Assessing the Potential Future Forest-Cover Change in Romania, Predicted Using a Scenario-Based Modelling

Kucsicsa, Gheorghe; Popovici, Elena-Ana; Bălteanu, Dan; Dumitrașcu, Monica; Grigorescu, Ines; Mitrică, Bianca

doi:10.1007/s10666-019-09686-6

Cited by 25 publications

(34 citation statements)

References 70 publications

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“…Furthermore, the economic hardships and weakened institutions of the transition period [68,69], as well as the shadow businesses coupled with corruption [70] led to massive forest exploitation, including both legal and illegal logging [53,57]. Therefore, increased deforestation rates have been reported after 1990, and particularly after 2000 at national and regional scale, (e.g., [56][57][58][71][72][73][74]), resulting not only in regionally important forest area decreases, but also in the significant loss of valuable ecosystems and old-growth forests [75]. In contrast, the expansion of the forest-cover area was also recorded in relation to the natural or artificial regeneration.…”

Section: Study Areamentioning

confidence: 99%

“…In contrast, the expansion of the forest-cover area was also recorded in relation to the natural or artificial regeneration. The artificial afforestation and re-afforestation were mainly directly dependent on the financial sources provided by the different projects and programmes, [57,58,76], mostly accessed in the pre-and post-accession to the European Union. Furthermore, changes in land-use and land management have also been indicated as the main influences of forest re-growth.…”

Section: Study Areamentioning

confidence: 99%

“…Thus, many studies have reported significant regional forest-cover changes, mainly indicating increased deforestation rates (e.g., [53][54][55][56][57]). Furthermore, a recent forest-cover pattern change scenario in Romania [58] suggests an overall increasing trend in forest-cover area in the near future, mainly in mountain areas, as a result of the decrease in human pressure and/or in relation to environmental changes. At the same time, important forest loss is predicted for the tableland, plateau and for plain regions, mainly as a result of them being converted into agricultural lands or artificially expanded areas.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, important forest loss is predicted for the tableland, plateau and for plain regions, mainly as a result of them being converted into agricultural lands or artificially expanded areas. These changes may point to possible environmental implications, having consequences in habitat fragmentation and leading to the occurrence of natural hazardous phenomena [55,58], and forest management [59].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario

Dumitrașcu

Kucsicsa

Dumitrică

et al. 2020

Forests

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The aboveground forest biomass plays a key role in the global carbon cycle and is considered a large and constant carbon reservoir. Hence, exploring the future potential changes in forest-cover pattern can help to estimate the trend of forest biomass and therefore, carbon stock in a certain area. As a result, the present paper attempts to model the potential changes in aboveground forest carbon stock based on the forest-cover pattern scenario simulated for 2050. Specifically, the resulting aboveground forest biomass, estimated for 2015 using the allometric equation based on diameter at breast height and the estimated forest density, was used as baseline data in the present approach. These spatial data were integrated into the forest-cover pattern scenario, predicted by using a spatially explicit model, i.e., the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), in order to estimate the potential variation of aboveground forest carbon stock. Our results suggest an overall increase by approximately 4% in the aboveground forest carbon stock until 2050 in Romania. However, important differences in the forest-cover pattern change were predicted on the regional scale, thus highlighting that the rates of carbon accumulation will change significantly in large areas. This study may increase the knowledge of aboveground forest biomass and the future trend of carbon stock in the European countries. Furthermore, due to their predictive character, the results may provide a background for further studies, in order to investigate the potential ecological, socio-economic and forest management responses to the changes in the aboveground forest carbon stock. However, in view of the uncertainties associated with the data accuracy and methodology used, it is presumed that the results include several spatial errors related to the estimation of aboveground forest biomass and simulation of future forest-cover pattern change and therefore, represent an uncertainty for the practical management of applications and decisions.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario

Dumitrașcu

Kucsicsa

Dumitrică

et al. 2020

Forests

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“…By itself, the Markov Chain is not capable of prediction, but the synergetic use of CA and the Markov Chain is capable of providing information on the spatiotemporal dynamics and future scenarios [41]. These stochastic algorithms consider the interaction effects of the spatial and temporal dynamics for forecasting future forest cover changes [42][43][44][45] that aid decision making [46,47]. These integrated models are based on the initial distribution and transition matrix.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

Malhi

Asundi

Srivastava

et al. 2020

IJGI

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Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

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Effect of the Grain‐for‐Green Program on forest fragmentation

Zhang

et al. 2023

Land Degrad Dev

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Fragmentation profoundly alters the character and pattern of forests, influencing biodiversity in the context of global climate change. Forest area density (FAD) is currently considered the most effective method for characterizing forest fragmentation. Therefore, reasonable and accurate quantification of FAD and its relationship with forest cover changes are essential for evaluating forest fragmentation in the Loess Plateau under the implementation of the Grain‐for‐Green Program (GGP). Based on land use/cover (LULC) in 1998–2022, we integrated the Random forest (RF) algorithm and the conversion of land use and its effects at small regional extent (CLUE‐S) model to derive LULC in 2030. Subsequently, we proposed a multi‐scale window threshold to evaluate forest fragmentation by constructing nonlinear models. Furthermore, we explored the relationship between FAD and forest cover changes by spatial regression models. The results are as follows: (1) in 1998–2030, the main conversion of LULC was of the cropland to broad‐leaf forests and grassland. (2) The 43 × 43 window served as the optimal multi‐scale threshold to assess forest fragmentation. (3) The GGP slowed down forest fragmentation. (4) The degree of fragmentation in broad‐leaf forests was higher than that in coniferous forests. (5) Spatial error model (SEM) was the most appropriate for establishing the spatial relationship between forest fragmentation and forest cover changes. In conclusion, since the implementation of the GGP, forest cover changes had a significant impact on forest fragmentation and its multi‐scale threshold. This research provides a basis for the reasonable configuration of land resources in the Loess Plateau and novel methods for studying forest cover changes and fragmentation.

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Assessing the Potential Future Forest-Cover Change in Romania, Predicted Using a Scenario-Based Modelling

Cited by 25 publications

References 70 publications

Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario

Estimation of Future Changes in Aboveground Forest Carbon Stock in Romania. A Prediction Based on Forest-Cover Pattern Scenario

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

Effect of the Grain‐for‐Green Program on forest fragmentation

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