Deforestation and forest fragmentation are interrelated processes that have significant impacts on ecosystems. Logging reduces forest cover, while fragmentation divides the remaining forest into smaller, isolated patches, altering the landscape and affecting biodiversity (Taubert et al., 2018; Liu et al., 2019). Deforestation and selective logging significantly increase the ratio of forest edge to area and the number of forest fragments, with edge effects extending deep into the remaining forest areas, leading to ecological consequences such as reduced genetic diversity, altered pollinator interactions, and different environmental conditions (Broadbent et al., 2008). Fragmentation affects the genetic and demographic structure of plant populations. It can lead to reduced genetic diversity, altered pollinator interactions and changes in environmental conditions due to edge effects (Honnay et al., 2005). Remote sensing technologies, which provide critical insight into the conservation status of habitats and structural changes within forested landscapes over time, have become a key tool in the analysis of forest fragmentation (Wulder, 1998). Remote sensing data was used by different researchers for the analysis of deforestation and forest fragmentation (Saunders et al., 1991; Nagendra et al., 2003; Biradar et al., 2005; Echeverría et al., 2011; Sahana et al., 2015, 2018). It allows for the efficient mapping of large areas by providing a precise and relevant collection of digital data at multiple scales. Forest fragmentation often occurs over large areas that are difficult to monitor using field-based methods alone (García-Gigorro and Saura, 2005; Carranza et al., 2014; Ganivet and Bloomberg, 2019). Forest fragmentation caused by deforestation is a major problem in natural landscapes, with significant consequences for terrestrial eco-systems (Echeverria et al., 2006; Taubert et al., 2018; Fischer et al., 2021; Slattery and Fenner, 2021; Ma et al., 2023). The relationship between deforestation and flooding is a critical area of study with significant implications for environmental management, economic activity, and human well-being (Angelsen and Kaimowitz, 1999). Previous studies have shown a significant correlation between deforestation and flood risk in affected regions (Bradshaw et al., 2007; Danáčová et al., 2020; Fazel-Rastgar, 2020; Peptenatu et al., 2020; Posada-Marín and Salazar, 2022; Hou et al., 2023). The process of forest fragmentation alters the structure and functions of these ecosystems, reducing their ability to regulate water flows and prevent flooding (Cramer and Hobbs, 2002; National Research Council, 2008; Hurtado-Pidal et al., 2022). Deforestation can affect hydrological services such as flood control, which is essential for preventing natural disasters and maintaining ecological balance (Bradshaw et al., 2007). As a result, forest fragmentation increases land vulnerability to flooding and amplify its impacts on human communities and infrastructure (Randhir and Erol, 2013; Renó et al., 2016). In developing countries, where the economic and human toll of floods can be devastating, this relationship is particularly pronounced. Loss of forested areas impacts liquid runoff on slopes (rapid flooding, accelerated land erosion, etc.) as well as loss of biodiversity. Such a study can contribute to the development of the institutional and regulatory framework for forest sector activity and sustainable management of forest resources. The preponderance of evidence supports the importance of forest conservation in flood risk management, although there is some debate about the impact of deforestation on major flood events (Chang et al., 2009; Dijk et al., 2009; Brookhuis and Hein, 2016; Tan-Soo et al., 2016).

Short-winged birds are particularly vulnerable to the effects of forest fragmentation. Because of their limited ability to move through gaps between woodlots in search of mates and food, many species accustomed to living deep in forests are exposed to predators in fragmented forests (Weeks et al., 2023). Conflicts between humans and wildlife intensify when the migration routes of certain species (wild boar, bears) are destroyed. Wild animals see human-altered landscapes, such as farms and towns, as another part of their original natural habitat (Betts et al., 2022).

The loss of carbon sequestration capacity through deforestation is a major contributor to accelerating climate change. Trees absorb carbon dioxide from the air and release it when they are felled (Tong et al., 2020). In addition, evapotranspiration, the process by which water is recycled to the atmosphere through leaf evaporation and plant root transpiration, is decreased when forests are cleared with a direct consequence through increased air temperature (Li et al., 2020).

The EU Deforestation Regulation (EUDR), which aims to tackle the urgent problems of greenhouse gas emissions and biodiversity loss, confirms the desire of governments to adopt a more cautious attitude towards forests Regulation (EU) 2023/1115 of the European Parliament and of the Council of 31 May 2023 on the making available on the Union market and the export from the Union of certain commodities and products associated with deforestation and forest degradation and repealing Regulation (EU) No 995/2010. Green Deal: EU agrees law to fight global deforestation and forest degradation driven by EU production and consumption (2022, December 6). European Commission. In this research we aim to analyze the dynamics of forest fragmentation through fractal imaging and examine the spatial processes of deforestation using the Fractal Fragmentation Index (FFI) (Andronache et al., 2016) and Fractal Fragmentation and Disorder Index (FFDI) indices (Peptenatu et al., 2023). These indices have demonstrated their utility in previous studies analyzing the evolution of forested areas (Einzmann et al., 2017; Andronache et al., 2019; Diaconu et al., 2019).

The analysis of the loss areas showed that the tree cover of the Eastern Carpathians decreased by 201,077.4218 ha, from 2,327,594.44 ha in 2000 to 2,126,517.01 ha in 2022, which is a decrease of 8.6%. The most intensive deforestation occurred in 2003 (3,117.05 ha) and 2007 (23,135.51 ha).

During the analyzed period, a total of 74,902 forest patches were cleared, with an average of 3,405 patches per year. The highest number of patches occurred in 2007 (6,679), while the lowest was in 2003 (1,461). Between 2001 and 2022, 74,902 forest patches were deforested, resulting in 31,710 cumulative patches. Of these loss forest patches, 58% disappeared due to merging, leading to the fragmentation and disorder of compact forest areas. During this period, 2,262 new forest patches appeared due to fragmentation, representing a 17% increase in patches (from 10,851 in 2000 to 13,113 in 2022). Only 3% of deforestation resulted in the creation of new forest patches. The remaining 97% led to the reduction of compact forest areas without fragmentation, by deforestation occurring in isolated forest patches.

The trends in tree cover and deforestation changes from 2001 to 2022 are shown in Figure 6.

From 2001 to 2012, there was a downward trend in the percentage of new patches of cumulative loss resulting from new loss events, as shown in Figure 6A. However, after 2012, although the downward trend continued, there were some years where the percentage of new patches of cumulative loss increased compared to the previous year, specifically in 2010, 2014–2015, 2020, and 2022. The most significant decrease was observed in 2003 compared to 2002 (−12%), while the most significant increase was observed in 2013 compared to 2012 (13%). Between 2002 and 2007, 50–89% of loss patches resulted in new patches of cumulative loss, indicating that deforestation mostly occurred in isolation. In contrast, deforestation occurred more in continuation of pre-existing losses in the years 2012, 2018–2019, and 2021–2022, as only between 17 and 25% of loss patches resulted in new patches of cumulative loss.

Furthermore, there is a trend towards an increase in the percentage of tree cover patches due to new patches of cumulative loss, which is more pronounced during the period of 2001–2012 (refer to Figure 6B). There were some exceptions where there were decreases in the years 2010, 2017, 2019, and 2022 compared to their preceding years. Like the previous situation, the largest increase was in 2018 compared to 2017 (5.32%), while the largest decrease was in 2013 compared to 2012 (−14%). Between 2001 and 2006, de-forestation only slightly fragmented the tree cover, with new patches of cumulative loss generating new patches of tree cover at a rate of 1.1–3.4%. However, in more recent years (2009, 2011–2012, 2018, and 2020–2022), as deforestation continued from pre-existing clearings, the fragmentation of tree cover into more patches increased significantly, with new patches of cumulative loss resulting in new patches of tree cover at a rate of 11.2–23.1%.

Regarding the percentage of tree cover patches resulting from new patches of cumulative loss (Figure 6C), there is a trend of yearly increase during the period 2001–2012, followed by a slight trend of decrease after 2013. Prior to 2012, deforestation led to a greater fragmentation of tree cover, with deforestation occurring in a more disorderly manner. However, after 2013, deforestation occurred more in continuation of previous clearings, but also over isolated small patches of tree cover. Two distinct patterns have been identified in the data. The largest increase occurred in 2007 compared to 2006 (1.82%), while the largest decrease occurred in 2013 compared to 2012 (−1.64%). Between 2001 and 2022, new loss patches generated new patches of tree cover in only 1.2–2.2% of cases, with higher values of 3.2–5% recorded in 2007, 2009, and 2011–2015, as well as in 2020.

In the case of loss areas, deforestation averaged between 2.13 ha/patch in 2003 and 3.46 ha/patch in 2007, while cumulative loss areas increased from 2.19 ha/patch in 2001 to 6.44 ha/patch in 2022. Conversely, tree cover areas decreased from 214.51 ha/patch in 2000 to 162.17 ha/patch (Figure 7).

Figure 7 illustrates the relationship between areas and number of patches for loss areas (Figure 7A), cumulative loss areas (Figure 7B) and tree cover (Figure 7C). Figures 7A,B show a dependence between deforested areas and the number of patches for loss and cumulative loss areas. However, as forest area decreases due to forest loss, it fragments, leading to an increase in the number of forest patches.

The analysis of loss and cumulative loss areas between 2001 and 2022 indicates that deforested patches are small and heterogeneously distributed spatially (Figures 8A,B).

Deforestation was slightly more compact in 2004, 2007, and 2020 (Figure 8A). The same pattern is observed for cumulative loss, with FFI increasing from 0 in 2001 to 0.003 from 2001 to 2022 (Figure 8B). A trend towards forest compaction was observed between 2001 and 2012, despite disorderly deforestation during the same period (Figure 8C). As deforestation occurs chaotically and in small, fragmented, rarely interconnected patches (Andronache et al., 2016), FFI values are very low, making this index unsuitable for differentiation between years. To tackle this issue, we employed FFDI, which measures the level of disorder in deforested areas (Figures 8D–F). FFDI peaked in 2007, a year marked by extensive, fragmented, and disorderly deforestation (Pintilii et al., 2017). The lowest FFDI value was observed in 2003, during a period of reduced deforestation and a much more orderly spatial distribution (Figure 8D). Over 22 years of continuous deforestation, the FFDI increased by 33%, indicating increasingly fragmented forests (Figure 8E). The analysis of deforestation effects on tree cover revealed continuous forest fragmentation, with FFI decreasing by 41% between 2000 and 2022 (Figure 8C). This ongoing forest fragmentation also led to spatial disorder of forest patches, as indicated by FFDI estimating a 4% increase in forest fragmentation and disorder (Figure 8F).

Figures 9A–C illustrate the correlation between the count of patches and FFDI. In all three analyses (loss, cumulative loss, and tree cover), the fragmentation and disorder of these patches increase as the number of deforested patches increases.

The analysis showed that a significant amount of deforestation occurred during the studied period, resulting in a total loss of 76,205 forest patches, averaging 3,464 patches per year. The highest number of patches was observed in 2007, indicating significant deforestation activity, while the lowest was observed in 2003.

The proposed methodology has proven to be relevant for identifying areas where major land use changes have occurred in a short time. The research is part of a series of analyses that looked at the structured impact of forest loss on the ecosystem (Andronache et al., 2016; Einzmann et al., 2017; Andronache et al., 2019; Diaconu et al., 2019; Peptenatu et al., 2023) and local economies (Pintilii et al., 2017).

The importance of this analysis methodology lies in the rapidity with which it provides information on how the woody mass on slopes has been exploited, and consequently on the potential impact on run-off coefficients on slopes and the degree of habitat fragmentation. The need for such research is highlighted in numerous studies (Clarke and Schweizer, 1991; Imaizumi et al., 2008; Zhang et al., 2020; Zhao et al., 2021).

FFDI revealed patterns of forest fragmentation and disorder over time, with FFDI values gradually increasing over the studied period, indicating increasingly fragmented forests. Figures 9A–C support this trend, indicating a positive correlation between the count of deforested patches and FFDI across different analyses (loss, cumulative loss, and tree cover).

The research has shown that the FFI–FFDI pair is advantageous for analyzing forest fragmentation. Both FFI and FFDI are useful for analyzing tree cover fragmentation and disorder. However, FFDI yielded better results for the analysis of loss and cumulative loss.

The results highlight the complex dynamics of deforestation and its implications for forest fragmentation and spatial disorder. Effective forest management strategies are important to mitigate fragmentation and preserve forest ecosystems. Detail analyses allow for better understanding of the factors that determine forest loss and are relevant methods to assist decision making in the silvic and biodiversity sectors. As such, silvic code could be completed with mechanisms that monitor resources and exploitation process of wood.

Our findings reveal ongoing forest fragmentation as a result of deforestation, accompanied by an increase in their spatial disorder. This fragmentation and disorder may contribute to heightened flood risk in the affected regions. Fragmented and disorderly forests may lose their ability to regulate water flows and mitigate flood risk, exacerbating land vulnerability to extreme precipitation events. Therefore, it is essential to understand and appropriately manage forest fragmentation and disorder processes to reduce the risk of flooding and protect human communities and infrastructure.

The evaluation methodology of forest fragmentation does not have a relevant correspondent that permits validating indices. It must be mentioned that analyzed image resolution and synoptic situations may lower the accuracy of data and generate errors. Another limitation of the FFI analysis is that it is calculated for the whole picture without providing information on regional variations. A scale limitation is another general problem in image analysis, with a single pixel as the smallest scale and the whole image size as the largest scale. In addition, natural objects are also intrinsically limited in scaling due to the finite size of their structural units. Our study has some limitations that must be addressed. The images used, with a spatial resolution of 30 m, allowed us to capture only a coarser picture of forest patterns. The use of more detailed images would resolve this restriction and thus improve FFI or LCFD accuracies.

The main disadvantage of FFI is that this index does not differentiate patterns where the component objects are extremely small so that the edges cannot be extracted, or where the objects completely occupy the space. The FFDI was also designed to overcome these limitations by further quantifying the information entropy in the image using the Information Dimension. FFDI provides a clearer differentiation in the categorization of synthetic images and in real applications.

Both FFI and FFDI have the disadvantage of only analyzing binary images. Initial preprocessing of binary images, such as binarization and noise removal, can influence the results of fractal analysis and must be carefully managed. Depending on the threshold chosen, binarization may introduce analytical bias; however, this effect can be further investigated and could be corrected by incorporating a scaling relationship or using FFI or FFDI as a relative index for comparing different patterns or states of the system, rather than as an absolute measure of fragmentation or fragmentation and de-ordering.

Furthermore, the interpretation of fractal analysis results can be influenced by the scale at which measurements are made, and an inappropriate choice of scale can lead to misinterpretations (Ciobotaru et al., 2019; Tarko et al., 2020).

In Romania, the economic pressure on the silvic fund has risen after 1990 at the same time when external commerce of logs became more permissive (Tronicke and Lück, 2023). The legislative regulations from that moment led to a rise in fragmentation due to authorized and unauthorized logging on surfaces covered in forest (Knorn et al., 2012; Iordăchescu and Vasile, 2022). In the field, this is identified with great difficulty, but when using remote sensing technologies this becomes more accessible and accurate.

Speaking of limitations, the resolution of satellite images significantly influences the calculated values of the Fractal Fragmentation Index (FFI) and Fractal Fragmentation and Disorder Index (FFDI). High resolution images provide more detailed information, which can lead to a more accurate fractal analysis of forest fragmentation. Lower resolution images may miss fine details, resulting in an underestimation of fragmentation metrics. In this study, to avoid this problem, all images analyzed were converted to the same resolution.

Converting grayscale images into binary images, a necessary step for fractal analysis, involves selecting a binarization threshold. An inappropriate threshold can either hide critical details or introduce noise, thus affecting the values of FFDI or other fractal metrics. It is important to note that the FFI is relatively insensitive to this bias, since isolated, noise-like pixels do not influence the final result. However, to minimize bias for FFDI or other binary fractal analysis metrics, future research should consider using adaptive binarization techniques that adjust based on local image features. In the situation of our study, there was no need for adaptive binarization because the images in the Global Forest Change database only provide information about the presence or absence of forests or deforestation.

The scale at which fractal analysis is performed can significantly affect the interpretation of FFI and FFDI. Analysis at different spatial scales can reveal different aspects of forest fragmentation. Analyses at small scales may reveal detailed patterns of fragmentation, while analyses at large scales may show broader trends. Future studies should conduct multi-scale analyses to capture both fine and coarse details of fragmentation. A hierarchical approach, analyzing data at different scales, can provide a comprehensive understanding of forest fragmentation.

Deforestation occurred through various mechanisms during this period. These mechanisms included the emergence of new loss areas, continuation of deforestation in previously lost areas, or the merging of two previously lost areas into a single loss area.

A notable finding is that 57% of loss patches disappeared due to merging, leading to the fragmentation and disorder of compact forest areas. This highlights the impact of deforestation on the spatial structure of forest landscapes.

The analysis identified the creation of new forest patches due to fragmentation, representing a 17% increase over the studied period. However, only 3% of deforestation resulted in the creation of new forest patches, indicating that most deforestation led to the reduction of compact forest areas without fragmentation.

Trends in tree cover changes and deforestation patterns were demonstrated. From 2001 to 2012, there was a downward trend in the percentage of new patches of cumulative loss, followed by fluctuations in subsequent years. Deforestation tended to occur more in continuation of pre-existing losses in recent years, rather than as isolated events.

The fractal indicators used offer the possibility to quantify the reduction in forest area in a fast and highly accurate way. If the limitations of the methodology are taken into account in this process, it can be widely used at least at the level of forested areas.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

DD: Conceptualization, Supervision, Validation, Writing – original draft, Writing – review & editing. IA: Conceptualization, Data curation, Formal analysis, Software, Validation, Writing – original draft. AG: Investigation, Writing – original draft. TB: Formal analysis, Writing – review & editing. AB: Investigation, Software, Validation, Writing – review & editing.