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Forests
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Impact of Land Use Change on Forest Biodiversity
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Impact of Land Use Change on Forest Biodiversity

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: closed (1 April 2020) | Viewed by 30159

Special Issue Editor

Dr. Daniela Smiraglia

E-Mail Website
Guest Editor
Institute for Environmental Protection and Research (ISPRA), Rome, Italy
Interests: change detection; spatial pattern analysis; land degradation; vegetation ecology; ecoregion classification; remote sensing

Special Issue Information

Dear Colleagues,

In the last century, accelerated ecosystem transformations have been identified as one of the major environmental problems at the global scale. Urbanization, industrialization, land abandonment and the overexploitation of natural resources have been regarded as relevant factors underlying land use changes. The human impact on the environment is very relevant because it is related to phenomena leading to climate change, land degradation and social changes, often acting in combination with each other and intensifying the effect. In particular biodiversity loss is one of the main phenomenon that confronts humankind when dealing with the sustainable management of natural resources. The loss of biodiversity and the reduction of the efficiency of related ecosystem services entail social and economic costs that affect not only environmental sustainability but also human well-being. Within this context, forests represent important reservoirs of flora and fauna biodiversity at all levels: from old-growth forests to agroforestry systems or to urban forests.

Over the last decades, numerous studies have highlighted the important role played by forests in maintaining a high degree of biological diversity, as well as the maintenance of mitigation and adaptation functions to climate change. At the same time they shed light on the main threats such as: wildfires, plant pathologies, habitat fragmentation, invasive alien species, the low consideration of ecosystem services, the inadequacy of planning and management tools and of monitoring programs. All these factors impact the fragile forest ecosystem. The great advances in scientific analysis have improved the measurement of land use change, the understanding of the causes, and the development of predictive models. Predicting how land-use changes affect forest ecosystems requires a good understanding of the human–environment interactions associated with land-use change. This Special Issue aims to gather emerging scientific research conducted in the framework of the land use change of forest ecosystems and the impact on biodiversity conducted across regions and scales, as well as the implications on which to base forest management and monitoring programs.

Dr. Daniela Smiraglia
Guest Editor

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Keywords

  • Global change
  • Fragmentation
  • Land vulnerability
  • Monitoring
  • Sustainable management
  • Ecosystem services
  • Change detection

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Published Papers (5 papers)

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Research

18 pages, 5958 KiB  
Article
Incorporating Landscape Character in Cork Oak Forest Expansion in Sardinia: Constraint or Opportunity?
by Ioannis N. Vogiatzakis, Geoffrey H. Griffiths and Maria Zomeni
Forests 2020, 11(5), 593; https://doi.org/10.3390/f11050593 - 24 May 2020
Cited by 2 | Viewed by 3994
Abstract
Cork oak (Quercus suber) is a declining woodland species across the island of Sardinia, despite its former economic importance for wine production and its significance for biodiversity. In particular, cork oak forests (COFs) on the island have seen a 29% decrease [...] Read more.
Cork oak (Quercus suber) is a declining woodland species across the island of Sardinia, despite its former economic importance for wine production and its significance for biodiversity. In particular, cork oak forests (COFs) on the island have seen a 29% decrease in the past 45 years. A spatial GIS model was developed to determine suitability for the expansion of cork oak forests on the island. The model uses a set of simple spatial decision rules based on principles of landscape ecology and expert opinion to assign a suitability score for pure cork oak forests to every land use parcel in Sardinia. These rules include the type of existing land parcel, its size, distance to existing cork oak forest, and the area of seminatural habitats in its neighborhood. This was coupled with a map of landscape types to assist with the development of policy for the protection of cork oak forests across Sardinia. The results show that there is an area of 116,785 ha potentially suitable for cork oak forest expansion in Sardinia, with the largest area of potential habitat on granitic mountains. There is a substantial overall agreement (Cohen’s kappa = 0.61) between the suitability map produced and the historical reference map. The model is flexible and can be rerun to reflect changes in policy relating to agri-environmental targets for habitats and species. Full article
(This article belongs to the Special Issue Impact of Land Use Change on Forest Biodiversity)
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23 pages, 4158 KiB  
Article
Assessing Habitat Suitability of Parasitic Plant Cistanche deserticola in Northwest China under Future Climate Scenarios
by Jing Liu, Yang Yang, Haiyan Wei, Quanzhong Zhang, Xuhui Zhang, Xiaoyan Zhang and Wei Gu
Forests 2019, 10(9), 823; https://doi.org/10.3390/f10090823 - 19 Sep 2019
Cited by 20 | Viewed by 3920
Abstract
Cistanche deserticola Ma, a perennial parasitic herb of family Orobanchaceae, is mainly parasitic on the roots of the Haloxylon ammodendron Bunge. In view of this special parasitic relationship, we applied random forest (RF) model to forecast potential geographic distribution, and developed a comprehensive [...] Read more.
Cistanche deserticola Ma, a perennial parasitic herb of family Orobanchaceae, is mainly parasitic on the roots of the Haloxylon ammodendron Bunge. In view of this special parasitic relationship, we applied random forest (RF) model to forecast potential geographic distribution, and developed a comprehensive habitat suitability model by integrating bioclimatic and soil factors to assess the suitable distribution of C. deserticola and H. ammodendron across China in 2050s and 2070s under RCP2.6, RCP4.5, and RCP8.5, respectively. We modeled the core potential geographic distribution of C. deserticola by overlaying the distribution of these two species, and analyzed the spatial distribution pattern and migration trend of C. deserticola by using the standard deviational ellipse. In addition, we evaluated the accuracy of RF model through three evaluation indexes, and analyzed the dominant climate factors. The results showed that the core potential distribution areas of C. deserticola are distributed in the Xinjiang Uygur Autonomous Region, the junction of Shaanxi–Gansu–Ningxia provinces, and the Inner Mongolia Autonomous Region. The spatial dispersion would intensify with the increasing of emission scenarios, and the geographical habitat is moving towards higher latitude. Among the three evaluation indexes, the area under the ROC curve (AUC) and True Skill Statistic (TSS) have better assessment results. The main bioclimatic factors affecting the distribution are min temperature of coldest month (Bio6), annual precipitation (Bio12), precipitation of wettest month (Bio13), precipitation of wettest quarter (Bio16), and precipitation of warmest quarter (Bio18), among which the importance of precipitation factors is greater than temperature factors. More importantly, the results of this study could provide some guidance for the improvement of desert forest system, the protection of endangered species and the further improvement of the ecological environment. Full article
(This article belongs to the Special Issue Impact of Land Use Change on Forest Biodiversity)
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25 pages, 2877 KiB  
Article
Semi-Supervised Classification and Landscape Metrics for Mapping and Spatial Pattern Change Analysis of Tropical Forest Types in Thua Thien Hue Province, Vietnam
by Truong Thi Cat Tuong, Hiroshi Tani, Xiufeng Wang and Nguyen Quang Thang
Forests 2019, 10(8), 673; https://doi.org/10.3390/f10080673 - 9 Aug 2019
Cited by 6 | Viewed by 3684
Abstract
Research Highlights: In this study, we classified natural forest into four forest types using time-series multi-source remotely sensed data through a proposed semi-supervised model developed and validated for mapping forest types and assessing forest transition in Vietnam. Background and Objectives: Data on current [...] Read more.
Research Highlights: In this study, we classified natural forest into four forest types using time-series multi-source remotely sensed data through a proposed semi-supervised model developed and validated for mapping forest types and assessing forest transition in Vietnam. Background and Objectives: Data on current forest state and changes detection are always essential for forest management and planning. There is, therefore, a need for improved tools to classify and evaluate forest dynamics more accurately and effectively. Our objective is to develop such tools using a semi-supervised model and landscape metrics to classify and map changes in natural forest types by using multi-source remotely sensed data. Materials and Methods: A combination of Landsat data with PALSAR and PALSAR-2 was used for forest classification through the proposed semi-supervised model. This model turned a kernel least square into a self-learning algorithm, trained by a small number of samples with given labels, and then used this classifier to assign labels to the unlabeled data. The overall accuracy, kappa, user’s accuracy, and producer’s accuracy were used to evaluate the classification accuracy by comparing the classified image with the results of ground truth interpretation. Based on the classified images, forest transition was evaluated using certain landscape metrics at the class and landscape levels. Results: The multi-source data approach achieved improved discrimination of forest types compared to only using single data (optical or radar data). Good classification accuracies were obtained, with kappas of 0.81, 0.76, and 0.74 for the years 2007, 2010, and 2016, respectively. The analysis of landscape metrics indicated that there were different behaviors in the four forest types, as well as provided much information about the trends in spatial pattern changes. Conclusions: This study highlights the utilization of a semi-supervised model in forest classification, and the analysis of forest transition using landscape metrics. However, future research should include a comparison of different models to estimate the improvement of the proposed model. Another important study that should be conducted is to test the proposed method on larger areas. Full article
(This article belongs to the Special Issue Impact of Land Use Change on Forest Biodiversity)
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20 pages, 6120 KiB  
Article
Urban Green Space Fragmentation and Urbanization: A Spatiotemporal Perspective
by Fangzheng Li, Wei Zheng, Yu Wang, Junhui Liang, Shuang Xie, Shiyi Guo, Xiong Li and Changming Yu
Forests 2019, 10(4), 333; https://doi.org/10.3390/f10040333 - 13 Apr 2019
Cited by 80 | Viewed by 11846
Abstract
Urbanization leads to the occupation of green areas, directly contributing to a high level of fragmentation of urban green spaces, which, in turn, results in numerous socioeconomic and environmental problems. Consequently, an understanding of the relationships between patterns of urban green spaces and [...] Read more.
Urbanization leads to the occupation of green areas, directly contributing to a high level of fragmentation of urban green spaces, which, in turn, results in numerous socioeconomic and environmental problems. Consequently, an understanding of the relationships between patterns of urban green spaces and urbanization processes is essential. Although previous quantitative studies have examined this relationship, they have not included an exploration of spatial heterogeneities in the effects of urbanization on the spatial patterns of urban green areas. We therefore applied a spatiotemporal perspective to examine the above relationship, while considering the wider planning context. First, we quantified the extent of fragmentation of urban green spaces using landscape metrics comprising the largest patch index (LPI) and landscape shape index (LSI). Next, using the calculated spatial metrics and nighttime light data (NTL) for central Beijing for the period 1992–2016, we applied a geographically weighted regression model to assess variations in the spatiotemporal effects of urbanization on the fragmentation of urban green spaces. The results showed that urbanization initially occurred mainly in the northern parts of Beijing, whereas urbanization of southern urban fringe areas occurred after 2008. The reduction in green spaces along with increasing fragmentation and complex spatial patterns are indicative of issues relating to Beijing’s rapid urbanization and planning policies. This study contributes to an understanding of how urbanization influences fragmentation of urban green spaces and offers insights for the planning of urban green spaces from the perspective of promoting sustainability. Full article
(This article belongs to the Special Issue Impact of Land Use Change on Forest Biodiversity)
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14 pages, 1701 KiB  
Article
Profile, Level of Vulnerability and Spatial Pattern of Deforestation in Sulawesi Period of 1990 to 2018
by Syamsu Rijal, Roland A. Barkey, Nasri Nasri and Munajat Nursaputra
Forests 2019, 10(2), 191; https://doi.org/10.3390/f10020191 - 20 Feb 2019
Cited by 16 | Viewed by 4947
Abstract
Deforestation is an event of loss of forest cover to another cover. Sulawesi forests have the potential to be deforested as with Sumatra and Kalimantan. This study aims to provide information on deforestation events in Sulawesi from 1990 to 2018. The data used [...] Read more.
Deforestation is an event of loss of forest cover to another cover. Sulawesi forests have the potential to be deforested as with Sumatra and Kalimantan. This study aims to provide information on deforestation events in Sulawesi from 1990 to 2018. The data used in this study are (1) land cover in 1990, 2000, 2010; (2) Landsat 8 imagery in 2018; (3) administrative map of BIG in 2018. The methods used are (1) image classification with on-screen digitation techniques following the PPIK land cover classification guidelines, Forestry Planning Agency (2008) using ArcGIS Desktop 10.6 from ESRI; (2) overlapping maps; (3) analysis of deforestation; (4) analysis of deforestation profiles, (5) vulnerability analysis; and (6) analysis of distribution patterns of deforestation. The results showed that the profile of deforestation occurring on Sulawesi Island in the 1990–2018 observation period was dominated by profile 3-1-1 (the proportion of large forest area, the highest incidence of deforestation early stage at the beginning, at a low rate) in 13 districts. The level of vulnerability to deforestation is a non-vulnerable category (37 districts) which is directed to become a priority in handling deforestation in Sulawesi. Spatial patterns of the deforestation that occurred randomly and were scattered are dominated by shrubs, dryland agricultural activities, and small-scale plantations. Full article
(This article belongs to the Special Issue Impact of Land Use Change on Forest Biodiversity)
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