渔子溪流域水文过程影响因素的特征时间尺度分析

采用渔子溪流域三期遥感影像(1986、1994和2002年)获取该区域的土地利用/覆被资料,并结合实际调查数据、采用斑块驱动模型模拟得到该流域1986~2004年间逐年土地利用变化数据;然后根据该模拟数据以及渔子溪流域不同时间尺度的气象和径流资料,采用逐步线性回归和多元非线性回归方法,对渔子溪流域不同时间尺度下降雨-土地利用-径流系统及其内部各元素之间的关系进行了统计分析,结果表明:(1)不同时间尺度下径流量与降雨量的相关关系具有明显差别,土地利用变化对降雨-径流关系有重要影响,在年尺度上,降雨量对径流量的影响要大于气温对径流的影响,而在月尺度上则相反。(2)不同时间尺度下非线性回归的模拟误差要比线性模拟误差下降1.18%~23.8%,且不同影响因子在不同时间尺度上的非线性程度各不相同。     

东北地区土地覆被格局变化模拟：基于CLUE-S和Markov-CA模型的对比分析

研究以遥感解译的东北地区2000年、2010年、2015年的土地覆被变化为基础,充分考虑自然和社会因素对土地覆被变化的影响,分别通过CLUE-S模型和Markov-CA模型对东北地区2015年和2030年土地覆被格局进行模拟,研究结果表明：通过与遥感解译的2015年实际土地覆被类型数据对比验证,CLUE-S模型和Markov-CA模型模拟结果的总体Kappa指数分别为0.970 0和0.964 9,结果表明2种模型的模拟结果较为理想,CLUE-S模型的模拟精度较Markov-CA模型更高。2015—2030年东北地区草地、耕地、湿地、其他用地和水体面积呈现下降趋势,林地、人工表面面积呈现增加趋势,人地关系越发紧张。东北地区作为生态环境相对脆弱的区域需警惕不可持续的土地覆被变化,需权衡生态保护、粮食增加与基础设施建设的用地需求和协调发展。     

干旱区绿洲LUCC马尔柯夫过程分析——以新疆于田绿洲为例

土地利用/覆盖变化(LUCC)在干旱区环境变化及生态监测中具有重要意义。本文选取塔里木盆地南缘典型绿洲于田县,利用TM遥感影像,基于Markov模型预测了于田绿洲未来的发展趋势,得出绿洲将向良性方向发展的结论。通过对Markov模型中不同步长收敛结果的讨论,可以看出间隔10 a1、2 a步长的马尔柯夫过程与间隔两年的马尔柯夫过程有着明显的不同,短时间序列(两年)具有很大的随机特性,说明马尔柯夫模型中时间尺度是一个重要环节。由本文研究可以看出,基于遥感的Markov模型可以分析绿洲的变化趋势,在干旱区绿洲LUCC及其环境动态监测中具有重要的实用意义。     

延河流域土地利用/覆被变化模型的尺度转换方法

采用延河流域和安塞县2000和2006年的土地利用/覆被遥感影像,结合实地调研获取的2000-2006年该区域生态建设尤其是退耕还林还草数据,分别在流域尺度和县域尺度上,采用主成分分析法对延河流域及位于延河流域上中游的安塞县土地利用/覆被变化及其驱动力进行了分析,建立了耕地面积变化的驱动力模型.研究表明,在这两个尺度上,影响耕地面积变化的关键因素都是总人口和农作物播种面积.以此为基础,基于不同尺度上共同驱动力因子之间的相互关系,建立了不同尺度上耕地变化模型的尺度转换方法.对于不同尺度的土地利用/覆被变化模型,无论其模型形式的复杂程度如何,只要不同尺度上的关键驱动因子相同或存在明确的定量关系,就可以基于关键驱动力因子的定量关系进行不同尺度上土地利用/覆被模型的尺度转换.研究成果为土地利用/土地覆被变化的尺度转换方法研究提供了新思路.     

山区县域土地利用/覆被变化多尺度多模型模拟对比——以云南省勐腊县为例

土地利用/覆被变化(LUCC)模拟及其尺度效应一直是土地变化科学的研究内容与难点。本文选择西南沿边山区县域——云南省勐腊县为试验区,利用LCM、FLUS、CA-Markov和CLUE-S探究该县域LUCC模拟的最适模型和栅格尺度。结合1994年、2004年两期Landsat TM影像分类生成的土地利用/覆被数据,以及自然与社会经济数据,在60、90、120、150、180、210和240 m七个栅格尺度下模拟该县域2014年土地利用/覆被空间格局,并以同期土地利用/覆被遥感分类数据为参考来评价模型模拟精度,筛选出最适模型和栅格尺度,进而模拟该县域2024年土地利用/覆被格局。研究表明:四种模型均能较好地模拟该县域土地利用/覆被变化(K_(standard)=0.67～0.84),其中以LCM模型模拟精度最高(K_(standard)=0.77～0.84);各模型对不同土地利用/覆被类型的适用性不同,LCM模型模拟林地、园地和耕地效果最好,而FLUS模型模拟建设用地更佳;四种模型模拟LUCC的最适栅格尺度均为120 m,其K_(standard)0.70。整合不同地类的最适模型和栅格尺度模拟结果有利于提高山区县域LUCC模拟精度。     

干旱区典型流域近30年土地利用/土地覆被变化的分形特征分析——以玛纳斯河流域为例

利用1976、1989、2005年三期遥感影像,运用GIS和分形理论研究干旱区典型流域——玛纳斯河流域近30年的土地利用/土地覆被变化的复杂性和稳定性。结果表明:玛纳斯河流域各时段的土地利用/土地覆被类型分布具有分形结构,土地利用/土地覆被类型形状复杂性和斑块的稳定性波动变化,1976、1989、2005年三时期的各种土地利用/土地覆被类型总平均分维值分别为1.394 0、1.363 4和1.389 9。总平均稳定性指数分别为0.606 0、0.636 6、0.610 1。三期土地利用/覆被类型平均稳定性排序为:沙地>未利用土地>居民点工矿用地>水域>林地>草地>耕地>盐碱地。耕地、林地、草地、盐碱地稳定性相对较差。通过对流域土地利用/土地覆被类型变化复杂性和稳定性的研究,可以为政府协调水土开发和调整土地利用结构提供支持。     

基于河链结构和LUCC的分布式流域水文模型及径流响应

为了定量评估土地利用/覆被变化的水文水资源效应,并考虑流域水文效应的空间差异,提出考虑土地利用/覆被变化的分布式流域水文模型。该模型基于Shreve河链概念对流域河网进行单元划分,基于中国科学院土地资源分类系统的一级类型,将流域划分为6种不同土地利用及覆被变化类型;在每一类土地利用及覆被变化面积上,分别考虑蒸发和下渗的差异,并利用蓄满-超渗耦合产流模型计算该面积上的地表径流量和地面以下径流量;利用地貌汇流模型和线性水库模型将每类面积上的地表径流量和地面以下径流量演算至流域出口断面,从而得到流域的水文过程。模型参数包括流域地貌特征参数、产流参数和汇流参数,前者可以根据地理信息系统技术和遥感技术获得,而后者则需要根据流域的水文资料通过最优化方法求得。利用大理河流域1981—1990年的逐日降雨、蒸发及流量资料对该模型进行了参数率定和检验,率定期1981—1987年的水量平衡系数为0.993,年径流深合格率为100.0%;检验期1988—1990年的水量平衡系数为1.230,径流深合格率为66.667%,计算结果表明该模型在大理河流域具有良好的适应性。并以2000年大理河流域的土地利用/覆被状况为基准,计算得到流域的基准径流深为30.36 mm,并对大理河流域未来土地利用情况进行情景分析,为充分发挥小流域(或单元)水土保持措施带来的各种效益提供参考依据。     

龙川江流域土地利用格局变化与趋势预测研究

以龙川江流域为研究对象,在遥感和GIS技术的支持下,应用1988年与1999年两期TM遥感影像解译制图,编制两期土地利用现状图。在ARC/INFO下,建立空间信息库,进行叠加分析,获取该流域土地利用类型动态转移数据矩阵,揭示该流域11年来土地利用空间分布规律及其变化特征。同时,应用Markov链模型对未来土地利用格局趋势进行预测分析,得出有林地、水域、水田、居民地在逐年增加,旱地、荒草地、灌木林逐年减少的结论,从而为调控该流域土地利用结构和方式,改善其生态环境状况提供依据。     

分布式流域水文模型水量过程模拟——以黄河河源区为例

文章分析了分布式水文模型、遥感和地理信息系统技术在流域径流模拟过程中的结合点,采用数字网格技术,以流域水量变化对地表土地覆被和气候变化响应过程为目的,讨论利用分布式水文模型研究下垫面和气候变化下流域水量响应过程的一般方法,并以黄河河源区为实例进行验证。     

流域土地覆被变化水文效应的模拟——以长江上游源头区梭磨河为例

为分析地表覆被变化的水文效应,以半分布式的地形指数模型 (TOPOMODEL) 为基础,对梭磨河流域1960~1999年逐日流量过程进行了模拟。结果表明,对于流域面积2536km² 的梭磨河流域,该模型也能取得较好的模拟结果。模拟了流域40年来气候波动和地表覆被变化对流域水文的影响。最后在4种不同的流域土地覆被情景下模拟了1960~1999年逐日径流过程。对于实际蒸散发与潜在蒸发有300多mm差距的梭磨河流域,在其它条件不变的情况下,随着流域土地覆被和冠层最大截流量的增加,冠层截流蒸发和流域总蒸发增加,植被蒸腾和土壤表面蒸发减少,土壤水分增加而流域水分含量和饱和层含水量减少。地表径流、地表以下径流、总径流减小。重现期小于20年的洪峰流量减小,但对40年一遇的洪峰流量影响很小,甚至有增加洪峰流量的作用。     

Spatio-temporal Rainfall Distribution and Markov Chain Analogue Year Stochastic Daily Rainfall Model in Ethiopia

This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model. Particularly, in this study, we used a Markov Chain Analogue Year (MCAY) model that is, Markov Chain with Analogue year (AY) component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull, Log normal, mixed exponential and Gamma distributions. The MCAY model best describes the occurrence process of daily rainfall, this is due to the AY component included in the MC to model the frequency of daily rainfall. Then, by combining the occurrence process model and amount process model, we developed Markov Chain Analogue Year Weibull model (MCAYWBM), Markov Chain Analogue Year Log normal model (MCAYLNM), Markov Chain Analogue Year mixed exponential model (MCAYMEM) and Markov Chain Analogue Year gamma model (MCAYGM). The performance of the models is assessed by taking daily rainfall data from 21 weather stations (ranging from 1 January 1984-31 December 2018). The data is obtained from Ethiopia National Meteorology Agency (ENMA). The result shows that MCAYWBM, MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM. The mean absolute percentage error (MAPE) in the four models: MCAYGM, MCAYWBM, MAYMEM and MCAYLNM are 2.16%, 2.27%, 2.25% and 11.41% respectively. Hence, MCAYGM, MCAYWBM, MAYMEM models have shown an excellent performance compared to MCAYLNM. In general, the light tailed distributions: Weibull, gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially, when these distributions are combined with MCAYM.     

准格尔丘陵区小流域土地覆盖变化的模型测算及空间位置转换

基于3个时相的土地覆盖类型图,在GIS的支持下,利用空间模型、数量模型和土地覆盖变化强度指数对内蒙古准格尔丘陵区五分地沟小流域土地覆盖变化进行数量测算,同时以耕地为例进行土地利用类型空间位置变化案例分析。模型测算结果表明：灌木林地、水域、居民点、草地是研究区域转出为其它土地覆盖类型的“敏感”类型；变化强度结果显示,1979-1987年内,草地、沟壑地是变化强度最大的土地覆盖类型；1987-2002年时段内,变化强度较小,反映出人类对于研究区干扰活动的减弱。耕地空间位置转换分析显示,耕地新增面积很大,并呈均匀、离散分布。     

城市土地利用演变信息的数据挖掘——以上海市为例

城市土地利用变化 ,具有非线性特征 ,一般的数据挖掘方法基本上失效。本文研究了利用马尔可夫链和神经网络两种方法 ,基于地理信息系统、遥感图像、电子地图 ,预测了上海市中心城区、 2 0 0 2年和 2 0 0 5年的土地利用总量和土地利用类型结构的变化 ,从而研究了城市土地利用状况演变预测的地学数据挖掘技术。     

Sensitivity of a stochastic land-cover change model to pixel versus polygonal land units

Land-use/cover change (LUCC) projections can be generated by a variety of land-cover change models (LCMs) and applied to a range of ecological and environmental studies. Most existing spatially explicit LCMs represent land use or cover using either pixel or polygon/patch spatial units. However, the effects of the choice to use pixel versus polygonal land units on the outputs from any given model have not yet been systematically assessed. We evaluated the impacts of alternative land units on the performance of a LCM. A stochastic LCM based on a geostatistical algorithm was developed and applied using both pixel and polygons, which were derived from parcel maps. Nine possible parcel-change scenarios were generated to evaluate the effects of geometric change in management boundaries. The approach was tested through the simulation of multiple land-cover transitions in Medina County, Ohio, between 1992 and 2011. Performance of the simulations was assessed using a metric for the accuracy of spatial allocations (figure of merit (FoM)) and several landscape pattern metrics describing the shapes and sizes of resulting land-cover patches. Results support the notion that there is a clear trade-off between pixel and polygon land units: using polygon boundaries is helpful in obtaining more realistic spatial patterns, but at the cost of location accuracy. Significant differences were found among different parcel-change scenarios on both location accuracy and spatial patterns, with the primary effects being dependent on the type of land-cover transition and the resolution at which validation was assessed.     

Incorporating spectral data into logistic regression model to classify land cover: a case study in Mt. Qomolangma (Everest) National Nature Preserve

Only few models for land-cover classification incorporated spectral data into ordinary logistic regression (OL model) in the Mt. Qomolangma (Everest) National Nature Preserve (QNNP) in China. In this study, spectral variables were incorporated into OL model and autologistic regression (AL) model to classify six main land covers. Twelve environmental variables and seven spectral variables of 10,000 stratified random sites in the QNNP were quantified and analyzed; OL model, AL model, OL model with spectral data (OLM model), and AL model with spectral data (ALM model) were estimated. The OLM and ALM models produced better estimates of regression coefficients and significantly improved model performance and overall accuracy for the grassland, sparsely vegetated land, and bare land compared with OL and AL models.     

Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity

We examined the impact of temporal dependence between patterns of error in classified time-series imagery through a simulation modeling approach. This research extended the land-cover-change simulation model we previously developed to investigate: (1) the assumption of temporal independence between patterns of error in classified time-series imagery; and (2) the interaction of patterns of change and patterns of error in a post-classification change analysis. In this research, the thematic complexity of the classified land-cover maps was increased by increasing the number of simulated land-cover classes. Simulating maps with increased categorical resolution permitted the incorporation of: (1) higher-order, more complex spatial and temporal interactions between land-cover classes; and (2) patterns of error that better reproduce the complex error interactions that often occur in time-series classified imagery. The overall modeling framework was divided into two primary components: (1) generation of a map representing true change; and (2) generation of a suite of change maps that had been perturbed by specific patterns of error. All component maps in the model were produced using simulated annealing, which enabled us to create a series of map realizations with user-defined spatial and temporal patterns. Comparing the true map of change to the error-perturbed maps of change using accuracy assessment statistics showed that increasing the temporal dependence between classification errors did not improve the accuracy of resulting maps of change when the categorical scale of the land-cover classified maps was increased. The increased structural complexity within the time series of maps effectively inhibited the impact of temporal dependence. However, results demonstrated that there are interactions between patterns of error and patterns of change in a post-classification change analysis. These interactions played a major role in determining the accuracy associated with the maps of change.     

An approach to spatially explicit reconstruction of historical forest in Northeast China

The spatially explicit reconstruction of historical land-cover datasets plays an important role in studying the climatic and ecological effects of land-use and land-cover change（LUCC）. Using potential natural vegetation（PNV） and satellite-based land use data, we determined the possible maximum distribution extent of forest cover in the absence of human disturbance. Subsequently, topography and climate factors were selected to assess the suitability of land for cultivation. Finally, a historical forest area allocation model was devised on the basis of the suitability of land for cultivation. As a case study, we used the historical forest area allocation model to reconstruct forest cover for 1780 and 1940 in Northeast China with a 10-km resolution. To validate the model, we compared satellite-based forest cover data with our reconstruction for 2000. A one-sample t-test of absolute bias showed that the two-tailed significance was 0.12, larger than the significant level 0.05, suggesting that the model has strong ability to capture the spatial distribution of forests. In addition, we calculated the relative difference of our reconstruction at the county scale for 1780 in Northeast China. The number of counties whose relative difference ranged from-30% to 30% is 99, accounting for 74.44% of all counties. These findings demonstrated that the provincial forest area could be transformed into forest cover maps well using the model.     

基于地学信息图谱的土地利用动态变化研究——以山东省龙口市为例

阐述了地学信息图谱的内涵及其概念模式.选择山东省龙口市为例,在遥感和地理信息系统技术支持下,利用2002年数字遥感图像和1996年土地利用图及其自然和社会经济统计数据,建立了龙口市土地利用动态变化信息图谱,并根据图谱从空间和时间两方面分析龙口市土地利用动态演变过程,最后结合马尔科夫模型对其未来的演变趋势进行动态预测.研究表明:自1996年以来,龙口市土地利用发生了大幅度变化,耕地面积减少5 383.48 hm2,园地和建设用地面积各增加33 729.62 hm2和2319.91 hm2;同时土地利用结构发生明显变化,主要表现在耕地和园地之间的转化,耕地和园地向建设用地的转化.预测表明:在未来的几十年内,耕地面积仍将继续减少,建设用地面积还会逐年增加,园地仍为该市的主要用地类型.