关键词：
Northeast China;land use;spatio-temporal change;scenario;ecosystem service
摘要：
Land use and its dynamics have attracted considerable scientific attention for their significant ecological and socioeconomic implications. Many studies have investigated the past changes in land use, but efforts exploring the potential changes in land use and implications under future scenarios are still lacking. Here we simulate the future land use changes and their impacts on ecosystem services in Northeast China (NEC) over the period of 2000–2050 using the CLUE–S (Conversion of Land Use and its Effects at Small regional extent) model under the scenarios of ecological security (ESS), food security (FSS) and comprehensive development (CDS). The model was validated against remote sensing data in 2005. Overall, the accuracy of the CLUE–S model was evaluated at 82.5%. Obtained results show that future cropland changes mainly occur in the Songnen Plain and the Liaohe Plain, forest and grassland changes are concentrated in the southern Lesser Khingan Mountains and the western Changbai Mountains, while the Sanjiang Plain will witness major changes of the wetlands. Our results also show that even though CDS is defined based on the goals of the regional development plan, the ecological service value (ESV) under CDS is RMB 2656.18 billion in 2050. The ESV of CDS is lower compared with the other scenarios. Thus, CDS is not an optimum scenario for eco-environmental protection, especially for the wetlands, which should be given higher priority for future development. The issue of coordination is also critical in future development. The results can help to assist structural adjustments for agriculture and to guide policy interventions in NEC.
作者机构：
[于雷; 章涛; 朱亚星; 周勇; 夏天; 聂艳] Hubei Provincial Key Laboratory for the Analysis and Simulation of Geographical Process, Central China Normal University, Wuhan;430079, China;College of Urban and Environmental Science, Central China Normal University, Wuhan;Research Institute for Sustainable Development of CCNU, Wuhan;[于雷; 章涛; 朱亚星; 周勇; 夏天; 聂艳] 430079, China <&wdkj&> College of Urban and Environmental Science, Central China Normal University, Wuhan
关键词：
光谱分析;作物;叶绿素;高光谱;特征波长变量;迭代和保留信息变量法;大豆
摘要：
在植物叶绿素特征波长变量筛选过程中,与叶绿素关系较弱的波长变量极易被忽略,导致这些弱信息变量包含叶绿素的有效信息丢失,因此,确定叶片光谱中弱信息变量对揭示叶绿素高光谱响应规律具有重要意义。该研究以江汉平原大豆鼓粒期的叶片为研究对象,采集80组大豆叶片高光谱和SPAD(soil and plant analyzer development)值,分析SPAD值与大豆叶片反射率相关关系和光谱波长变量自相关关系,基于迭代和保留信息变量法(iteratively retains informative variables,IRIV)筛选大豆叶片的特征波长变量,建立偏最小二乘回归(partial least squares regression,PLSR)和支持向量机(support vector machine,SVM)模型估测SPAD值。结果表明,大豆叶片SPAD值与光谱反射率在可见光波段具有极显著负相关,在近红外波段存在不显著的正相关性(P>0.01);可见光、近红外2波段的波长变量之间相关性较弱,但2波段内变量之间的相关性较强;基于IRIV算法确定了大豆叶绿素的特征波长变量,利用特征波长变量建立的估测模型的估测能力高于仅利用强信息波长变量建立的估测模型,表明弱信息变量对估测叶片SPAD值具有重要意义; IRIV-SVM模型估测能力最优,验证集R~2和相对分析误差(RPD)分别为0.73、1.82。该文尝试证明了光谱中弱信息变量的重要性,为揭示叶片高光谱响应机理提供了理论依据。
通讯机构：
[Wu Wenbin] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat, Hubei Prov Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
land use change;CLUE-S;Northeast China;two-way simulation
摘要：
Spatially explicit modeling techniques recently emerged as an alternative to monitor land use changes. This study adopted the well-known CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model to analyze the spatio-temporal land use changes in a hot-spot in Northeast China (NEC). In total, 13 driving factors were selected to statistically analyze the spatial relationships between biophysical and socioeconomic factors and individual land use types. These relationships were then used to simulate land use dynamic changes during 1980-2010 at a 1 km spatial resolution, and to capture the overall land use change patterns. The obtained results indicate that increases in cropland area in NEC were mainly distributed in the Sanjiang Plain and the Songnen Plain during 1980-2000, with a small reduction between 2000 and 2010. An opposite pattern was identified for changes in forest areas. Forest decreases were mainly distributed in the Khingan Mountains and the Changbai Mountains between 1980 and 2000, with a slight increase during 2000-2010. The urban areas have expanded to occupy surrounding croplands and grasslands, particularly after the year 2000. More attention is needed on the newly gained croplands, which have largely replaced wetlands in the Sanjiang Plain over the last decade. Land use change patterns identified here should be considered in future policy making so as to strengthen local eco-environmental security.
作者机构：
[刘目兴; 于雷; 朱亚星; 洪永胜; 夏天; 周勇] Key Laboratory for Geographical Process Analysis & Simulation, Central China Normal University, Wuhan;Hubei Province;430079, China;College of Urban & Environmental Science, Central China Normal University, Wuhan;[刘目兴; 于雷; 朱亚星; 洪永胜; 夏天; 周勇] Hubei Province
关键词：
hyperspectral;inversion;leaf area index;LAI;retrieval
摘要：
The leaf area index (LAI) is an important vegetation parameter, which is used widely in many applications. Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies. During the last two decades, hyperspectral remote sensing has been employed increasingly for crop LAI estimation, which requires unique technical procedures compared with conventional multispectral data, such as denoising and dimension reduction. Thus, we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques. First, we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation. Second, we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types: approaches based on statistical models, physical models (i.e., canopy reflectance models), and hybrid inversions. We summarize and evaluate the theoretical basis and different methods employed by these approaches (e.g., the characteristic parameters of LAI, regression methods for constructing statistical predictive models, commonly applied physical models, and inversion strategies for physical models). Thus, numerous models and inversion strategies are organized in a clear conceptual framework. Moreover, we highlight the technical difficulties that may hinder crop LAI estimation, such as the “curse of dimensionality” and the ill-posed problem. Finally, we discuss the prospects for future research based on the previous studies described in this review.
作者机构：
[夏天; 吴文斌; 周清波] Key Laboratory of Agricultural Information Technology, Ministry of Agriculture, Beijing, 100081, China;[周勇] College of Urban and Environment Sciences, Huazhong Normal University, Wuhan 430079, China;[夏天; 吴文斌; 周清波] Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
通讯机构：
Key Laboratory of Agricultural Information Technology, Ministry of Agriculture, China
关键词：
遥感;回归分析;神经网络;估算;冬小麦;反演方法
摘要：
冬小麦叶面积指数（LAI, leaf area index）是评价其长势和预测产量的重要农学参数,高光谱遥感能够实现快速无损地监测叶面积指数.该文旨在将田间监测与高光谱遥感相结合,探索研究不同冬小麦叶面积指数高光谱反演方法的模拟精度及适应性.针对国际上普遍应用的2种高光谱遥感反演 LAI 模型方法,即回归分析法和 BP神经网络法,在介绍2种 LAI 反演模型的基础上,选择位于黄淮海平原的山东省济南市长清区为研究区域,通过ASD 地物光谱仪和 SunScan 冠层分析系统对冬小麦的冠层光谱及 LAI 变化进行田间观测,然后利用回归分析法和BP 神经网络法构建冬小麦 LAI 反演模型,将模型估算 LAI 值和田间观测 LAI 值进行比对,分析评价2种方法的反演精度.结果表明,BP 神经网络法较回归分析法估算冬小麦 LAI 的精度有较大提高,检验方程的决定系数（R2）为0.990、均方根误差（RMSE）为0.105.利用 BP 神经网络法构建反演模型能较好的对冬小麦 LAI 进行反演.研究结果可为不同冬小麦长势遥感监测提供理论和技术上的支持,并为大尺度传感器监测冬小麦长势和估产提供参考.
