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Article

Monitoring Forest Disturbances and Associated Driving Forces in Guangdong Province Using Long-Term Landsat Time Series Images

by
Lin Qiu
1,2,3,
Zhongbing Chang
1,2,3,
Xiaomei Luo
1,2,3,
Songjia Chen
4,
Jun Jiang
4 and
Li Lei
1,2,3,*
1
Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510663, China
2
Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Guangzhou 510663, China
3
Guangdong Science and Technology Collaborative Innovation Center for Natural Resources, Guangzhou 510663, China
4
Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(1), 189; https://doi.org/10.3390/f16010189
Submission received: 26 November 2024 / Revised: 8 January 2025 / Accepted: 9 January 2025 / Published: 20 January 2025
(This article belongs to the Section Urban Forestry)
Browse Figures
Figure 1
<p>Location of the study area and spatial distribution of forests from GlobeLand30 (2020).</p> "> Figure 2
<p>Research technology roadmap.</p> "> Figure 3
<p>Construction process of forest subdivision process model (The red square is an example of an eight-neighborhood).</p> "> Figure 4
<p>Disturbance results for three typical areas ((<b>a</b>–<b>c</b>) were three representative areas).</p> "> Figure 5
<p>Result of centroid analysis in the spatial process of forest subdivision.</p> "> Figure 6
<p>Correlation coefficients between the area of forest disturbance and various factors.</p> ">
Versions Notes

Abstract

:
Research on monitoring forest disturbances and analyzing its driving factors is crucial for the sustainable management of forest ecosystems. To quantitatively identify the spatial distribution and dynamic changes of forest disturbance and its driving factors in Guangdong Province from 1990 to 2019, the long-term Landsat time series imagery and the LandTrendr change detection algorithm were utilized. The impact of forest disturbances on four types of landscape fragmentation (attrition, perforation, shrinkage, and subdivision) was analyzed using the Forman index. The Geodetector model was used to analyze the driving factors of forest disturbance from human activity and the natural environment. The results showed that the LandTrendr algorithm achieved a Kappa coefficient of 0.79, with an overall accuracy of approximately 82.59%. The findings indicate a consistent increase in shrinkage patches, both in quantity and area. Spatially, the centroids of forest fragmentation processes exhibited a clear inland migration trend, reflecting the growing ecological pressures faced by inland forest ecosystems. Furthermore, interactions among driving factors, particularly between population density and economic factors, significantly amplified their combined impacts. The correlation between forest disturbances and socio-economic factors revealed distinct regional variations, highlighting significant differences in forest disturbance dynamics across cities with varying levels of economic development. This study provides critical insights into the spatiotemporal dynamics of forest disturbances under rapid urbanization and economic development. It lays the groundwork for sustainable forest management strategies in Guangdong Province and may contribute to global discussions on managing forest ecosystems during periods of rapid socio-economic transformation.

1. Introduction

Forests play a central role in the exchange of matter and energy among soil, water, and the atmosphere, serving as essential components of the Earth’s biosphere. They are vital for regulating ecosystem cycles and significantly contribute to mitigating global climate change [1,2,3]. Forest disturbance is any discrete event that influences the structure, species composition, and resources available [4], and is considered a primary driver of forest heterogeneity [5]. Evidence suggests temporal changes in forest disturbance regimes across all continents, which might be critically altering forest ecosystems, biodiversity, functions, and services [6,7,8]. Exploring forest disturbance is critical to the sustainable development of forest resources and global ecosystems.
In the past, remote sensing techniques for forest change detection employed bi-temporal approaches based on images acquired before and after disturbance events [9]. The entire historic Landsat archive has been made available to the public since 2008 [10]. This has been accompanied by the development of automated change detection algorithms based on the Landsat time series such as Landsat-based Detection of Trends in Disturbance and Recovery or LandTrendr [11], Vegetation Change Tracker or VCT [12], and Breaks For Additive Season and Trend or BFAST [13]. Of these, the ability of the LandTrendr segmentation algorithm to detect historical forest disturbance has been demonstrated in various contexts [9,14,15,16,17]. An advantage of this algorithm is that the fitted vertices allow detection of both abrupt disturbance events as well as long-duration disturbance. Furthermore, this algorithm effectively filters out noise within long-term spectral trajectories without the need for a predefined trajectory model, thus proving advantageous for monitoring long-term forest dynamics [18].
Forest disturbance is a complex ecological phenomenon. Severe disturbances can significantly disrupt vegetation cover and growth conditions, intensifying forest landscape fragmentation, leading to biodiversity loss, and reducing ecosystem services. These impacts pose substantial threats to ecosystem stability and functionality [1,2]. In landscape ecology, landscape fragmentation is considered as a temporal course that involves five distinct and spatially explicit processes—perforation, dissection, subdivision, shrinkage, and attrition [19]. These spatially explicit processes have different spatial attributes, and they significantly impact the characteristics of the landscape pattern and ecological processes [19,20]. However, previous studies predominantly relied on landscape metrics to indirectly describe landscape patterns and structures. Metrics such as patch area, number of patches, landscape division, diversity index, and patch density were often used to quantify the degree of landscape fragmentation [21,22,23]. In contrast, the spatially explicit and visually apparent processes of landscape fragmentation have received relatively little attention in the literature.
Forest disturbance refers to events driven by human activities or natural forces, including but not limited to climate change, pest outbreaks, wildfires, storms, and direct human interventions. Among these, rapid urbanization and intensified human activities—such as deforestation, urban expansion, agricultural encroachment, and infrastructure development—have increasingly disrupted forest ecosystems, often with severe consequences [24,25]. While considerable research has explored the drivers of forest disturbances, the complex interactions among these factors, which collectively shape the structure and function of forest ecosystems, remain poorly understood [26]. In particular, the interdependencies and synergies among multiple drivers are often underrepresented in existing studies.
Guangdong Province offers a compelling case for studying forest disturbance dynamics, given its unique combination of ecological and socio-economic conditions. Situated in the southernmost region of mainland China, Guangdong is home to diverse and abundant forest resources, including subtropical evergreen broadleaf forests, subtropical evergreen monsoon rainforests, and tropical evergreen monsoon rainforests [27], which contribute significantly to regional and global ecosystem functions. As one of the earliest regions in China to undergo reform and opening-up, Guangdong has experienced rapid socio-economic development. The province’s population nearly doubled from approximately 63.47 million in 1990 to about 126 million in 2019, while its GDP surged from RMB 155.9 billion to RMB 10,798.69 billion—an astonishing 69-fold increase over the same period [28,29]. These transformations have resulted in significant land-use changes and heightened human pressures on forest ecosystems. Such disturbances not only alter forest structure and function but also pose substantial risks to ecological balance and the delivery of vital ecosystem services. Despite its importance, there has been little investigation into the spatiotemporal patterns of forest disturbances and the associated driving factors in Guangdong Province during this transformative period. The lack of research on how rapid urbanization has influenced forest landscape fragmentation processes and their underlying drivers represents a critical knowledge gap.
To address this, the present study examined the spatiotemporal dynamics of forest disturbances and the resulting landscape fragmentation in Guangdong Province from 1990 to 2019. Specifically, it aimed to analyze the driving mechanisms behind forest disturbances and establish a comprehensive framework for studying these processes using long-term remote sensing data. By doing so, this research provides valuable insights for regional forest management and contributes to global discussions on forest disturbance under the pressures of urbanization and development. The key objectives of this study were as follows: (i) To identify forest disturbance areas in Guangdong Province from 1990 to 2019 using Landsat long-term time series data; (ii) to quantify the spatial patterns and temporal trends of forest landscape fragmentation using a landscape fragmentation model; (iii) to investigate the drivers of forest disturbance dynamics, analyzing the interactions between various factors and their spatiotemporal relationships, as well as the temporal evolution of key drivers.

2. Materials and Methods

2.1. Study Area

Guangdong Province is located on the southern coast of mainland China, between latitude 20°09′–25°31′ N and longitude 109°45′–117°20′ E, covering an area of approximately 179,700 km2 (Figure 1). The province enjoys a favorable natural geography, spanning three temperature zones from north to south, with an East Asian maritime monsoon climate characterized by warm temperatures and abundant rainfall. As one of the regions in China richest in sunlight, heat, and water resources, Guangdong supports high levels of plant growth. The province is rich in forest resources, with typical vegetation including subtropical evergreen broad-leaved forests in the northern Nanling region, subtropical evergreen monsoon rainforests in the central region, and tropical evergreen monsoon rainforests in the southern region [27]. Additionally, Guangdong features extensive plantations of species such as Pinus massoniana, Cunninghamia Lanceolata, Pinus Elliottii Engelm, Cryptomeria fortune, and Eucalyptus [30]. From a socio-economic perspective, Guangdong, as one of the pioneers of China’s reform and opening-up policies, has experienced rapid socio-economic development. According to the China Statistical Yearbook, Guangdong has been the most populous province in China for many years. Since 1989, it has consistently ranked first in gross national product, establishing itself as China’s largest economic powerhouse.

2.2. Data

Landsat images covering Guangdong Province from 1990 to 2019 were obtained from the publicly accessible remote sensing data catalog. The dataset includes L2-level surface reflectance data acquired by the Landsat 5 Thematic Mapper (TM) manufactured by Hughes Aircraft Company (El Segundo, CA, USA), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) developed by Raytheon (Waltham, MA, USA), and Landsat 8 Operational Land Imager (OLI) sensors built by Ball Aerospace & Technologies Corp (Boulder, CO, USA). Elevation and slope data were derived from NASA’s most recent Digital Elevation Model (DEM) data, accessible at LP DAAC—Homepage, with a spatial resolution of 30 m. NASA has enhanced the precision of this elevation and slope data by reprocessing the original Shuttle Radar Topography Mission (SRTM) data [21]. Additionally, demographic and economic statistics including population density and per capita GDP, along with data on primary, secondary, and tertiary industries, were extracted from the “China Statistical Yearbook”.

2.3. Methods

This investigation assessed forest disturbances in Guangdong Province over the period from 1990 to 2019, exploring the spatiotemporal patterns of these disturbances and their associations with various driving factors. The initial step involved identifying areas of forest disturbance using the LandTrendr algorithm. Subsequently, a landscape subdivision process model was applied to assess and quantify forest landscape fragmentation and analyze its spatiotemporal dynamics. The Geodetector tool and correlation coefficients were then employed to examine the mechanisms driving these variations, with a focus on their spatial distribution and temporal changes. This research quantitatively analyzed the interrelationships and interactions among forest disturbances and various factors, as well as the temporal correlations of these dynamic factors. The methodological framework of this study is depicted in Figure 2.

2.3.1. Pre-Processing of Landsat Images

Landsat images covering Guangdong Province from 1990 to 2019 were obtained from the publicly accessible remote sensing data catalog. The dataset includes L2-level surface reflectance data acquired by the Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) sensors. These images had undergone preprocessing steps, including geometric correction, radiometric calibration, and atmospheric correction [31]. To ensure comprehensive coverage of the study area and minimize the impact of phenological changes on forest spectral information, the growing season of forests in Guangdong Province (April to October) was selected as the study period. Landsat images with less than 5% cloud cover were chosen for analysis. Contaminated pixels caused by cloud cover and shadows were eliminated from all images using the quality assessment band (BQA). Finally, annual cloud-free composite images for Guangdong Province from 1990 to 2019 were generated by calculating the median value of image pixels for each year and applying vector clipping [32].

2.3.2. Disturbance Areas

Forest disturbances were identified using the LandTrendr algorithm, which provides a foundational dataset for subsequent landscape fragmentation analysis. Developed by Kennedy et al., the LandTrendr algorithm is designed to detect disturbance events and change trends through spectral change trajectories generated from Landsat time series data [14]. It facilitates the determination of disturbance timing and the intensity of changes before and after disturbances, as well as forest recovery by evaluating the characteristics of these change trajectories [33]. This algorithm effectively filters out noise within long-term spectral trajectories without the need for a predefined trajectory model, thus proving advantageous for monitoring long-term forest dynamics [18].
The LandTrendr algorithm utilizes spectral indices, notably the Normalized Burn Ratio (NBR), which is especially sensitive to disturbance events. The NBR index is constructed using near-infrared (NIR) and shortwave infrared (SWIR) bands, which are closely associated with vegetation structure and highly responsive to alterations in forest structural complexity [34]. Therefore, this study utilized annual cloud-free composite Landsat images from 1990 to 2019 to construct a time series of the Normalized Burn Ratio (NBR) index for forest disturbance extraction. The parameters were carefully selected to meet the specific requirements of the data, as detailed in Table 1.
To validate the accuracy of forest disturbance monitoring, a stratified random sampling approach was employed to select forest disturbance samples. Each stratum was defined based on specific categories, including annual disturbance areas, persistent non-forest areas, and persistent forest areas. A total of 100–200 random samples were selected from each stratum, with additional samples allocated to years and regions with higher disturbance levels to ensure adequate representation. The validation data were primarily derived from the visual interpretation of Landsat imagery and high-resolution Google Earth imagery, and further cross-verified using annual vegetation index time series. A confusion matrix was constructed for accuracy assessment, with overall accuracy, user accuracy, and producer accuracy used as evaluation metrics.

2.3.3. Analysis of Landscape Fragmentation Spatial Process

The Landscape Fragmentation Spatial Process Model, underpinned by Forman’s theory of landscape subdivision [19], serves to analyze the spatial dynamics of landscape fragmentation. This model integrates both disturbed and undisturbed forest areas to delineate lost forest patches and those that remain (Figure 3). These lost patches are subsequently categorized into four distinct spatial processes: perforation, subdivision, shrinkage, and attrition. Forest disturbances often exhibit temporal and spatial persistence. Disturbances often unfold incrementally over time, with repeated events occurring in the same region rather than as isolated, singular occurrences. For instance, perforation often serves as a precursor to more advanced fragmentation processes. An initial disturbance may begin as a small perforation in a forest patch, but over subsequent years, neighboring pixels may also be disturbed, resulting in progressive changes to the spatial structure, such as shrinkage, subdivision, or attrition. This temporal and spatial continuity reflects the cumulative nature of forest disturbances and their evolution over time [5,19]. Therefore, analyzing forest disturbances at shorter time intervals could introduce variability or inaccuracies, especially in areas where disturbance processes are ongoing. To reduce such potential inaccuracies and to better capture the overall trends in spatial and temporal patterns, we divided the time series into three decadal intervals (1990–1999, 2000–2009, and 2010–2019). These data were then juxtaposed with forest classification maps to differentiate between disturbed and undisturbed areas within each period, facilitating the identification of both the lost and remaining forest pixels.
The Landscape Subdivision Spatial Process Model comprises three principal components. The first component systematically classifies lost forest patches into one of four categories: perforation, attrition, subdivision, or shrinkage. The detailed processes are outlined as follows:
(1)
An eight-neighborhood approach is employed to amalgamate both lost and remaining forest pixels, resulting in the creation of maps depicting lost forest patches and Forest Change Map 1, where background values are set to zero.
(2)
The count of distinct patches within the eight-neighborhood of lost forest pixels is recorded. A count of one indicates complete encirclement by other lost forest pixels, whereas a count greater than or equal to two denotes a presence of varying values.
(3)
The maximum value within each lost forest patch is analyzed statistically. A value of two is indicative of either a perforation or attrition patch, while a value of three or more suggests a subdivision or shrinkage patch.
The second component of the model focuses on excluding disappeared patches and further categorizing the remaining lost forest patches into subdivision, perforation, or shrinkage. The specific steps involved include the following:
(1)
Setting the background of Forest Change Map 1 to null to generate Forest Change Map 2. Subsequently, the lost forest pixels in Forest Change Map 2 are set to null, producing Forest Change Map 3.
(2)
A focal minimum value statistical analysis is conducted on Forest Change Map 3, alongside a focal maximum value statistical analysis on Forest Change Map 2. The focal maximum value of lost forest pixels is adjusted to one and then to null to exclude disappeared patches.
(3)
If the focal minimum value of pixels matches the maximum value and the pixels are part of the same patch with this maximum value, the patch is classified as either perforation or shrinkage. Other configurations are identified as subdivision patches.
The third component integrates the outcomes from the initial two components via conditional calculations, culminating in the generation of the spatial process map of forest subdivision.

2.3.4. Driver Analysis

This study utilized both Geodetector and Pearson correlation coefficient analyses to explore the spatiotemporal driving mechanisms of forest disturbances, considering aspects of spatial distribution and dynamic changes.
Geodetector is a statistical tool that leverages spatial differentiation to identify driving forces by detecting associations between two variables based on the consistency of their spatial distributions [35]. This tool not only evaluates the differential impacts of various explanatory variables on a dependent variable but also explores the interactions among different driving factors [36]. In this analysis, the area of forest disturbance and its proportion to urban areas were examined across three distinct periods, considering factors such as distance from cities, per capita GDP, population density, and elevation as potential drivers. Geodetector was applied to scrutinize and elucidate the driving factors influencing forest disturbance throughout these periods.
Pearson correlation coefficient analysis, a method commonly employed to assess linear relationships between two variables, was used to investigate the spatiotemporal dynamics of forest disturbance processes within the study area [37]. This analysis focused on changes in per capita GDP, population density, and transitions within the primary, secondary, and tertiary sectors.

3. Results

3.1. The Results of Forest Disturbance Detection

To assess the accuracy of forest disturbance detection by LandTrendr, we performed an accuracy evaluation using randomly selected samples, generating annual confusion matrices (see Supplementary Table S1). The results indicate an overall accuracy (OA) of 82.59% and a Kappa coefficient of 0.79, with user accuracy ranging from 70.16% to 98.56%. Some short-term and weaker disturbance events were excluded during the LandTrendr processing, which led to occasional omissions in the statistical data, particularly resulting in lower producer accuracy in certain years for the forest change class. Figure 4 presents disturbance results for three representative areas, along with Google Earth images and NBR time series curves. Overall, the LandTrendr algorithm effectively captured the most prominent disturbance patches, ensuring that significant changes and long-term trends were well represented. The disturbance results are robust and sufficient for subsequent research applications.

3.2. Analysis of Forest Landscape Subdivision Spatial Processes

The results of the number and area of the spatial process of forest subdivision across three periods in Table 2. From 1990 to 1999, attrition accounted for the largest proportion of forest subdivision patches, followed by perforation and shrinkage, with subdivision contributing the least. In the subsequent decade (2000 to 2009), the proportion of shrinkage patches surged significantly to 61.44%, becoming the dominant category, with perforation and attrition following. Notably, compared to the previous decade, perforation decreased by 5.39%, and attrition declined significantly by 35.73%, while subdivision saw a slight increase. From 2010 to 2019, the prevalence of shrinkage patches remained relatively stable. During this period, perforation decreased by 8.34%, but attrition rose by 7.09%, and subdivision continued to represent the smallest proportion. The rank order by area of the forest subdivision processes (attrition, perforation, shrinkage, subdivision) from 1990 to 1999 mirrored their respective quantities. During the periods from 2000 to 2009 and 2010 to 2019, changes in the proportion of patch area for these processes closely aligned with those observed in their quantities. However, subdivision ranked third in terms of area, surpassing attrition, which accounted for the smallest proportion. The results across the three study periods reveal a progressive evolution of forest fragmentation processes. Both in terms of patch number and area, shrinkage patches exhibited a consistent increase, primarily driven by large-scale land-use changes, urbanization, and infrastructure development. These changes have led to intensified erosion of forest edges, potentially resulting in severe ecological consequences, including reduced biodiversity and ecosystem services.
The centroid coordinates of various forest subdivision processes across the study area were analyzed for three distinct periods, resulting in the production of a centroid spatial distribution map (Figure 5). The centroids of all subdivision processes were predominantly located within the Pearl River Delta region. From 1990 to 1999, the centroids were primarily situated between Jiangmen and Huizhou. By the decade spanning 2000 to 2009, these centroids had shifted northwest to the area between Dongguan and Zhaoqing. From 2010 to 2019, the centroids were primarily located around Foshan, Guangzhou, and Dongguan. Specifically, during the 1990–1999 period, the centroid of perforation patches was located at the western boundary of Guangzhou, moving southwestward to the southeastern area of Zhaoqing in the following decade, and subsequently migrated northeastward in the 2010–2019 period. The centroid of shrinkage patches, originally near the boundary between Guangzhou and Dongguan, moved eastward to Foshan from 2000 to 2009 and then remained relatively stable. The centroid of subdivision patches, positioned at the junction of Guangzhou, Zhongshan, and Foshan in the 1990s, shifted northwestward to the junction of Zhaoqing and Foshan in the subsequent decade, continuing northeastward thereafter. The centroid of attrition patches, initially at the junction of Jiangmen, Foshan, and Zhuhai, consistently migrated northeastward in a nearly straight-line trajectory, eventually settling in the junction area of Huizhou, Guangzhou, and Dongguan from 2000 to 2019.
Overall, the spatial shifts in fragmentation centroids indicate significant changes in the spatial patterns of forest disturbances. The general northwestward migration of fragmentation processes toward inland areas reflects broader regional transitions in urbanization and land-use patterns. This trend suggests that ongoing urban expansion and infrastructure development are shifting from coastal regions to inland areas, placing increasing ecological pressure on forest ecosystems in Guangdong’s interior.

3.3. Analysis of Driving Factors

3.3.1. Analysis of the Driving Mechanisms Behind the Spatial Distribution Pattern of Forest Disturbance

Over three distinct periods, statistical analyses were conducted on the areas affected by forest disturbances and their proportions within urban regions. Factors such as distance from cities, GDP per capita, population density, and elevation were investigated as potential drivers of these disturbances. Geographical detectors were utilized to examine and quantify the influence of these factors across the different periods.
The findings from the geographical detectors for the period 1990–1999 are summarized in Table 3. From a univariate perspective, population density emerged as the primary indirect driver of forest disturbance, with a q-value of 0.64, signifying its substantial influence. Other factors displayed q-values below 0.3, indicating their relatively minor individual impacts. Regarding interaction effects, the combination of population density and distance from cities demonstrated the most significant influence on forest disturbance, with a q-value of 0.83, followed by the interaction between population density and GDP per capita (q = 0.75). For the 2000–2009 period, the results, detailed in Table 4, align with earlier findings. Population density remained the predominant indirect driver of forest disturbance from a univariate analysis standpoint. The interaction effects of population density with distance from cities and with GDP per capita yielded q-values of 0.69 and 0.51, respectively, highlighting their significant impacts. The analysis for the 2010–2019 period is presented in Table 5. In univariate analyses, both population density and GDP per capita recorded q-values over 0.5, signaling their considerable effects on forest disturbance. Interaction effects involving these factors, particularly combinations of population density with GDP per capita and with distance from cities, were notable, with q-values exceeding 0.7, indicating a pronounced influence.
A longitudinal analysis of q-values for single factors across the periods reveals a decreasing explanatory power of population density, although it consistently remains the most significant driver of forest disturbance. In contrast, the explanatory power of GDP per capita and distance from cities exhibits an increasing trend. The examination of q-values for interaction effects between pairs of factors across different periods shows that interactions involving population density significantly boost the explanatory power for forest disturbance, with a noticeable rise in the explanatory power of interactions involving GDP per capita. Further analysis and discussion of the relevant results can be found in Section 4.3.

3.3.2. Analysis of the Spatiotemporal Dynamics Driving Forest Disturbance

Between 1990 and 2019, the study analyzed the spatiotemporal dynamics of forest disturbance by calculating correlation coefficients for population density, GDP per capita, and the economic contributions from primary, secondary, and tertiary industries (Figure 6). We examined the normality of datasets, and they were found to follow a normal (Gaussian) distribution. Across most cities, the correlation coefficients between forest disturbance area and population density were typically in the range of 0.4 to 0.6. Cities such as Heyuan, Huizhou, Shanwei, and Shaoguan exhibited correlation coefficients ranging from 0.6 to 0.8 between forest disturbance area and GDP per capita. Cities with coefficients below 0.4, indicating a weaker correlation, included Guangzhou, Zhuhai, Foshan, and Shantou, which are predominantly economic zones. The correlation coefficients between forest disturbance area and the primary industry varied significantly; cities like Zhaoqing, Qingyuan, Shaoguan, Heyuan, and Shanwei showed coefficients ranging from 0.6 to 0.8, whereas cities like Guangzhou, Zhuhai, Foshan, and Yangjiang recorded coefficients below 0.4. For the secondary industry, cities such as Shaoguan and Shanwei had coefficients between forest disturbance area and GDP per capita within the 0.6 to 0.8 range, whereas Guangzhou, Zhuhai, Foshan, Maoming, and Yangjiang showed coefficients below 0.4. Regarding the tertiary industry, the correlation coefficients between forest disturbance area and GDP per capita ranged from 0.6 to 0.8 in cities like Shaoguan, Zhanjiang, Meizhou, Shanwei, and Heyuan. Cities with coefficients below 0.4 included Guangzhou, Zhuhai, Foshan, Jieyang, and Yangjiang, while Maoming, Zhaoqing, Jiangmen, Huizhou, and Chaozhou fell within the 0.4 to 0.6 range.
Overall, Pearson correlation coefficients revealed significant regional variations in the influence of socioeconomic factors on forest disturbances across Guangdong. In cities undergoing rapid development characterized by significant increases in population and GDP, the correlation between forest disturbance area and the factors of GDP, population density, and the structure of GDP tended to be relatively low. In areas with relatively slow economic development, such as Heyuan, Shaoguan, and Zhaoqing, the correlation between forest disturbance area and the factors of GDP, population density, and the structure of GDP tended to be relatively high. Further analysis and discussion of the relevant results can be found in Section 4.3.

4. Discussion

4.1. Forest Disturbance Monitoring Algorithm

Tropical and subtropical forest ecosystems, such as those in Guangdong Province, play an irreplaceable role in global biodiversity conservation and the carbon cycle. Therefore, timely and accurate monitoring of forest disturbances in these regions is of critical importance. Advances in remote sensing, particularly the integration of the LandTrendr algorithm into a cloud-based remote sensing analysis platform by Kennedy et al., have significantly enhanced the efficiency of large-scale, long-term data processing by eliminating barriers related to raw data access and computational costs [38]. Studies have demonstrated that LandTrendr is one of the most effective methods for monitoring forest change, excelling in terms of computational efficiency, accuracy, and the ability to capture disturbance patch integrity.
However, the algorithm’s reliance on time series data makes the quality of remote sensing imagery a crucial factor affecting its performance. Guangdong Province’s tropical and subtropical climate, characterized by frequent cloud cover and rainfall [32], often hampers the acquisition of cloud-free optical imagery, thereby limiting the effectiveness of LandTrendr in forest disturbance monitoring. Furthermore, the adaptability of the algorithm across different regions requires careful temporal configuration. Selecting appropriate time windows is essential to minimize monitoring errors and address challenges posed by seasonal variations and data gaps. For regions with limited cloud-free data, longer time windows may help alleviate data scarcity, albeit at the cost of finer temporal resolution. Alternatively, the integration of multi-source data, such as optical and radar imagery, could enhance the accuracy of forest disturbance detection.
Another critical factor influencing the success of disturbance detection is the selection of input variables. Vegetation indices, such as the Normalized Burn Ratio (NBR), have been shown to outperform single spectral bands, as they reduce the influence of external factors like topography and atmospheric variability. The use of NBR in this study was based on its proven effectiveness, as highlighted in numerous studies [39,40]. However, the performance of vegetation indices can vary across different ecological environments. This highlights the need for future research to compare multiple indices to identify those most suitable for specific regions and types of disturbances.

4.2. Spatial Processes of Forest Fragmentation

The analysis of forest fragmentation processes in Guangdong Province reveals notable temporal and spatial variations over the three decades studied.
From 1990 to 1999, attrition accounted for the largest proportion of forest subdivision patches, indicating that the initial stages of forest loss were primarily driven by land conversion for agriculture and urbanization. This period represents the early phases of human-induced forest disturbances, a pattern consistent with other tropical and subtropical regions [41,42]. Between 2000 and 2009, shrinkage emerged as the dominant fragmentation process, with its proportion surging to 61.44%. This marked increase reflects the intensified impacts of rapid urban expansion in Guangdong Province, where forest edges were increasingly eroded, leading to greater reductions in forest patch size. Subdivision, in contrast, consistently represented the least significant fragmentation process over the three decades. However, its relative importance increased slightly after 2000, likely due to urban sprawl and peri-urban development. Subdivision is often associated with smaller scale forest fragmentation caused by infrastructure expansion and other human activities [21].
The spatial analysis of fragmentation processes revealed a clear northwestward shift in the centroids of all fragmentation types over the study period. These centroids were predominantly located within the Pearl River Delta region, reflecting the intense urbanization in one of China’s most rapidly developing urban areas [43]. The northwestward migration of centroids highlights the shifting pressure of urban expansion from coastal areas to inland regions. This trend suggests that inland forest ecosystems are facing increasing threats from urban development, necessitating targeted conservation efforts to mitigate future fragmentation impacts.

4.3. Drivers of Forest Disturbance

The drivers of forest disturbance in Guangdong Province, as analyzed through geographical detectors and Pearson correlation coefficients, reveal complex interactions between socio-economic factors and forest ecosystems over time. These findings contribute to understanding the dynamic nature of forest disturbance in rapidly urbanizing regions.
Population density consistently emerged as the most significant indirect driver of forest disturbance, particularly during the early periods (1990–1999 and 2000–2009), with high q-values (e.g., 0.64 in 1990–1999). This underscores the profound influence of rapid urban population growth on forest ecosystems, likely driven by the conversion of forested land to accommodate infrastructure expansion and residential development. However, the explanatory power of population density declined over time, while the influence of GDP per capita increased, particularly during the 2010–2019 period, with q-values exceeding 0.5. This shift reflects a transition from direct demographic impacts to economic pressures associated with urban and industrial expansion. The interaction effects among factors, especially between population density and GDP per capita, exhibited q-values exceeding 0.7 across all periods, underscoring the compounding impact of these variables. This reinforces the need to consider not just individual factors but their combined effects on forest ecosystems.
Spatially, Pearson correlation coefficients revealed significant differences in how socio-economic factors influenced forest disturbance across Guangdong. Rapidly urbanizing cities like Guangzhou, Zhuhai, and Foshan exhibited weaker correlations (coefficients < 0.4) between forest disturbance and socio-economic factors. These cities are highly urbanized, with limited forest resources and possibly more effective urban planning, reducing the direct impacts of population and economic pressures [44]. In contrast, less urbanized cities such as Heyuan, Shaoguan, and Zhaoqing displayed stronger correlations (coefficients between 0.6 and 0.8) with factors like GDP per capita and population density. These regions, characterized by slower economic growth, are more susceptible to land-use changes driven by agricultural expansion and logging. Similar findings were reported in other regions with uneven urbanization, where the impacts of economic and demographic factors vary spatially [44]. The correlation with economic sectors further illustrates this spatial variation. For instance, the primary industry was strongly correlated with forest disturbance in less urbanized cities, reflecting the role of agricultural land conversion in driving forest loss. Meanwhile, the secondary and tertiary industries had a greater influence in cities undergoing industrialization and service-sector growth, consistent with findings from other developing regions [45].
These findings suggest that forest disturbances in Guangdong are driven by a combination of direct and indirect socio-economic factors. The diminishing influence of population density and the growing impact of GDP per capita reflect the evolving nature of human–environment interactions in urbanizing regions. The identified interaction effects highlight the need for integrated management strategies that address the combined pressures of demographic and economic growth. Policymakers should prioritize sustainable urban planning and forest conservation, particularly in rapidly developing regions such as the Pearl River Delta. In less urbanized areas, targeted interventions can help mitigate forest loss caused by agricultural expansion and economic development. These results contribute to the broader understanding of forest disturbance dynamics in urbanizing contexts, providing valuable insights for policymakers and researchers.

4.4. Future Research

In addition to the research mentioned in Section 4.1 regarding improving the accuracy of forest disturbance results, landscape spatial pattern studies also warrant further exploration. Some researchers utilize landscape metrics to indirectly describe landscape patterns and structures. Metrics such as patch area, patch number, landscape division, diversity index, and patch density are commonly used to quantify the degree of landscape fragmentation [21,22,23]. These metrics are often combined with classification methods for landscape pattern analysis. Both approaches—statistical indices and direct spatial relationship analysis—can be effective for analyzing landscape fragmentation and spatial changes. In our case, the spatial processes under examination, such as forest disturbance and fragmentation, are effectively captured through direct pixel-based analysis. Future studies can explore and compare these methods, including their suitability for different types of landscape dynamics.
Moreover, as the forest landscape continues to be reshaped by human activities, future research should focus on evaluating the ecological consequences of forest fragmentation, particularly its impacts on biodiversity, carbon sequestration, and ecosystem services. Additionally, our results indicate that socioeconomic factors exhibit significant variations in their impact on different stages of development within the same region, or on different areas within the same period. Future research could further explore how to mitigate forest disturbance patterns during the ongoing urbanization process, and establish context-specific, time-sensitive policies that not only reduce forest disturbance but also promote sustainable development. The dynamics of forest disturbance vary significantly between cities with different levels of economic development.

5. Conclusions

This study utilized long-term Landsat imagery and the LandTrendr algorithm to detect forest disturbance areas in Guangdong Province from 1990 to 2019, providing a comprehensive analysis of their impacts on forest landscape fragmentation and the underlying driving mechanisms. By integrating spatial process models and quantitative assessments of factor interactions, the findings indicate a consistent increase in shrinkage patches, both in quantity and area, driven by ongoing forest edge erosion caused by urbanization, land-use changes, and infrastructure development. Spatially, the centroids of forest fragmentation processes exhibited a clear inland migration trend, reflecting the growing ecological pressures faced by inland forest ecosystems. Interactions among driving factors, particularly between population density and economic factors, significantly amplified their combined impacts. The correlation between forest disturbances and socio-economic factors revealed distinct regional variations, highlighting significant differences in forest disturbance dynamics across cities with varying levels of economic development. This study provides critical insights into the spatiotemporal dynamics of forest disturbances under rapid urbanization and economic development. It lays the groundwork for sustainable forest management strategies in Guangdong Province and may contribute to global discussions on managing forest ecosystems during periods of rapid socio-economic transformation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16010189/s1, Table S1: Annual confusion matrices.

Author Contributions

Conceptualization, L.Q. and L.L.; methodology, L.Q.; validation, J.J.; writing—original draft preparation, L.Q.; writing—review and editing, Z.C., X.L. and S.C.; investigation, J.J.; resources, L.L.; project administration, L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science and Technology Program of Guangdong Province (No. 2021B1212100003 and No. 2024A1111120008), the Science and Technology Projects in Guangzhou (2023A04J0927), the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR (20230505), and the Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Chinese Academy of Sciences (VRMDE2306).

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an ongoing study and time limitations. Requests to access the datasets should be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 16 00189 g001
Figure 1. Location of the study area and spatial distribution of forests from GlobeLand30 (2020).
Figure 1. Location of the study area and spatial distribution of forests from GlobeLand30 (2020).
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Figure 2. Research technology roadmap.
Figure 2. Research technology roadmap.
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Figure 3. Construction process of forest subdivision process model (The red square is an example of an eight-neighborhood).
Figure 3. Construction process of forest subdivision process model (The red square is an example of an eight-neighborhood).
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Figure 4. Disturbance results for three typical areas ((a–c) were three representative areas).
Figure 4. Disturbance results for three typical areas ((a–c) were three representative areas).
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Figure 5. Result of centroid analysis in the spatial process of forest subdivision.
Figure 5. Result of centroid analysis in the spatial process of forest subdivision.
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Figure 6. Correlation coefficients between the area of forest disturbance and various factors.
Figure 6. Correlation coefficients between the area of forest disturbance and various factors.
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Table 1. Required parameter settings.
Table 1. Required parameter settings.
ProcessParametersParameter Values
Number of maximum segments6
Vertex parameter0.9
Time series segmentationRecovery threshold0.25
Max p value of the fit0.05
Optimal model scale0.75
Thresholds for 1-year vegetation cover loss10
FiltersThresholds for 30-year vegetation cover loss3
Thresholds for percentage of vegetation growth5
Pre-disturbance coverage threshold20
MappingMinimum mapping in pixels11
Table 2. The number and area of the spatial process of forest subdivision across three periods.
Table 2. The number and area of the spatial process of forest subdivision across three periods.
QuantitiesArea (ha)
Period1990–19992000–20092010–20191990–19992000–20092010–2019
Perforation209,342161,083124,45046,797.2738,620.8780,730.07
Attrition291,17954,295109,38793,291.2016,910.6915,855.75
Subdivision741616,53525,62912,959.9520,285.3321,367.49
Shrinkage142,662369,427414,99349,450.71111,663.07116,592.53
Quantities (%)Area (%)
Period1990–19992000–20092010–20191990–19992000–20092010–2019
Perforation32.18%26.79%18.45%23.11%20.60%34.42%
Attrition44.76%9.03%16.22%46.07%9.02%6.76%
Subdivision1.14%2.75%3.80%6.40%10.82%9.11%
Shrinkage21.93%61.44%61.53%24.42%59.56%49.71%
Table 3. The q-value table of geographical detection of various driving factors of forest disturbance from 1990 to 1999.
Table 3. The q-value table of geographical detection of various driving factors of forest disturbance from 1990 to 1999.
qELPDPCGDTC
EL0.0317
PD0.64420.6372
PCG0.31870.74990.2932
DTC0.16820.82790.49650.1390
EL, PD, PCG, and DTC represent elevation, population density, per capita GDP, and distance to the city, respectively.
Table 4. The q-value table of geographical detection of various driving factors of forest disturbance from 2000 to 2009.
Table 4. The q-value table of geographical detection of various driving factors of forest disturbance from 2000 to 2009.
qELPDPCGDTC
EL0.0330
PD0.14800.1156
PCG0.29520.69140.2575
DTC0.29420.50870.45640.2565
EL, PD, PCG, and DTC represent elevation, population density, per capita GDP, and distance to the city, respectively.
Table 5. The q-value table of geographical detection of various driving factors of forest disturbance from 2010 to 2019.
Table 5. The q-value table of geographical detection of various driving factors of forest disturbance from 2010 to 2019.
qELPDPCGDTC
EL0.0267---
PD0.53550.5221--
PCG0.54700.76510.5287-
DTC0.33090.68370.74790.3135
EL, PD, PCG, and DTC represent elevation, population density, per capita GDP, and distance to the city, respectively.
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Qiu, L.; Chang, Z.; Luo, X.; Chen, S.; Jiang, J.; Lei, L. Monitoring Forest Disturbances and Associated Driving Forces in Guangdong Province Using Long-Term Landsat Time Series Images. Forests 2025, 16, 189. https://doi.org/10.3390/f16010189

AMA Style

Qiu L, Chang Z, Luo X, Chen S, Jiang J, Lei L. Monitoring Forest Disturbances and Associated Driving Forces in Guangdong Province Using Long-Term Landsat Time Series Images. Forests. 2025; 16(1):189. https://doi.org/10.3390/f16010189

Chicago/Turabian Style

Qiu, Lin, Zhongbing Chang, Xiaomei Luo, Songjia Chen, Jun Jiang, and Li Lei. 2025. "Monitoring Forest Disturbances and Associated Driving Forces in Guangdong Province Using Long-Term Landsat Time Series Images" Forests 16, no. 1: 189. https://doi.org/10.3390/f16010189

APA Style

Qiu, L., Chang, Z., Luo, X., Chen, S., Jiang, J., & Lei, L. (2025). Monitoring Forest Disturbances and Associated Driving Forces in Guangdong Province Using Long-Term Landsat Time Series Images. Forests, 16(1), 189. https://doi.org/10.3390/f16010189

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