Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.     

晋北地区土地利用覆被格局的演变与模拟

区域土地利用覆被变化及未来发展情景对区域土地管理和可持续发展具有重要意义。以地处农牧交错带、土地利用覆被变化剧烈的晋北地区为研究区,获取其2010、2015年的土地利用覆被(Land use/land cover,LULC)数据,选取高程、人口、经济、气温、降水等9种影响因素作为驱动因子,采用CLUE-S模型拟合研究区2015年的土地覆被格局并判断拟合精度,在此基础上,分别设置了3种社会经济发展情景,模拟这些情景下研究区2020年的土地利用覆被格局演变。结果表明:1)晋北地区土地利用覆被以耕地、林地和草地为主,各类型土地主要呈西北斜向的条带状分布;2)Logistic回归模型可以很好地提取LULC与驱动因子之间的关系,反映不同的驱动因素对不同的土地利用类型分布格局的影响效果及程度;3)CLUE-S模型在晋北地区土地利用覆被格局的拟合上有较好的精度,模拟Kappa系数值达0.89,表明该模型能够很好地模拟晋北地区的土地利用覆被;4)情景模拟结果表明,研究区生态保护情景(c)下的土地利用覆被格局明显优于维持现状情景(a)和经济优先情景(b),建议在未来土地开发利用过程中,应当减缓工矿用地增加速度,严格控制建设用地规模,优化土地利用格局。     

Interacting Social and Environmental Predictors for the Spatial Distribution of Conservation Lands

Conservation decisions should be evaluated for how they meet conservation goals at multiple spatial extents. Conservation easements are land use decisions resulting from a combination of social and environmental conditions. An emerging area of research is the evaluation of spatial distribution of easements and their spatial correlates. We tested the relative influence of interacting social and environmental variables on the spatial distribution of conservation easements by ownership category and conservation status. For the Appalachian region of the United States, an area with a long history of human occupation and complex land uses including public-private conservation, we found that settlement, economic, topographic, and environmental data associated with spatial distribution of easements (N = 4813). Compared to random locations, easements were more likely to be found in lower elevations, in areas of greater agricultural productivity, farther from public protected areas, and nearer other human features. Analysis of ownership and conservation status revealed sources of variation, with important differences between local and state government ownerships relative to non-governmental organizations (NGOs), and among U.S. Geological Survey (USGS) GAP program status levels. NGOs were more likely to have easements nearer protected areas, and higher conservation status, while local governments held easements closer to settlement, and on lands of greater agricultural potential. Logistic interactions revealed environmental variables having effects modified by social correlates, and the strongest predictors overall were social (distance to urban area, median household income, housing density, distance to land trust office). Spatial distribution of conservation lands may be affected by geographic area of influence of conservation groups, suggesting that multi-scale conservation planning strategies may be necessary to satisfy local and regional needs for reserve networks. Our results support previous findings and provide an ecoregion-scale view that conservation easements may provide, at local scales, conservation functions on productive, more developable lands. Conservation easements may complement functions of public protected areas but more research should examine relative landscape-level ecological functions of both forms of protection.     

Land availability and potential biomass production with poplar and willow short rotation coppices in Germany

Several factors influence land availability for the growth of short rotation coppices (SRC) with fast‐growing tree species, including the nationwide availability of agricultural land, economic efficiency, ecological impacts, political boundaries and environmental protection regulations. In this study, we analysed the growing potential of poplar and willow SRC for bioenergy purposes in Germany without negative ecological impacts or land use conflicts. The potential biomass production using SRC on agricultural land in Germany was assessed taking into account ecological, ethical, political and technical restrictions. Using a geographic information system (GIS), digital site maps, climate data and a digital terrain model, the SRC biomass production potential on cropland and grassland was estimated using water supply and mean temperature during the growing season as parameters. From this analysis, a yield model for SRC was developed based on the analysed growth data and site information of 62 short rotation plantations in Germany and France. To assess the technical, ethical and ecological potential of SRC, restrictions in protected areas, technical constraints and competition with food and feed production were investigated. Our results revealed that approximately 18% (2.12 Mio. ha) of cropland and 54% (2.5 Mio. ha) of grassland in Germany were highly suitable for SRC plantations, providing favourable water supplies and mean temperatures during the growing season. These identified sites produced an average yield of more than 14 tons of dry matter per hectare per year. Due to local climate and soil conditions, the federal states in northern and eastern Germany had the highest theoretical SRC potential for agricultural land. After considering all ecological, ethical, political and technical restrictions, as well as future climate predictions, 5.7% (680 000 ha) of cropland and 33% (1.5 Mio. ha) of grassland in Germany were classified as suitable for biomass production with fast‐growing tree species in SRC.     

基于地理模型与优化的城市扩张与生态保护二元空间协调优化

协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特性,提出将地理模拟与优化(Geographical Simulation and Optimization Systems,简写为GeoSOS)等复杂GIS空间分析技术引入规划决策分析。通过利用最小累积阻力模型获取生态敏感区保护压力格局,并利用元胞自动机模型进行城市扩张模拟,分析城市惯性扩张模式对生态敏感区的潜在影响;然后根据生态敏感区保护和城市空间扩张的协调性发展目标进行生态适宜性评价,进而利用蚁群智能空间优化配置模型产生一种优化的城市空间布局方案。研究以我国珠江三角洲地区的广州市为案例,详细分析了基于GeoSOS的城市扩张与生态保护的协调决策过程。结果表明,整合了城市发展惯性与生态敏感区保护双重目标的空间优化布局方案,比单纯基于地理模拟进行规划布局更符合生态型城市建设需求,研究所提出的城市与生态二元空间协调分析框架可为城市规划提供可靠的定量决策支撑。     

Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes

This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands.     

Assessing impact of forest landscape dynamics on migratory corridors: a case study of two protected areas in Himalayan foothills

Contiguity of protected areas (PAs) is a critical factor to promote well being of the native flora, fauna and life support system to humans. Such contiguity cannot be guaranteed without providing a path or ‘a corridor’ through forested landscapes that includes natural land cover and undisturbed patches. Incidentally, the Himalayan foothills have greater pressure on these landscapes due to high human dependence for livelihood. This pressure is expected to increase in the coming years altering the potential corridors between PAs. The PA managers need flexible processing, modeling and decision tools to propose a range of acceptable corridors between the PAs and ensure their sustainable health. Such flexible tools can be utilized in future to modify for taking decision to conserve the patches connecting patches and adapt as per changing landscapes. This article describes utility of geospatial modeling tools to assess the status of corridors in light of changing landscapes between Rajaji and Jim Corbett National Park, the two most important PAs in the Himalayan foothills. The work has been carried out in four stages, first—using satellite data land use land cover (LULC) maps were prepared for year 1990, 2000 and 2005, second—Land Change Modeler (LCM) was used for LULC change analysis, third—Multi Layer Perceptron Neural Network (MLPNN) was used to predict the status of LULC for 2015 and 2020, and fourth—using temporal morphology of the areas behaving both as barrier and easiness, friction surface cost was calculated to identify least cost pathways (LCPs)/migratory corridors between the PAs. The LULC maps for 1990, 2000 and 2005 were evaluated using accuracy assessment (80%) and Khat statistics (>0.79). The change prediction model was validated by comparing actual LULC of 2005 with predicted LULC of 2005 and the agreement was 71%. The LCP has shifted with the predicted change in the classes. The corridor has shifted by 0.5–3 km towards the south and has come closer to the agriculture fields and river channels.     

Conservation Efforts May Increase Malaria Burden in the Brazilian Amazon

Background

Large-scale forest conservation projects are underway in the Brazilian Amazon but little is known regarding their public health impact. Current literature emphasizes how land clearing increases malaria incidence, leading to the conclusion that forest conservation decreases malaria burden. Yet, there is also evidence that proximity to forest fringes increases malaria incidence, which implies the opposite relationship between forest conservation and malaria. We compare the effect of these environmental factors on malaria and explore its implications.

Methods and Findings

Using a large malaria dataset (∼1,300,000 positive malaria tests collected over ∼4.5 million km²), satellite imagery, permutation tests, and hierarchical Bayesian regressions, we show that greater forest cover (as a proxy for proximity to forest fringes) tends to be associated with higher malaria incidence, and that forest cover effect was 25 times greater than the land clearing effect, the often cited culprit of malaria in the region. These findings have important implications for land use/land cover (LULC) policies in the region. We find that cities close to protected areas (PA’s) tend to have higher malaria incidence than cities far from PA’s. Using future LULC scenarios, we show that avoiding 10% of deforestation through better governance might result in an average 2-fold increase in malaria incidence by 2050 in urban health posts.

Conclusions

Our results suggest that cost analysis of reduced carbon emissions from conservation efforts in the region should account for increased malaria morbidity, and that conservation initiatives should consider adopting malaria mitigation strategies. Coordinated actions from disparate science fields, government ministries, and global initiatives (e.g., Reduced Emissions from Deforestation and Degradation; Millenium Development Goals; Roll Back Malaria; and Global Fund to Fight AIDS, Tuberculosis and Malaria), will be required to decrease malaria toll in the region while preserving these important ecosystems.     

Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.     

Effectiveness of natural protected areas to prevent land use and land cover change in Mexico

This study evaluated the extent to which natural protected areas (NPAs) in Mexico have been effective for preventing land use/land cover change, considered as a major cause of other degradation processes. We developed an effectiveness index including NPA percentage of transformed areas (agriculture, induced vegetation, forestry plantations, and human settlements) in 2002, the rate and absolute extent of change in these areas (1993–2002), the comparison between rates of change observed inside the NPA and in an equivalent surrounding area, and between the NPA and the state(s) in which it is located. We chose 69 terrestrial federal NPAs, decreed before 1997, that were larger than 1,000 ha, not urban/reforested with non-native vegetation, not islands and not coastal strips, and estimated the extent of transformed areas using 1993 and 2002 land use/land cover maps. Over 54% of NPAs were effective, and were heterogeneously distributed by management categories: 65% of Biosphere Reserves, 53% of Flora and Fauna Protection Areas, and 45% of National Parks. 23% of NPAs were regarded as weakly effective, and the remaining 23% as non-effective. We recognize the importance of NPAs as a relevant conservation instrument, as half of NPAs analyzed (particularly biosphere reserves) prevented natural vegetation loss compared with their geographic context. Our results suggest that conservation based on NPAs in Mexico still faces significant challenges. Our approach can be expanded for evaluating the effectiveness of NPA in other regions, as land use/land cover maps are now available almost worldwide.     

Evaluating the Return in Ecosystem Services from Investment in Public Land Acquisitions

We evaluate the return on investment (ROI) from public land conservation in the state of Minnesota, USA. We use a spatially-explicit modeling tool, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), to estimate how changes in land use and land cover (LULC), including public land acquisitions for conservation, influence the joint provision and value of multiple ecosystem services. We calculate the ROI of a public conservation acquisition as the ratio of the present value of ecosystem services generated by the conservation to the cost of the conservation. For the land scenarios analyzed, carbon sequestration services generated the greatest benefits followed by water quality improvements and recreation opportunities. We found ROI values ranged from 0.21 to 5.28 depending on assumptions about future land use change, service values, and discount rate. Our study suggests conservation is a good investment as long as investments are targeted to areas with low land costs and high service values.     

地市尺度气候调节服务评估——以福州市为例

生态系统服务是衔接生态系统功能和人类福利的桥梁,气候调节服务在生态系统服务中占有极其重要的地位。对气候调节服务发生的全过程进行评估,对科学开展生态系统服务评估具有重要意义。本研究以福州市为案例,开展地市尺度气候调节服务评估,分析气候调节服务在行政单元、地类尺度上的时空变化特征。结果表明: 2015、2018年,福州市气候调节服务总实物量分别为4.01×10¹² MJ(价值量6139.44亿元,GDP为5618.08亿元)和4.66×10¹² MJ(价值量7140.02亿元,GDP为7856.81亿元),气候调节服务价值大致与当年GDP相当。主要的土地利用/覆被类型是森林、耕地、水域,分别占福州市国土面积的57%、15%和9%;水域对福州市气候调节服务贡献最大,2018年贡献度超过60%,高于林地(12%)及耕地(13%)。建成区、东部农耕区域气候调节服务价值较低。2015、2018年,福州市土地利用/覆被变化面积为1805.5 km²,变化最大的用地类型是耕地、林地,主要的土地利用转移方向是耕地与林地、林地与园地、耕地与城镇村及工矿用地之间的转化;气候调节服务总实物量变化了6.74×10¹¹ MJ,相应的价值变化量为1035.8亿元;气候调节服务变化集中在闽侯、闽清、永泰等中西部地区,以及罗源、福清等西部山区;水域的气候调节服务变化最剧烈,水域类型转化会产生极为强烈的气候调节服务变化,远高于其他用地类型转换的效果。     

Assessing the impacts of ecological governance on carbon storage in an urban coal mining subsidence area

Carbon storage, which is considered one of the important service functions of ecosystems, plays an irreplaceable role in maintaining the regional carbon balance and regulating the climate. Regional carbon storage is closely related to regional land use and land cover (LULC). With the development and expansion of coal resource-based cities, the construction areas of cities have started to overlap with underground coal resources. Coal mining leads to regional LULC changes, such as large-scale surface subsidence and subsidence waterlogging, and LULC has changed and consequently affected carbon storage in urban coal mining subsidence areas. This study analyses the change trend of carbon storage and clarifies the effect of ecological governance being implemented in the urban coal mining subsidence area. First, the LULC change map of the ecological governance scenario was obtained via remote sensing technology. Then, the natural evolution scenario from 2000 to 2021 was simulated using the hybrid cellular automata and Markov chain, also named the CA–Markov model. Finally, combined with the subsidence waterlogging in the urban subsidence area, the InVEST model was used to analyse the spatial–temporal variation characteristics of carbon storage. The analysis results showed that LULC and carbon storage in small-scale urban coal mining subsidence areas changed dramatically between 2000 and 2021 due to coal mining and ecological governance. The subsidence waterlogging area increased by 1033.83 ha, resulting in total carbon storage decreasing by 37,560.21 t. Subsidence waterlogging is the key influencing factor in the decrease in carbon storage. The forest area increased by 1270.83 ha, resulting in a total carbon storage increase of 216,531.04 t. Forest is the crucial increasing factor in carbon storage. The changes in carbon storage in the urban coal mining subsidence area can be classified as follows: obvious improvement area, basically unchanged area, and significantly degraded area. As opposed to the natural evolution scenario, the ecological governance scenario increased the coverage of the “obvious improvement area” by 818.46 ha in the urban coal mining subsidence area. Overall, this study illustrates that ecological governance can effectively improve carbon sequestration and is conducive to the healthy development of coal resource-based cities.     

Assessment of soil erosion risk using an integrated approach of GIS and Analytic Hierarchy Process (AHP) in Erzurum,Turkiye

Assessing soil erosion hazards and mapping the spatial distribution of soil erosion have an essential role in sustainable forest management. In this study, the potential soil erosion risk was evaluated through the Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) in the Oltu forest planning unit, Erzurum. Seven erosion-related criteria, including slope, bedrock type, relative relief, drainage density and frequency, rainfall, and land use/land cover (LULC) were used for the present assessment. According to the AHP analysis, the slope was the most influential factor (21%) followed by bedrock type (19%), land cover (17%), and relative relief (14%) in the soil erosion process. The soil erosion risk in the study area was strongly influenced by the LULC where 59.46% is bare land with high erosion risk and 12.07%, with the lowest risk, is in an area with any forest cover. The estimated soil erosion risk was classified into five different classes namely very low, low, moderate, high, and very high. The results showed that this study area is highly prone to soil erosion. The larger proportion of the area (39.16%) is exposed to high to very high erosion, mainly determined by forest cover and geomorphology. To analyze the accuracy of the soil erosion risk map, 40 points were selected randomly in this study area. In these points, predicted values were compared to the real values obtained by Google Earth-colored images. The area under the ROC curve (AUC) method was applied to validate the efficiency of the AHP which showed a satisfactory accuracy of 81.00%. Findings presented that including the more influencing factors with a slope instead of including only the slope contributes to a more accurate erosion risk map. This study highlighted that GIS-based multi-criteria decision-making is a valuable and practical tool for decision-makers and land managers in creating soil erosion susceptibility maps and determining high-priority areas that require conservation measures for sustainable land use management by reducing the economic and ecological impacts of soil loss. Also, this approach can be practically applied in other planning units.     

Simulating Deforestation in Minas Gerais,Brazil, under Changing Government Policies and Socioeconomic Conditions

Agricultural expansion is causing deforestation in Minas Gerais, Brazil, converting savanna and tropical dry forest to farmland, and in 2012, Brazil’s Forest Code was revised with the government reducing deforestation restrictions. Understanding the effects of policy change on rates and locations of natural ecosystem loss is imperative. In this paper, deforestation in Minas Gerais was simulated annually until 2020 using Dinamica Environment for Geoprocessing Objects (Dinamica EGO). This system is a state-of-the-art land use and cover change (LUCC) model which incorporates government policy, landscape maps, and other biophysical and anthropogenic datasets. Three studied scenarios: (i) business as usual, (ii) increased deforestation, and (iii) decreased deforestation showed more transition to agriculture from shrubland compared to forests, and consistent locations for most deforestation. The probability of conversion to agriculture is strongly tied to areas with the smallest patches of original biome remaining. Increases in agricultural revenue are projected to continue with a loss of 25% of the remaining Cerrado land in the next decade if profit is maximized. The addition of biodiversity value as a tax on land sale prices, estimated at over $750,000,000 USD using the cost of extracting and maintaining current species ex-situ, can save more than 1 million hectares of shrubland with minimal effects on the economy of the State of Minas Gerais. With environmental policy determining rates of deforestation and economics driving the location of land clearing, site-specific protection or market accounting of externalities is needed to balance economic development and conservation.     

Regional Scale Prioritisation for Key Ecosystem Services,Renewable Energy Production and Urban Development

Although the importance of addressing ecosystem service benefits in regional land use planning and decision-making is evident, substantial practical challenges remain. In particular, methods to identify priority areas for the provision of key ecosystem services and other environmental services (benefits from the environment not directly linked to the function of ecosystems) need to be developed. Priority areas are locations which provide disproportionally high benefits from one or more service. Here we map a set of ecosystem and environmental services and delineate priority areas according to different scenarios. Each scenario is produced by a set of weightings allocated to different services and corresponds to different landscape management strategies which decision makers could undertake. Using the county of Cornwall, U.K., as a case study, we processed gridded maps of key ecosystem services and environmental services, including renewable energy production and urban development. We explored their spatial distribution patterns and their spatial covariance and spatial stationarity within the region. Finally we applied a complementarity-based priority ranking algorithm (zonation) using different weighting schemes. Our conclusions are that (i) there are two main patterns of service distribution in this region, clustered services (including agriculture, carbon stocks, urban development and plant production) and dispersed services (including cultural services, energy production and floods mitigation); (ii) more than half of the services are spatially correlated and there is high non-stationarity in the spatial covariance between services; and (iii) it is important to consider both ecosystem services and other environmental services in identifying priority areas. Different weighting schemes provoke drastic changes in the delineation of priority areas and therefore decision making processes need to carefully consider the relative values attributed to different services.     

鱼类生物群落对太子河流域土地利用、河岸带栖息地质量的响应

河流生态系统的退化是多空间尺度环境因子作用的结果。探讨不同尺度环境因子及水生生物之间的作用关系,识别影响水生生物群落完整性的尺度问题,是有效开展水生生物保护的基础。基于2009年对太子河流域15个样点的鱼类、河岸带栖息地质量评价,结合遥感影像解译的太子河流域土地利用情况(包括流域尺度和河段尺度),研究鱼类完整性指数(F-IBI)与两种尺度土地利用、栖息地质量参数之间的关系。结果表明太子河上游地区河岸栖息地质量较好,下游地区由于农业用地、城镇用地比例的增加河岸栖息地质量明显下降。F-IBI与自然用地比例呈正相关,与农业、城镇用地比例呈负相关。农业用地对F-IBI的影响体现在流域尺度,而城镇用地在两种尺度上都存在显著影响。相比于农业用地,城镇用地相同比例的增加会导致F-IBI更快的下降。底质、水质状况、人类活动强度是显著影响F-IBI的栖息地质量评价参数。3项参数均随农业和城镇用地比例增加而降低,农业用地主要在流域尺度上对3项参数产生影响,城镇用地主要影响底质和水质状况2项参数,而在两种尺度上的影响相差不大。     

Ecosystem Service Valuation Assessments for Protected Area Management: A Case Study Comparing Methods Using Different Land Cover Classification and Valuation Approaches

Accurate and spatially-appropriate ecosystem service valuations are vital for decision-makers and land managers. Many approaches for estimating ecosystem service value (ESV) exist, but their appropriateness under specific conditions or logistical limitations is not uniform. The most accurate techniques are therefore not always adopted. Six different assessment approaches were used to estimate ESV for a National Nature Reserve in southwest China, across different management zones. These approaches incorporated two different land-use land cover (LULC) maps and development of three economic valuation techniques, using globally or locally-derived data. The differences in ESV across management zones for the six approaches were largely influenced by the classifications of forest and farmland and how they corresponded with valuation coefficients. With realistic limits on access to time, data, skills and resources, and using acquired estimates from globally-relevant sources, the Buffer zone was estimated as the most valuable (2.494 million ± 1.371 million CNY yr^-1 km^-2) and the Non-protected zone as the least valuable (770,000 ± 4,600 CNY yr^-1 km^-2). However, for both LULC maps, when using the locally-based and more time and skill-intensive valuation approaches, this pattern was generally reversed. This paper provides a detailed practical example of how ESV can differ widely depending on the availability and appropriateness of LULC maps and valuation approaches used, highlighting pitfalls for the managers of protected areas.     

Decadal Trend in Agricultural Abandonment and Woodland Expansion in an Agro-Pastoral Transition Band in Northern China

Land use land cover (LULC) changes frequently in ecotones due to the large climate and soil gradients, and complex landscape composition and configuration. Accurate mapping of LULC changes in ecotones is of great importance for assessment of ecosystem functions/services and policy-decision support. Decadal or sub-decadal mapping of LULC provides scenarios for modeling biogeochemical processes and their feedbacks to climate, and evaluating effectiveness of land-use policies, e.g. forest conversion. However, it remains a great challenge to produce reliable LULC maps in moderate resolution and to evaluate their uncertainties over large areas with complex landscapes. In this study we developed a robust LULC classification system using multiple classifiers based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and posterior data fusion. Not only does the system create LULC maps with high statistical accuracy, but also it provides pixel-level uncertainties that are essential for subsequent analyses and applications. We applied the classification system to the Agro-pasture transition band in northern China (APTBNC) to detect the decadal changes in LULC during 2003–2013 and evaluated the effectiveness of the implementation of major Key Forestry Programs (KFPs). In our study, the random forest (RF), support vector machine (SVM), and weighted k-nearest neighbors (WKNN) classifiers outperformed the artificial neural networks (ANN) and naive Bayes (NB) in terms of high classification accuracy and low sensitivity to training sample size. The Bayesian-average data fusion based on the results of RF, SVM, and WKNN achieved the 87.5% Kappa statistics, higher than any individual classifiers and the majority-vote integration. The pixel-level uncertainty map agreed with the traditional accuracy assessment. However, it conveys spatial variation of uncertainty. Specifically, it pinpoints the southwestern area of APTBNC has higher uncertainty than other part of the region, and the open shrubland is likely to be misclassified to the bare ground in some locations. Forests, closed shrublands, and grasslands in APTBNC expanded by 23%, 50%, and 9%, respectively, during 2003–2013. The expansion of these land cover types is compensated with the shrinkages in croplands (20%), bare ground (15%), and open shrublands (30%). The significant decline in agricultural lands is primarily attributed to the KFPs implemented in the end of last century and the nationwide urbanization in recent decade. The increased coverage of grass and woody plants would largely reduce soil erosion, improve mitigation of climate change, and enhance carbon sequestration in this region.