oak and oak–pine forests had expanded their relative extent, reducing grasslands. This trend intensified by 2014, with a higher proportion of mixed forests and fewer grasslands. Finally, by 2022, there was a confirmed decrease in grassland areas and a clear predominance of pine and pine–oak forests. These changes reflect a significant transformation in land use and ecological dynamics in both regions. In this context, sustainable and adaptive forestry has proven to be an essential component in increasing tree cover in Ejido El Largo y Anexos. Additionally, restoration practices, including natural and assisted regeneration, promote the expansion of tree cover and the reduction of grasslands.
During the study period from 1995 to 2022, significant changes were observed in the rates of change of different ecosystem types in Ejido El Largo y Anexos, as well as in the Tutuaca Protected Natural Area (PNA). In Ejido El Largo y Anexos, there was a notable increase in oak forest area, with a relative change rate of 45.96%, indicating a deceleration in the loss of these ecosystems and an increase in their coverage. However, oak–pine and pine–oak mixed forests showed decreases of 19.63% and 21.38%, respectively, suggesting efforts to slow down their loss and increase their coverage have been effective.
In contrast, the Tutuaca PNA experienced significant decreases in oak (−223.12%) and pine (−161.79%) forest areas, indicating possible changes in the management and conservation of these ecosystems, resulting in accelerated and sustained loss. On the other hand, Douglas Fir forests showed a significant reduction in coverage loss, with a relative change rate indicating an increase in forest cover within the PNA, as shown in Figure 7. These results underscore the importance of actively monitoring and managing forest resources to maintain biodiversity and ecosystem functionality in the face of environmental and anthropogenic pressures. Additionally, it highlights that sustainably managed and adaptively managed forests, as seen in Ejido El Largo y Anexos, tend to increase vegetation cover.

3.4. Deforestation Rate

Figure 8 shows that in Ejido El Largo y Anexos, a decrease in deforestation is observed, primarily marked in oak and grassland ecosystems, with deforestation rates of 2.93% and 3.69%, respectively, followed by minor decreases in pine forests. In Tutuaca, significant forestation is highlighted in Douglas Fir forests (12.38%) and pine–oak forests (4.68%), while oak and pine forests showed significant loss of coverage, with deforestation rates of −4.81% and −3.96%, respectively.
The analysis reveals significant patterns in deforestation rates. In Ejido El Largo y Anexos, pine forests showed a considerable decrease in deforestation rates in 2022, with a marked reduction observed from 2014 to 2022. Pine–oak forests, on the other hand, exhibited the lowest deforestation related to tree cover loss, particularly in 2022, reflecting changes in management practices or environmental conditions. There was also a notable decrease in deforestation rates in grasslands, primarily reduced in 2014 and 2022.
In contrast, in Tutuaca (PNA), there was notable mitigation in the loss of Juniper and Douglas Fir forests, reducing impacts up to 9.03% in 2014 for the former and up to 22.00% in 2022 for the latter. However, there was a steady decline in oak coverage, with a significant rate in 2014 and 2022, and a similar trend was observed in pine and pine–oak forests during the same period. Lastly, the dry tropical forest showed the highest deforestation rate, with a decrease in coverage from 2014 to 2022, though with fluctuations in intermediate periods (Figure 9).
In Ejido El Largo y Anexos, there has been a significant increase in the total area of various types of forest ecosystems (p = 0.0001). In contrast, Tutuaca National Park (PNA) has experienced a loss in the covered area of several types of forest ecosystems. This comparison suggests that while Ejido El Largo y Anexos have achieved notable reductions in deforestation rates, PNA Tutuaca has seen a decrease in its forested area, likely due to conservation policies that restrict forest management and may lead to illegal logging, unsustainable agricultural practices, the degradation of vegetation cover, and an increase in the agricultural gap.

4. Discussion

4.1. Kappa Index

The classification of images in this study presented average Kappa index values of 0.79, according to Brennan and Prediger (1981) [43]. Cohen interpreted this Kappa value as the proportion of agreement among raters after accounting for chance agreement, indicating that a higher Kappa value implies greater agreement. However, several studies mention that the Kappa index has increasingly faced criticism due to its sensitivity to asymmetric distributions and inherent uncertainty [44,45,46,47]. Nevertheless, other authors suggest that Kappa values greater than 0.75 are considered highly accurate [9,24,48,49,50]. Therefore, despite criticisms regarding Kappa’s sensitivity, the value obtained in this study suggests high agreement and, consequently, reliable analysis of changes in vegetation cover. Interpretation of this result should consider both limitations highlighted by some studies and support from others validating Kappa’s use as a robust indicator of agreement and accuracy in complex classifications.

4.2. Coverage of Different Ecosystems

The comparison of results between Ejido El Largo y Anexos and Tutuaca National Park reveals divergent trends in the coverage of different ecosystems, highlighting both similarities and differences in land use changes and forest conservation. In Ejido El Largo, there is a notable expansion of ecosystems, where the pine–oak forest, which was the largest in 1995, covering 31.71% of the area, increased to 33.39% by 2022. The pine forest decreased in relative area from 28.46% in 1995 to 28.62% in 2022; however, the absolute area trended upwards. On the other hand, Tutuaca National Park shows a significant decrease in the coverage of certain forests, particularly oak forests. In 1995, the oak–pine forest had a relative coverage of 32.49% of the area, which grew to 36.75% by 2008, continuing this trend to reach 35.93% by 2022. However, the ecosystems show a significant decrease in their extent, reflecting loss of ecosystems and an increase in vegetation-free areas.
Other studies conducted in various geographic regions have focused on measuring land use change, where results similar to those described in this study have been identified. Cases such as the study of the Qena–Luxor governorates in Egypt, covering the period from 1984 to 2018, found that agricultural lands grew from an average of 123,870 ha (9.8%) in 1984 to 170,704 ha (13.40%) in 2018, and urban lands increased from 34,520 ha (2.7%) in 1984 to 44,528 ha (3.5%) in 2019. Moreover, lands recovering vegetation increased from approximately 437,970 ha in 1984 (34.4% of the total study area) to 452,105 ha in 2000 (35.50%); however, this classification was followed by a marked decrease to 437,351 ha (34.35%) between 2000 and 2010 and then increased to approximately 444,200 ha (34.89%) between 2010 and 2018. On the other hand, desert lands (limestone plateau and some lowland desert strips) decreased from 663,540 ha (52.2%) to 600,350 ha (47.15%) [51]. These studies reflect complex dynamics in land use change, where factors such as agricultural growth, urbanization, and vegetation recovery play crucial roles in different geographic contexts. The expansion of forests in Ejido El Largo and the loss in vegetation cover in Tutuaca National Park are comparable to the changes observed in agricultural and desert lands in Egypt.
Vivekananda et al. (2021) [52] identified land use changes from 1978 to 2018 in the Ananthapur district of Andhra Pradesh state in southern India. They found a marked spatial expansion of urban areas. In 1978, the area occupied by urbanized land was minimal (681 ha) and primarily located in the center of the study area, increasing to 3775 ha by 2018. Categories such as water bodies, forests, and agricultural lands exhibited a decrease in area, −73.04%, −31.00%, and −61.84%, respectively, while built-up lands and vegetation-free lands exhibited an increase in area, 454.33% and 104.70%, respectively. This study reflects a global trend in land use change, where urbanization and expansion of built-up areas are common, often at the expense of natural and agricultural areas; however, such a situation has not occurred in the study cases since they are rural regions where anthropogenic isolation leads to migration trends.
In a study conducted for the period 1999 to 2014 in the Ribeirão do Meio watershed in Brazil, a direct relationship was found between changes in land use, land cover, and verified environmental fragility classification. Land use in the watershed changed significantly, with sugarcane cultivation expanding notably from 20.11% to 58.96% of the total area. This growth was accompanied by a significant reduction in exposed soil, decreasing from 47.50% to 15.82%, and a decrease in permanent crops from 13.44% to 2.40%. Urbanization and rural settlements also increased, though to a lesser extent, while forest area slightly increased. Additionally, both mining and fish farming experienced small increases, while water resources marginally decreased from 0.38% to 0.36% [53].

4.3. Changes in Vegetation Cover

The findings of the PNA Tutuaca differ from those reported by Rosero et al. (2023) [10], who estimated spatiotemporal changes in landscape and fragmentation levels in the Llanganates National Park (PNL), a protected area in the Andean center of Ecuador. They recorded no significant difference in land cover change between 1991 and 2016 and found fragmentation levels to be low. Changes in land cover in the study area are not evident, as it is a protected area where ecosystems are generally expected to maintain their initial conditions over time. They concluded that biodiversity conservation and landscape processes in the PNL are effective.
Furthermore, the findings of the PNA Tutuaca differ from those obtained by [9], who conducted a multi-temporal analysis of the Cumbres de Monterrey National Park, Mexico, spanning from 2002 to 2018. The results indicate a gradual recovery of forest cover, mainly in pine, pine–oak, oak–pine, oak, and Douglas Fir forests, in response to resilience capacity and prioritization of ecological restoration strategies in these ecosystems.
The data obtained for the PNA Tutuaca are comparable to those achieved by [54], who studied land use change between 1984 and 2015 in the Metchie-Ngoum Forest Reserve in Cameroon. Their results indicated that 36.11% of forest cover suffered degradation, resulting in secondary forest cover, which also experienced cumulative loss in area. They mention that forest loss is related to oil palm expansion, population growth, urbanized areas, and settlement-related activities such as illegal logging.
In the Cortadera Regional Natural Park in Colombia, between 1986 and 2016, [25] evaluated the agricultural frontier and multi-temporality of vegetation cover. The results of this study are similar to those obtained in the PNA Tutuaca. A continuous decrease in dense non-woody grasslands of firm ground over time was identified (from 3604 ha in 1986 to 2531 ha in 2016). In contrast, the mosaic cover of grasslands and crops increased by 1447 ha over 30 years (from 1231 ha in 1986 to 2678 ha in 2016). It is concluded that changes in the distribution of vegetation cover types occur due to agricultural frontier expansion despite the declaration of the protected area.
Gallardo-Cruz et al. (2021) [26] mention that the effectiveness of a Protected Natural Areas system largely depends on the social matrix in which it occurs. Moreover, [54] mentions that forest cover loss is basically due to increased settled population and areas near Protected Natural Areas. Therefore, authors like Miller et al. (2011) [55] indicate that a paradigm shift incorporating new ideas in the conceptualization, design, and management of Protected Natural Areas that considers emerging environmental concerns and different socioeconomic contexts is important.
Although general trends in land use change and vegetation cover are similar in various regions, local specificities such as land management policies, environmental conditions, and socioeconomic factors play a crucial role in determining specific outcomes. The expansion of certain forest types in Ejido El Largo contrasts with the decrease in forest cover in ANP Tutuaca, as does the increase in sugarcane cultivation in the Ribeirão do Meio watershed, reflecting region-specific dynamics related to primary sector activities that are relevant and characteristic of each geographical region.
In a study conducted between 1996 and 2017 in the Haridwar and Laksar regions of India, Kumar et al. (2020) [56] found that orchard area decreased rapidly by 11,806.65 ha (9.82%) and permanently converted to urban and agricultural use due to urbanization and industrialization in the Haridwar region from 1996 to 2017. Meanwhile, other land use areas, such as urban land, grasslands, and hydrographic basins, increased by 3022.11 ha (2.51%), 10,088.82 ha (8.39%), and 5191.47 ha (4.32%), respectively. Terrestrial vegetation and water mass have been decreasing since 1996 by 12,856.41 ha (10.70%) and 802.63 ha (0.66%), respectively, over 21 years. In contrast, Tutuaca, being a Protected Natural Area, applies strict conservation policies limiting urban expansion and promoting the preservation of natural ecosystems. On the other hand, as in the case of Ejido El Largo, government subsidies and support programs for conservation and sustainable land management incentivize communities to maintain and expand forest areas instead of developing urbanized land.
In the particular case of Mexico, similar results have been obtained regarding changes in land use for predominantly forested lands. In the state of Michoacán, Mexico, 28.36% of temperate forests were lost over an 18-year period (1975–1993), with a deforestation rate of −1.8% [57]. Similar results were observed in the San Marcos River sub-basin, Puebla, Mexico, where, over a 24-year period (1976–2000), 62.65% of cloud forest was eliminated, mainly due to the introduction of crops such as coffee [58]. Meanwhile, in other regions of Mexico, such as Michoacán and Puebla, significant forest losses have been recorded due to deforestation caused by agriculture and other land uses. Ejido El Largo has managed to conserve and even expand its natural ecosystems through effective management and restoration strategies. These cases demonstrate the importance of appropriate forest policies, community participation, and integrated territorial planning to mitigate the negative effects of land use change in sensitive areas and promote sustainable development.

4.4. Deforestation Rate

Various studies attribute tree cover loss to timber forestry [59,60], multiple pressures from anthropogenic activities [9], urbanization growth [61,62,63], the abandonment of agricultural lands, and an increase in bare soils [64]. On the other hand, Oliveira-Andreoli et al. (2021) [53] mention that the application of environmental legislation could have a positive effect on land use change scenarios. In contrast to the negative effects observed in other studies, such as tree cover loss due to timber forestry and other anthropogenic activities, Ejido El Largo has implemented sustainable management practices. These practices not only protect the productivity of existing forests but also promote their expansion, as evidenced by the increase in pine and pine–oak forest areas and stabilization of forest cover.
Figueredo-Fernández et al. (2020) [65] indicated that managed pine forests in Guisa, Cuba, showed the greatest vegetation cover recovery. This demonstrates that forest management in that area has been effective, similar to what has been observed in Ejido El Largo. Similarly, in Linares, Nuevo León, Hernández-Cavazos et al. (2023) [24] observed that oak, oak–pine, and pine–oak forests had the lowest coverage in the municipality and experienced a decrease from 1995 to 2021. Additionally, they do not mention any type of timber forestry in these areas. This contrasts with findings in Ejido El Largo, where timber forestry appears to be an important strategy for conserving these types of vegetation.
Policies and perspectives in Mexico, national parks are established in sites with ecosystems of scenic beauty and historical, scientific, educational, and recreational value that conserve special flora and fauna and, above all, are suitable for tourism development (art. 50). In terms of zoning, there can be core zones for protection and restricted use and buffer zones with subzones for traditional use, public use, and recovery. The sustainable use of natural resources is not permitted (art. 47 bis 1).
Finally, it is worth mentioning that strict environmental regulations in PNA Tutuaca have played a relevant role in the loss of natural areas related to urbanization and other forms of unsustainable development. However, these regulations have not allowed maintaining the ecological integrity of the protected area and preventing tree cover loss, mainly in oak and oak–pine ecosystems. Likewise, strict land conservation leads to an increase in fuels, which leads to the presence of fires, pests, and diseases that decrease tree cover. Because the natural protected areas have so many legal restrictions, the inhabitants have no regulated alternatives for taking advantage of the flora or fauna, which leads them to do so illegally. This is the case, in addition to the fact that the natural protected areas have few human and infrastructure resources to be able to monitor the areas. It is important to evaluate the option of sustainably harvesting forest resources, as this would promote their proper management and generate legal, permanent, and well-paid jobs.
According to Rendón (2014), in order to carry out adequate management in the PNA, it is important to I) promote the participation of communities in the conservation and management of natural resources in protected natural areas, II) recover those ecosystems in these protected areas that present alterations; III) have scientific and technological knowledge that provides a solid basis for decision making regarding conservation, management, and the use of natural resources; and IV) promote the development of alternative productive activities to those traditionally carried out by the populations that inhabit these natural protected areas that allow them to improve their standard of living and, at the same time, make rational use of the resources of the natural protected areas.

5. Conclusions

The hypothesis proposed is partially rejected since one of the evaluated areas did not maintain or increase vegetative cover. On the one hand, the area with forest management in Ejido El Largo y Anexos has experienced a notable increase in the surface area of various types of forests and grasslands over the past three decades. Pine–oak and pine forests have shown significant expansion, while grasslands have significantly reduced, reflecting sustainable management of these arboreal ecosystems due to adaptive forest use activities and control over agricultural, livestock, and urban activities.
On the other hand, the Tutuaca National Protected Area (PNA) has shown a significant loss in oak and oak–pine forest cover. This decrease suggests that conservation and management policies implemented in the protected area have not been effective. However, the reduced loss of Douglas Fir forest highlights activities related to protecting sensitive ecosystems, emphasizing the need for more focused and adaptive conservation approaches.
The comparison between Ejido El Largo y Anexos and Tutuaca PNA reveals two opposite dynamics: while the Ejido has shown considerable forestation and expansion of sustainably managed ecosystems, the ANP has not been able to increase its forested area. These differences highlight the influence of management and conservation policies on vegetative cover dynamics. The results underscore the importance of actively managing forest resources to maintain cover, biodiversity, and ecosystem functionality in the face of environmental and anthropogenic pressures.

Author Contributions

Conceptualization, R.S.-G., J.R.-S. and E.A.-R.; Methodology, R.S.-G.; Formal analysis, E.A.-R., S.G.-G., J.A.S. and O.A.-C.; Investigation, R.S.-G. and J.R.-S.; Data curation, R.S.-G.; Writing—original draft, J.R.-S.; Writing—review & editing, E.A.-R., S.G.-G., J.A.S. and O.A.-C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bera, D.; Chatterjee, N.D.; Ghosh, S.; Dinda, S.; Bera, S.; Mandal, M. Assessment of forest cover loss and impacts on ecosystem services: Coupling of remote sensing data and people’s perception in the dry deciduous forest of West Bengal, India. J. Clean. Prod. 2022, 356, 131763. [Google Scholar] [CrossRef]
  2. Fang, L.; Wang, L.; Chen, W.; Sun, J.; Cao, Q.; Wang, S.; Wang, L. Identifying the impacts of natural and human factors on ecosystem service in the Yangtze and Yellow River Basins. J. Clean. Prod. 2021, 314, 127995. [Google Scholar] [CrossRef]
  3. Wang, Y.; Yang, F.; He, F. Reconstruction of Forest and Grassland Cover for the Conterminous United States from 1000 AD to 2000 AD. Remote Sens. 2023, 15, 3363. [Google Scholar] [CrossRef]
  4. Zhang, C.; Ye, Y.; Fang, X.; Li, H.; Wei, X. Synergistic Modern Global 1 Km Cropland Dataset Derived from Multi-Sets of Land Cover Products. Remote Sens. 2019, 11, 2250. [Google Scholar] [CrossRef]
  5. FAO. El Estado de los Bosques del Mundo 2020. Los Bosques, la Biodiversidad y las Personas; FAO: Rome, Italy, 2020; 197p. [Google Scholar]
  6. CONAFOR. Estimación de la Tasa de Deforestación Bruta en México para el Período 2001–2018 Mediante el Método de Muestreo. Documento Técnico; ONAFOR: Jalisco, Mexico, 2020. [Google Scholar]
  7. Wrońska-Pilarek, D.; Rymszewicz, S.; Jagodziński, A.M.; Gawryś, R.; Dyderski, M.K. Temperate forest understory vegetation shifts after 40 years of conservation. Sci. Total Environ. 2023, 895, 165164. [Google Scholar] [CrossRef]
  8. Nascibem, F.G.; Da Silva, R.F.B.; Viveiro, A.A.; Gonçalves Junior, O. The Role of Private Reserves of Natural Heritage (RPPN) on natural vegetation dynamics in Brazilian biomes. Land Use Policy 2023, 132, 106820. [Google Scholar] [CrossRef]
  9. Sandoval-García, R.; González-Cubas, R.; Jiménez-Pérez, J. Análisis multitemporal del cambio en la cobertura del suelo en la Mixteca Alta Oaxaqueña. Rev. Mex. Cienc. For. 2021, 12, 96–121. [Google Scholar] [CrossRef]
  10. Rosero, C.; Otero, X.; Bravo, C.; Frey, C. Multitemporal Incidence of Landscape Fragmentation in a Protected Area of Central Andean Ecuador. Land 2023, 12, 500. [Google Scholar] [CrossRef]
  11. Aguirre-Calderón, O.A. Manejo forestal en el siglo XXI. Madera Bosques 2015, 21, 17–28. [Google Scholar] [CrossRef]
  12. Kucsicsa, G.; Bălteanu, D. The influence of man-induced land-use change on the upper forest limit in the Romanian Carpathians. Eur. J. For. Res. 2020, 139, 893–914. [Google Scholar] [CrossRef]
  13. Kouba, Y.; Alados, C.L. Spatio-temporal dynamics of Quercus faginea forests in the Spanish Central Pre-Pyrenees. Eur. J. For. Res. 2012, 131, 369–379. [Google Scholar] [CrossRef]
  14. Gopalakrishnan, R.; Kauffman, J.S.; Fagan, M.E.; Coulston, J.W.; Thomas, V.A.; Wynne, R.H.; Fox, T.R.; Quirino, V.F. Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery. Forests 2019, 10, 234. [Google Scholar] [CrossRef]
  15. Solberg, S.; Kvaalen, H.; Puliti, S. Age-independent site index mapping with repeated single-tree airborne laser scanning. Scand. J. For. Res. 2019, 34, 763–770. [Google Scholar] [CrossRef]
  16. Bontemps, J.-D.; Bouriaud, O. Predictive approaches to forest site productivity: Recent trends, challenges and future perspectives. For. Int. J. For. Res. 2014, 87, 109–128. [Google Scholar] [CrossRef]
  17. Goodbody, T.R.H.; Coops, N.C.; Luther, J.E.; Tompalski, P.; Mulverhill, C.; Frizzle, C.; Fournier, R.; Furze, S.; Herniman, S. Airborne laser scanning for quantifying criteria and indicators of sustainable forest management in Canada. Can. J. For. Res. 2021, 51, 972–985. [Google Scholar] [CrossRef]
  18. Tompalski, P.; Coops, N.C.; White, J.C.; Wulder, M.A.; Pickell, P.D. Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series. Can. J. Remote Sens. 2015, 41, 232–245. [Google Scholar] [CrossRef]
  19. Blaga, L.; Ilieș, D.C.; Wendt, J.A.; Rus, I.; Zhu, K.; Dávid, L.D. Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery. Remote Sens. 2023, 15, 3168. [Google Scholar] [CrossRef]
  20. Li, C.; Li, M.; Li, Y. Improving estimation of forest aboveground biomass using Landsat 8 imagery by incorporating forest crown density as a dummy variable. Can. J. For. Res. 2019, 50, 390–398. [Google Scholar] [CrossRef]
  21. Wu, L.; Li, Z.; Liu, X.; Zhu, L.; Tang, Y.; Zhang, B.; Xu, B.; Liu, M.; Meng, Y.; Liu, B. Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland. Remote Sens. 2020, 12, 341. [Google Scholar] [CrossRef]
  22. Elhag, M.; Boteva, S.; Al-Amri, N. Forest cover assessment using remote-sensing techniques in Crete Island, Greece. Open Geosci. 2021, 13, 345–358. [Google Scholar] [CrossRef]
  23. Erfanifard, Y.; Lotfi Nasirabad, M.; Stereńczak, K. Assessment of Iran’s Mangrove Forest Dynamics (1990–2020) Using Landsat Time Series. Remote Sens. 2022, 14, 4912. [Google Scholar] [CrossRef]
  24. Hernández-Cavazos, M.C.; Sandoval-García, R.; Molina-Guerra, V.M.; Alanís-Rodríguez, E. Análisis multitemporal del cambio de uso de suelo en el municipio de Linares, Nuevo León. Ecosistemas Recur. Agropecu. 2023, 10. [Google Scholar] [CrossRef]
  25. Anzoategui, L.V.; Gil-Leguizamón, P.A.; Sanabria-Marin, R. Frontera agrícola y multitemporalidad de cobertura vegetal en Páramo del Parque Regional Natural Cortadera (Boyacá, Colombia). Bosque 2023, 44, 159–170. [Google Scholar] [CrossRef]
  26. Gallardo-Cruz, J.A.; Peralta-Carreta, C.; Solórzano, J.V.; Fernández-Montes de Oca, A.I.; Nava, L.F.; Kauffer, E.; Carabias, J. Deforestation and trends of change in protected areas of the Usumacinta River basin (2000–2018), Mexico and Guatemala. Reg. Environ. Change 2021, 21, 97. [Google Scholar] [CrossRef]
  27. Gao, J.; O’Neill, B.C. Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nat. Commun. 2020, 11, 2302. [Google Scholar] [CrossRef]
  28. Liang, X.; Guan, Q.; Clarke, K.C.; Liu, S.; Wang, B.; Yao, Y. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Comput. Environ. Urban Syst. 2021, 85, 101569. [Google Scholar] [CrossRef]
  29. Zhang, J.; Li, X.; Zhang, C.; Yu, L.; Wang, J.; Wu, X.; Hu, Z.; Zhai, Z.; Li, Q.; Wu, G.; et al. Assessing spatiotemporal variations and predicting changes in ecosystem service values in the Guangdong–Hong Kong–Macao Greater Bay Area. GIScience Remote Sens. 2022, 59, 184–199. [Google Scholar] [CrossRef]
  30. Estrada-Murrieta, Ó.; Rodríguez-García, S.G. Más de 100 Años de Cultivo al Bosque en Chihuaua. Caso Ejido El Largo y Anexos; Dirección Técnica Forestal de Ejido El Largo y Anexos: Chihuahua, Mexico, 2022; 143p. [Google Scholar]
  31. CONANP. Programa de Manejo Área de Protección de Flora y Fauna Tutuaca; Comisión Nacional de Áreas Naturales Protegidas: Delegación Tlalpan, Mexico, 2014; 162p. [Google Scholar]
  32. Cuervo-Robayo, A.P.; Téllez-Valdés, O.; Gómez-Albores, M.A.; Venegas-Barrera, C.S.; Manjarrez, J.; Martínez-Meyer, E. An update of high-resolution monthly climate surfaces for Mexico. Int. J. Climatol. 2014, 34, 2427–2437. [Google Scholar] [CrossRef]
  33. EOS (Earth Observing System). Spatial Resolution of a Satellite Image. 2020. Available online: https://eos.com/ (accessed on 10 March 2024).
  34. Astola, H.; Häme, T.; Sirro, L.; Molinier, M.; Kilpi, J. Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal región. Remote Sens. Environ. 2019, 223, 257–273. [Google Scholar] [CrossRef]
  35. Pham, L.T.; Brabyn, L.; Ashraf, S. Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach. Int. J. Appl. Earth Obs. Geoinf. 2016, 50, 187–197. [Google Scholar] [CrossRef]
  36. Azzari, G.; Jain, M.; Lobell, D.B. Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries. Remote Sens. Environ. 2017, 202, 129–141. [Google Scholar] [CrossRef]
  37. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer: New York, NY, USA, 2009. [Google Scholar] [CrossRef]
  38. QGIS Development Team. QGIS Geographic Information System. Available online: https://qgis.org (accessed on 10 March 2024).
  39. INEGI. Conjunto de Datos Vectoriales de Uso del Suelo y Vegetación. Escala 1:250, 000. Serie VII (Conjunto Nacional); INEGI: Aguascalientes, Mexico, 2021. [Google Scholar]
  40. Palacio-Prieto, J.L.; Sánchez-Salazar, T.M.; Casado-Izquierdo, J.M.; Propin-Frejomil, E.; Delgado-Campos, J.; Velázquez-Montes, A.; Chias-Becerril, L.; Ortiz-Álvarez, M.I.; González-Sánchez, J.; Negrete-Fernández, G.; et al. Indicadores para la Caracterización y el Ordenamiento Territorial; Instituto Nacional de Ecología: Coyoacán, Mexico, 2004. [Google Scholar]
  41. Meshesha, T.W.; Tripathi, S.; Khare, D. Analyses of land use and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa Watershed Northern Central Highland of Ethiopia. Model. Earth Syst. Environ. 2016, 2, 1–12. [Google Scholar] [CrossRef]
  42. Puyravaud, J.-P. Standardizing the calculation of the annual rate of deforestation. For. Ecol. Manag. 2003, 177, 593–596. [Google Scholar] [CrossRef]
  43. Brennan, R.L.; Prediger, D.J. Coefficient kappa: Some uses, misuses, and alternatives. Educ. Psychol. Meas. 1981, 41, 687–699. [Google Scholar] [CrossRef]
  44. Feizizadeh, B.; Darabi, S.; Blaschke, T.; Lakes, T. QADI as a New Method and Alternative to Kappa for Accuracy Assessment of Remote Sensing-Based Image Classification. Sensors 2022, 22, 4506. [Google Scholar] [CrossRef]
  45. Feizizadeh, B.; Blaschke, T.; Tiede, D.; Moghaddam, M.H.R. Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes. Geomorphology 2017, 293, 240–254. [Google Scholar] [CrossRef]
  46. Kvålseth, T.O. Measurement of Interobserver Disagreement: Correction of Cohen’s Kappa for Negative Values. J. Probab. Stat. 2015, 2015, 751803. [Google Scholar] [CrossRef]
  47. Foody, G.M. Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sens. Environ. 2020, 239, 111630. [Google Scholar] [CrossRef]
  48. Verma, P.; Raghubanshi, A.; Srivastava, P.K.; Raghubanshi, A. Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Model. Earth Syst. Environ. 2020, 6, 1045–1059. [Google Scholar] [CrossRef]
  49. Jiménez-Pérez, J.; Sandoval-García, R.; Alanís-Rodríguez, E.; Yerena-Yamallel, J.I.; Aguirre-Calderón, O.A. Dinámica de cambio en ecosistemas urbanos y periurbanos en el área metropolitana de Monterrey, México. Rev. Cuba. Cienc. For. 2022, 10, 278–291. [Google Scholar]
  50. Mendes, D.F.; Da Silva, S.; Ferreri, J.; Dos Santos, A.; García, R.F. Acurácia temática do classificador por máxima verossimilhança em imagem de alta resolução espacial do satélite Geoeye-1. Nucleus 2015, 12, 107–118. [Google Scholar] [CrossRef]
  51. Kamel, M. Monitoring of Land Use and Land Cover Change Detection Using Multi-temporal Remote Sensing and Time Series Analysis of Qena-Luxor Governorates (QLGs), Egypt. J. Indian Soc. Remote Sens. 2020, 48, 1767–1785. [Google Scholar] [CrossRef]
  52. Raj, S.; Rawat, K.S.; Tripathi, V.K. Multi-Temporal Image Processing for LULC Classification and Change Detection. Env. Ecol. 2024, 42, 1349–1357. [Google Scholar] [CrossRef]
  53. Oliveira-Andreoli, E.Z.; Moraes, M.C.P.d.; Faustino, A.d.S.; Vasconcelos, A.F.; Costa, C.W.; Moschini, L.E.; Melanda, E.A.; Justino, E.A.; Di Lollo, J.A.; Lorandi, R. Multi-temporal analysis of land use land cover interference in environmental fragility in a Mesozoic basin, southeastern Brazil. Groundw. Sustain. Dev. 2021, 12, 100536. [Google Scholar] [CrossRef]
  54. Meli Fokeng, R.; Gadinga Forje, W.; Meli Meli, V.; Nyuyki Bodzemo, B. Multi-temporal forest cover change detection in the Metchie-Ngoum Protection Forest Reserve, West Region of Cameroon. Egypt. J. Remote Sens. Space Sci. 2020, 23, 113–124. [Google Scholar] [CrossRef]
  55. Miller, T.R.; Minteer, B.A.; Malan, L.-C. The new conservation debate: The view from practical ethics. Biol. Conserv. 2011, 144, 948–957. [Google Scholar] [CrossRef]
  56. Kumar, S.; Shwetank; Jain, K. A Multi-Temporal Landsat Data Analysis for Land-use/Land-cover Change in Haridwar Region using Remote Sensing Techniques. Procedia Comput. Sci. 2020, 171, 1184–1193. [Google Scholar] [CrossRef]
  57. Bocco, G.; Mendoza, M.; Masera, O.R. La dinámica del cambio del uso del suelo en Michoacán. Una propuesta metodológica para el estudio de los procesos de deforestación. Investig. Geogr. 2001, 1, 18–36. [Google Scholar] [CrossRef]
  58. Castelán Vega, R.; Ruiz Careaga, J.; Linares Fleites, G.; Pérez Avilés, R.; Tamariz Flores, V. Dinámica de cambio espacio-temporal de uso del suelo de la subcuenca del río San Marcos, Puebla, México. Investig. Geogr. 2007, 64, 75–89. [Google Scholar] [CrossRef]
  59. Rascón Solano, J.; Galván Moreno, V.S.; Aguirre Calderón, O.A.; García García, S.A. Caracterización estructural y carbono almacenado en un bosque templado frío censado en el noroeste de México. Rev. Mex. Cienc. For. 2022, 13, 136–165. [Google Scholar] [CrossRef]
  60. García García, S.A.; Alanís Rodríguez, E.; Aguirre Calderón, O.A.; Treviño Garza, E.J.; Graciano Ávila, G. Regeneración y estructura vertical de un bosque de Pseudotsuga menziesii (Mirb.) Franco en Chihuahua, México. Rev. Mex. Cienc. For. 2020, 11, 92–111. [Google Scholar] [CrossRef]
  61. Duan, Y.; Wang, X.; Wei, Y. Land use change analysis of Daishan Island using multi-temporal remote sensing imagery. Arab. J. Geosci. 2020, 13, 741. [Google Scholar] [CrossRef]
  62. Azari, M.; Billa, L.; Chan, A. Multi-temporal analysis of past and future land cover change in the highly urbanized state of Selangor, Malaysia. Ecol. Process. 2022, 11, 2. [Google Scholar] [CrossRef]
  63. Dangulla, M.; Manaf, L.A.; Mohammad, F.R. Spatio-temporal analysis of land use/land cover dynamics in Sokoto Metropolis using multi-temporal satellite data and Land Change Modeller. Indones. J. Geogr. 2020, 52, 306–316. [Google Scholar] [CrossRef]
  64. Capolupo, A.; Monterisi, C.; Saponaro, M.; Tarantino, E. Multi-Temporal Analysis of Land Cover Changes Using Landsat Data through Google Earth Engine Platform; SPIE: Bellingham, WA, USA, 2020; Volume 11524. [Google Scholar]
  65. Figueredo Fernández, J.L.; Ramon Puebla, A.M.; Barrero Medel, H. Análisis multitemporal del cambio de cobertura vegetal en el área de manejo “Los Números” Guisa, Granma. Rev. Cuba. Cienc. For. 2020, 8, 1–15. [Google Scholar]
Sustainability 16 07866 g001
Figure 1. Location of the study area.
Figure 1. Location of the study area.
Sustainability 16 07866 g001
Sustainability 16 07866 g002
Figure 2. Visual comparison of Landsat images, 30 m/pixel; bands 6,5,4 (A), Sentinel, 10 m/pixel; bands 4,3,2 (B), Orthophotos, 1.5 m/pixel (C) and Birdseye, 0.28 m/pixel (D).
Figure 2. Visual comparison of Landsat images, 30 m/pixel; bands 6,5,4 (A), Sentinel, 10 m/pixel; bands 4,3,2 (B), Orthophotos, 1.5 m/pixel (C) and Birdseye, 0.28 m/pixel (D).
Sustainability 16 07866 g002
Sustainability 16 07866 g003
Figure 3. Flowchart of the methodological framework applied in the study.
Figure 3. Flowchart of the methodological framework applied in the study.
Sustainability 16 07866 g003
Sustainability 16 07866 g004
Figure 4. Orthomosaics of Ejido El Largo y Anexos and the Tutuaca Protected Natural Area, Chihuahua, for the years 1995 (A), 2008 (B), 2014 (C), and 2022 (D).
Figure 4. Orthomosaics of Ejido El Largo y Anexos and the Tutuaca Protected Natural Area, Chihuahua, for the years 1995 (A), 2008 (B), 2014 (C), and 2022 (D).
Sustainability 16 07866 g004
Sustainability 16 07866 g005
Figure 5. Vegetation cover in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area.
Figure 5. Vegetation cover in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area.
Sustainability 16 07866 g005
Sustainability 16 07866 g006
Figure 6. Evolution of vegetation cover in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area for the years 1995 (A), 2008 (B), 2014 (C), and 2022 (D).
Figure 6. Evolution of vegetation cover in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area for the years 1995 (A), 2008 (B), 2014 (C), and 2022 (D).
Sustainability 16 07866 g006
Sustainability 16 07866 g007
Figure 7. Relative change in land cover in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area (1995–2022).
Figure 7. Relative change in land cover in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area (1995–2022).
Sustainability 16 07866 g007
Sustainability 16 07866 g008
Figure 8. Contrast of the overall deforestation rates between Ejido El Largo y Anexos and the Tutuaca Protected Natural Area.
Figure 8. Contrast of the overall deforestation rates between Ejido El Largo y Anexos and the Tutuaca Protected Natural Area.
Sustainability 16 07866 g008
Sustainability 16 07866 g009
Figure 9. Deforestation rates by ecosystem type over the period from 1995 to 2022 in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area.
Figure 9. Deforestation rates by ecosystem type over the period from 1995 to 2022 in Ejido El Largo y Anexos and the Tutuaca Protected Natural Area.
Sustainability 16 07866 g009
Table 1. Vegetation-devoid surface by ecosystem in the Ejido El Largo y Anexos and the Tutuaca Protected Natural Area in the Years 1995, 2008, 2014, and 2022.
Table 1. Vegetation-devoid surface by ecosystem in the Ejido El Largo y Anexos and the Tutuaca Protected Natural Area in the Years 1995, 2008, 2014, and 2022.
Ecosystem1995200820142022
Ha%Ha%Ha%Ha%
Ejido El Largo y Anexos
Oak23,97222.8119,32520.3216,23718.3814,50119.89
Oak–Pine13,96913.2913,42114.1112,97214.6911,23015.40
Pine29,91528.4625,70827.0324,64327.9020,87128.62
Pine–Oak33,32331.7132,98634.6931,75535.9524,34733.39
Grassland39223.7336573.8527163.0819652.69
Summation105,101100.0095,097100.0088,323100.0072,914100.00
Protected Natural Area Tutuaca
Douglas Fir2420.271840.13560.03100.00
Oak22,62125.5551,98136.7560,35436.3873,24935.93
Oak–Pine28,77032.4942,82430.2850,29630.3262,44230.63
Pine84619.5514,37310.1617,89610.7922,13510.86
Pine–Oak15,88017.9318,64813.1922,87913.7926,62113.06
Juniper70.0180.0150.0020.00
Grassland11,08512.5211,5478.1612,3747.4616,4138.05
Tropical deciduous forest14881.68 18661.3220441.2329981.47
Summation88,554100.00141,430100.00165,904100.00203,870100.00
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sandoval-García, R.; Rascón-Solano, J.; Alanís-Rodríguez, E.; García-García, S.; Sigala, J.A.; Aguirre-Calderón, O. Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico. Sustainability 2024, 16, 7866. https://doi.org/10.3390/su16177866

AMA Style

Sandoval-García R, Rascón-Solano J, Alanís-Rodríguez E, García-García S, Sigala JA, Aguirre-Calderón O. Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico. Sustainability. 2024; 16(17):7866. https://doi.org/10.3390/su16177866

Chicago/Turabian Style

Sandoval-García, Rufino, Joel Rascón-Solano, Eduardo Alanís-Rodríguez, Samuel García-García, José A. Sigala, and Oscar Aguirre-Calderón. 2024. "Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico" Sustainability 16, no. 17: 7866. https://doi.org/10.3390/su16177866

APA Style

Sandoval-García, R., Rascón-Solano, J., Alanís-Rodríguez, E., García-García, S., Sigala, J. A., & Aguirre-Calderón, O. (2024). Multitemporal Analysis of Land Cover Changes in Areas with Contrasted Forest Management and Conservation Policies in Northern Mexico. Sustainability, 16(17), 7866. https://doi.org/10.3390/su16177866

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop
  NODES
Association 2
chat 1
coding 2
Community 4
games 2
games 2
Idea 3
idea 3
innovation 2
Interesting 2
Intern 30
iOS 6
Javascript 2
languages 2
mac 29
Note 9
OOP 2
os 336
server 1
text 9
todo 2
Training 4
twitter 1
Users 1
visual 4
web 2