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Forests
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Watershed Scale Forest Restoration and Sustainable Development
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Watershed Scale Forest Restoration and Sustainable Development

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 June 2019) | Viewed by 24062

Special Issue Editor

Dr. Guangyu Wang

E-Mail Website
Guest Editor
Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: forest management; forest policy; land-use change; conservation; ecosystems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Watershed management is an ever-evolving practice involving the management of land, water, biota, and other resources in a defined area for ecological, social, and economic purposes. In this Special Issue, we wish to explore the following questions: How has watershed management evolved? What new tools are available, such as developments in remote sensing, GIS, big data, cloud computing, and multi-level social-ecological systems analysis into watershed management strategies? How can the tools be integrated into sustainable watershed management? What kind of benefits might be obtained from integration across disciplines and jurisdictional boundaries, as well as the incorporation of technological advancements? We also welcome case studies of the successes and failures of integrated watershed management in addressing different ecological, social, and economic dilemmas in geographically diverse locations. We encourage studies from all fields, including integrated management policies, strategy development, traditional knowledge, cross-jurisdictional cooperation and information sharing, advanced data collection and analysis methods, and the consideration of both ecological and socio-economic concerns.

Prof. Dr. Guangyu Wang
Guest Editor

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Keywords

  • Integrated watershed management
  • Adaptive management
  • Climate change impacts
  • Spatial analysis modelling
  • Cloud computing and big data
  • Social-ecological systems analysis
  • Traditional ecological knowledge
  • Watershed management
  • Carbon Sequestration and management
  • Ecosystem Services
  • Land-use and land-cover change

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Published Papers (5 papers)

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Research

22 pages, 5181 KiB  
Article
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed
by Dieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, Marten Geertsema, Ebrahim Omidvar, John J. Clague, Binh Thai Pham, Jie Dou, Dawood Talebpour Asl, Baharin Bin Ahmad and Saro Lee
Forests 2019, 10(9), 743; https://doi.org/10.3390/f10090743 - 28 Aug 2019
Cited by 99 | Viewed by 5134
Abstract
We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), [...] Read more.
We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but “distance to road” was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas. Full article
(This article belongs to the Special Issue Watershed Scale Forest Restoration and Sustainable Development)
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20 pages, 2711 KiB  
Article
If They Come, Where will We Build It? Land-Use Implications of Two Forest Conservation Policies in the Deep Creek Watershed
by Markandu Anputhas, Johannus Janmaat, Craig Nichol and Adam Wei
Forests 2019, 10(7), 581; https://doi.org/10.3390/f10070581 - 12 Jul 2019
Cited by 1 | Viewed by 3084
Abstract
Research Highlights: Forest conservation policies can drive land-use change to other land-use types. In multifunctional landscapes, forest conservation policies will therefore impact on other functions delivered by the landscape. Finding the best pattern of land use requires considering these interactions. Background and Objectives: [...] Read more.
Research Highlights: Forest conservation policies can drive land-use change to other land-use types. In multifunctional landscapes, forest conservation policies will therefore impact on other functions delivered by the landscape. Finding the best pattern of land use requires considering these interactions. Background and Objectives: Population growth continues to drive the development of land for urban purposes. Consequently, there is a loss of other land uses, such as agriculture and forested lands. Efforts to conserve one type of land use will drive more change onto other land uses. Absent effective collaboration among affected communities and relevant institutional agents, unexpected and undesirable land-use change may occur. Materials and Methods: A CLUE-S (Conversion of Land Use and its Effects at Small Scales) model was developed for the Deep Creek watershed, a small sub-basin in the Okanagan Valley of British Columbia, Canada. The valley is experiencing among the most rapid population growth of any region in Canada. Land uses were aggregated into one forested land-use type, one urban land-use type, and three agricultural types. Land-use change was simulated for combinations of two forest conservation policies. Changes are categorized by location, land type, and an existing agricultural land policy. Results: Forest conservation policies drive land conversion onto agricultural land and may increase the loss of low elevation forested land. Model results show where the greatest pressure for removing land from agriculture is likely to occur for each scenario. As an important corridor for species movement, the loss of low elevation forest land may have serious impacts on habitat connectivity. Conclusions: Forest conservation policies that do not account for feedbacks can have unintended consequences, such as increasing conversion pressures on other valued land uses. To avoid surprises, land-use planners and policy makers need to consider these interactions. Models such as CLUE-S can help identify these spatial impacts. Full article
(This article belongs to the Special Issue Watershed Scale Forest Restoration and Sustainable Development)
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21 pages, 10314 KiB  
Article
Does Land Use Change Affect Green Space Water Use? An Analysis of the Haihe River Basin
by Yu Zhao, Xuanchang Zhang, Yang Bai and Feng Mi
Forests 2019, 10(7), 545; https://doi.org/10.3390/f10070545 - 28 Jun 2019
Cited by 6 | Viewed by 2884
Abstract
Research Highlights: Land use/cover change (LUCC) has an impact on the water use efficiency (WUE) of green space in the Haihe River Basin. Background and Objectives: The Haihe River Basin has historically been one of the most water-stressed basins in China. With the [...] Read more.
Research Highlights: Land use/cover change (LUCC) has an impact on the water use efficiency (WUE) of green space in the Haihe River Basin. Background and Objectives: The Haihe River Basin has historically been one of the most water-stressed basins in China. With the increase in green space and economic development, land use and water use in the Haihe River Basin have changed significantly. In order to contribute to the sustainable development of basin water management, the impacts of LUCC on the WUE of the Haihe River Basin were assessed with the goal to support decision makers with regard to water resources planning and watershed management. Materials and Methods: (1) Moderate Resolution Imaging Spectroradiometer (MODIS) data and land use data were used to produce land use/land cover and other related maps. (2) The WUE equation was used to calculate the green space WUE. (3) The contribution rates of changes in land use were assessed to illustrate how LUCC affected green space WUE. Results: (1) Artificial surfaces increased and large areas of farmland were converted to non-agricultural use, accompanied by the addition of green space. (2) Green space WUE increased significantly from 2005 to 2015. The average annual WUE exhibited a relatively uniform spatial distribution in the Haihe River Basin. Except for the central area of urban land, the WUE of most areas exhibited an increasing trend. (3) The impact of LUCC on WUE was mainly a result from the conversion of farmland and artificial surfaces and the increase in green space. Ecological restoration and crop adjustment contributed greatly to the improvement in green space WUE in the basin. Conclusions: Green space WUE of the Haihe River Basin was significantly affected by LUCC and there is room for improvement in the WUE of green spaces in the basin. The paper concludes with recommendations for further research to assist in planning for green space to promote sustainable development related to land use and water management. Full article
(This article belongs to the Special Issue Watershed Scale Forest Restoration and Sustainable Development)
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17 pages, 5423 KiB  
Article
Potential for Forest Restoration and Deficit Compensation in Itacaiúnas Watershed, Southeastern Brazilian Amazon
by Sâmia Nunes, Rosane B. L. Cavalcante, Wilson R. Nascimento, Jr., Pedro Walfir M. Souza-Filho and Diogo Santos
Forests 2019, 10(5), 439; https://doi.org/10.3390/f10050439 - 21 May 2019
Cited by 10 | Viewed by 4516
Abstract
The conservation and restoration of native vegetation is vital for providing key hydrological services (i.e., maintaining high water quality, atmospheric humidity, and precipitation patterns). However, this research area lacks fine-scale studies at the watershed level to evaluate opportunities for forest restoration and deficit [...] Read more.
The conservation and restoration of native vegetation is vital for providing key hydrological services (i.e., maintaining high water quality, atmospheric humidity, and precipitation patterns). However, this research area lacks fine-scale studies at the watershed level to evaluate opportunities for forest restoration and deficit (the shortfall of forest required to be restored or compensated), as well as the implications for watershed management. We provide the first fine-scale estimation of forest and deficit distribution, integrating permanent preservation areas (APPs, in Portuguese) and legal reserves (RL, in Portuguese), according to Brazilian environmental law, for the 41,300 km2 Itacaiúnas watershed in the Brazilian state of Pará, which has lost 50% of its vegetation cover. Using 30 m- and 10 m-resolution imagery, a multi-temporal land use classification was performed by geographic object-based image analysis (GEOBIA). The results were combined with a set of Brazilian regulations on the conservation and restoration of APPs and RL to assess patterns of forest cover and legal compliance. We found that the total RL deficit (4383 km2) was higher than the total forest surplus (above legal obligation) (3241 km2). However, most of this deficit (56%) could be compensated by protecting a forest area in another property within the Amazon biome, while 44% must be legally restored. Only 4% of the total forest surplus can be legally deforested, and the remaining 96% is already protected by law but can be used to compensate for areas under the deficit. We also found that, despite 57% (3017 km2) of the total APP being forested, only 26% (1356 km2) of the APP must be restored and 17% (881 km2) can remain deforested (consolidated areas). The 2012 law revision reduced the obligation to restore RL and APPs. This change could affect hydrological and ecological services. Compensation mechanisms could be used to protect forest within the Itacaiúnas watershed, rather than in the biome, to reduce further deforestation pressure. Full article
(This article belongs to the Special Issue Watershed Scale Forest Restoration and Sustainable Development)
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27 pages, 12078 KiB  
Article
Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling
by Vu Viet Nguyen, Binh Thai Pham, Ba Thao Vu, Indra Prakash, Sudan Jha, Himan Shahabi, Ataollah Shirzadi, Dong Nguyen Ba, Raghvendra Kumar, Jyotir Moy Chatterjee and Dieu Tien Bui
Forests 2019, 10(2), 157; https://doi.org/10.3390/f10020157 - 12 Feb 2019
Cited by 133 | Viewed by 7920
Abstract
This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide [...] Read more.
This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area of Van Chan district, Yen Bai province (Vietnam) was carried out with the help of a spatial database of the area, considering past landslides and 12 landslide conditioning factors. The proposed models were validated using different methods such as Area under the Receiver Operating Characteristics (ROC) curve (AUC), Mean Square Error (MSE), Root Mean Square Error (RMSE). Results indicate that the RFBFDT (AUC = 0.826, MSE = 0.189, and RMSE = 0.434) is the best method in comparison to other hybrid models, namely PSOANFIS (AUC = 0.76, MSE = 0.225, and RMSE = 0.474) and PSOANN (AUC = 0.72, MSE = 0.312, and RMSE = 0.558). Thus, it is reasonably concluded that the RFBFDT is a promising hybrid machine learning approach for landslide susceptibility modeling. Full article
(This article belongs to the Special Issue Watershed Scale Forest Restoration and Sustainable Development)
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