基于3S技术的土地利用变更调查出现的问题及对策

随着我国经济的快速发展,城市化进程的逐步推进,和经济建设有着直接联系的土地资源开发利用更加频繁。而我国人均土地资源不足,如何合理地利用土地资源成为一件关系子孙后代的大事。基于此,我国每年的土地利用变更调查成为一项事关国计民生的大事,对土地资源的合理利用、科学规划,土地政策的制订起着指导性意见。本文在总结现实中可能存在的问题基础上,结合了具体实践,提出了一些切实可行的解决方案。     

论县级土地利用总体规划中规划弹性的实现

县级土地利用总体规划在中国五级土地利用总体规划的地位、县域经济发展中的复杂性和偶然性以及县级土地管理的要求,决 定必须强调县级土地利用总体规划中规划弹性。结合现存土地利用总体规划刚性太强、弹性不足的现状,采用文献综述法和归纳演 绎法,具体分析县级土地利用总体规划中规划弹性的实现问题,提出实现县级土地利用总体规划中规划弹性的总体思路、弹性指标设 置的基本原则以及规划目标、规划指标、规划用途和规划布局的弹性四个关键点。     

县（市）级土地利用规划管理信息系统建设研究

本文以福州市仓山区为研究区域，通过对该区域土地利用规划信息管理系统的研究．探索县级土地利用规划数据库建设、规划管理信息系统软件开发的方法，研究具有推广价值的系统应用软件，完善系统建设规范和标准。     

青岛市土地规划管理局业务跟踪监控系统——走好城市规划第一步

青岛市土地规划管理局是青岛市政府的职能机构,负责全市土地和城市规划的统一管理工作,具体实施包括建设项目选址意见书、建设用地规划许可证、建设用地批准书、建设工程规划许可证、土地使用证的受理、审批、签发业务,业务非常繁杂。为此,该局与上海集高电子科技有限公司合作开发的业务审批监控系统,在国内率先实现了规划管理、土地管理业务审批流程自动化和业务处理信息化,为国内的规划管理和土地管理工作开创了一种新的管理模式。该系统于1998年荣获了山东省土地管理局科技进步二等奖。     

县域土地利用规划环境影响评价

土地利用规划对社会经济的发展和生态环境都会产生深刻的影响,因此尽早地开展环境影响评价对于保证决策的正确性是十分必要的。县域土地利用规划环境影响评价是对县级土地利用规划实施后可能造成的环境影响进行分析、预测和评价,提出预防或缓解措施。本文以四川郫县为例,制定了县域土地利用规划环境影响评价的基本程序,并对郫县的土地利用开展了环境影响评价工作,还对其生态用地进行了分区。     

利用遥感技术进行中山市土地覆盖变化监测的初步研究

利用遥感技术进行中山市土地覆盖变化监测的初步研究尚东（中国测绘科学研究院）中山市是广东省重点发展城市。城市经济建设发展很快。城市建设的蓬勃发展，必然对环境及土地利用产生影响，因此，了解土地利用的现状、城市化发展速度、搞好规划布局，成为当务之急。实现这...     

GIS支持下土地管理办公自动化及若干功能的实现

一、引言现代土地管理不仅应满足地籍管理、土地利用规划管理、建设用地管理、土地评估、土地档案资料管理和统计分析等业务的需要,还需要在多部门、跨地区地运作。为了提高土地管     

土地开发整理管理信息系统方案设计

土地开发整理规划及管理是土地开发整理项目的立项依据和实施保障，其过程涉及大量空间数据的处理，利用GIS技术建立相应的信息系统，对土地开发整理工作的现代化建设有非常重要的意义。     

基于GIS的海涂围垦区土地资源多目标适宜性评价研究

海涂土地资源适宜性评价是合理开发海涂的基础.选择浙江沿海典犁海涂围垦区,基于GIs建立空间和属性数据库,采用栅格运算与评价模型相结合的方法,对研究区的土地资源进行多目标适宜性评价研究;根据优先用地规划方案,应用评价结果,初步实现了研究区土地利用结构的空间配置.结果表明:研究区的土地资源具有多宜性特点,不同利用类型的适宜等级间存在"重叠"现象;基于土地多目标适宜性评价结果进行土地利用结构的空间配置时,不同的用地规划方案会产生不同的配置结果.     

基于Rough集理论的营区规划综合评价

随着社会经济的发展,军用土地资源的重要性越来越为人们所重视,土地资源的规划直接影响土地资源的管理,因此,对军用土地资源规划管理的研究受到了广泛的重视,是目前研究的重点之一。军队营区规划涉及方方面面,文章首先建立营区规划评价指标体系,并运用Rough集理论对营区性能进行综合评价,为营区规划评价提出了新的方法。     

Annual Total Gross Primary Production Estimation based on Vegetation Indices Seasonal Variation Curve

For the estimation of annual Gross Primary Productivity(GPP),it is proposed an estimation method with simple parameters and small errors.By taking each type of vegetation in the area of Three-North Shelterbelt Program(TNSP) as the research subject,the MODIS vegetation indices were obtained,and the seasonal variation curve of vegetation indices were built.Then,the fitting relation between the integral of time series vegetation indices(ΣVIs) and GPP products of MODIS was established,so as to realize a simple GPP estimation method and study the applicable ΣVIs for estimating the GPP of all vegetation types.The results show that：(1) ΣVIs is suitable for estimating the annual total GPP in research area and significantly correlated with MODIS GPP at the confidence level of p<0.01;(2) ΣEVI2 is applicable to estimate the GPP of evergreen needleleaf forest,decidious needleleaf forest,decidious broadleaf forest,mixed forest,woody savannas,savannas,permanent wetlands,croplands,croplands/natural vegetation mosaic,while the effect of ΣNDVI for estimating the GPP of closed shrublands,open shrublands,grasslands,croplands,and barren or sparsely vegetated is superior to ΣEVI andΣEVI2;(3) Since the NDVI itself is saturated in the area of high Leaf Area Index(LAI),the error of estimating the GPP of high LAI vegetation type by ΣNDVI is larger,while using ΣEVI and ΣEVI2 to estimate them has better accuracy,and the limitation from blue band of EVI2 reduces compared with EVI,which can be applied to the GPP research of long time series better.     

The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product

The results of the first consecutive 12 months of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) global burned area product are presented. Total annual and monthly area burned statistics and missing data statistics are reported at global and continental scale and with respect to different land cover classes. Globally the total area burned labeled by the MODIS burned area product is 3.66 × 10⁶ km² for July 2001 to June 2002 while the MODIS active fire product detected for the same period a total of 2.78 × 10⁶ km², i.e., 24% less than the area labeled by the burned area product. A spatio-temporal correlation analysis of the two MODIS fire products stratified globally for pre-fire leaf area index (LAI) and percent tree cover ranges indicate that for low percent tree cover and LAI, the MODIS burned area product defines a greater proportion of the landscape as burned than the active fire product; and with increasing tree cover (> 60%) and LAI (> 5) the MODIS active fire product defines a relatively greater proportion. This pattern is generally observed in product comparisons stratified with respect to land cover. Globally, the burned area product reports a smaller amount of area burned than the active fire product in croplands and evergreen forest and deciduous needleleaf forest classes, comparable areas for mixed and deciduous broadleaf forest classes, and a greater amount of area burned for the non-forest classes. The reasons for these product differences are discussed in terms of environmental spatio-temporal fire characteristics and remote sensing factors, and highlight the planning needs for MODIS burned area product validation.     

Mapping the invasive species,Chinese tallow,with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress‐tupelo forest, senescing Chinese tallow with red leaves (‘red tallow’), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress‐tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non‐active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs.     

Persistent changes in NDVI between 1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and Mapping Studies) data

The preliminary results of Normalized Difference Vegetation Index (NDVI) change studies over India using data from Advanced Very High Resolution Radiometer Global Inventory Modeling and Mapping Studies (AVHRR GIMMS) between 1982 and 2003 are presented. The three methodologies of univariate differencing, temporal profiling and anomaly analysis were undertaken. Univariate differencing was used to determine overall NDVI change between 1982 and 2003. A persistence filter was used to filter out ephemeral changes. The temporal profile analyses were carried out over different meteorological subdivisions to compare changes in NDVI with rainfall patterns. In the anomaly analysis, the areas of change were analysed over different land cover categories derived from IRS‐WiFS data. The preliminary results indicate that positive trends in vegetation change occurred over most parts of the country and these changes appear not to be highly correlated with rainfall data, indicating that land cover transformations may be the major driving force behind the changes. The land cover classifications experiencing the greatest increasing NDVI were tropical thorn forests and intensive agriculture and the land cover experiencing very slow growth included current jhum, tropical moist deciduous and temperate evergreen forest. Five‐year moving averages indicate a general increase in NDVI from 1986 to 1998 and then declining thereafter. This is a concern in most of the meteorological subdivisions.     

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis

This study compared the suitability of LIDAR (LIght Detection And Ranging) data, three-band multispectral data, and LIDAR data integrated with multispectral information, for classifying spatially complex vegetation in the Aspen Parkland of western Canada. Classifications were performed for both a) general vegetation classes limited to three major formations of deciduous forest, shrubland and grassland, and b) eight detailed vegetation classes including upland mixed prairie and fescue grasslands, closed and semi-open aspen forests, western snowberry and silverberry shrublands, and fresh and saline riparian (lowland) meadows. A Digital Elevation Model (DEM) and Surface Elevation Model (SEM) developed from LIDAR data incorporated both topographic and biological biases in community positioning across the landscape. Using multispectral data, the original digital image mosaic, its hybrid color composite, and an intensity-hue-saturation (IHS) image were each tested. Final vegetation classification was done through integration of information from both digital images and LIDAR data to evaluate the improvement in classification accuracy. Among the land cover schedules with three and eight classes of vegetation, classification from the multispectral imagery, specifically the hybrid color composite image, had the highest accuracy, peaking at 74.6% and 59.4%, respectively. In contrast, the LIDAR classification schedules led to an average classification accuracy of 64.8% and 52.3%, respectively, for the general and detailed vegetation data. Subsequent integration of the LIDAR and digital image classification schedules resulted in accuracy improvements of 16 to 20%, resulting in a superior final accuracy of 91% and 80.3%, respectively, for the three and eight classes of vegetation. A final land cover map including 8 classes of vegetation, fresh and saline water, as well as bare ground, was created for the study area with an overall accuracy of 83.9%, highlighting the benefit of integrating LIDAR and multispectral imagery for enhanced vegetation classification in heterogenous rangeland environments.     

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO₂ flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.     

Assessment of the MODIS LAI product for Australian ecosystems

The leaf area index (LAI) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) is important for monitoring and modelling global change and terrestrial dynamics at many scales. The algorithm relies on spectral reflectances and a six biome land cover classification. Evaluation of the specific behaviour and performance of the product for regions of the globe such as Australia are needed to assist with product refinement and validation. We made an assessment of Collection 4 of the MODIS LAI product using four approaches: (a) assessment against a continental scale Structural Classification of Australian Vegetation (SCAV); (b) assessment against a continental scale land use classification (LUC); (c) assessment against historical field-based measurement of LAI collected prior to the Terra Mission; and (d) direct comparison of MODIS LAI with coincident field measurements of LAI, mostly from hemispherical photography. The MODIS LAI product produced a wide variety of geographically and structurally specific temporal response profiles between different classes and even for sub-groups within classes of the SCAV. Historical and concurrent field measurements indicated that MODIS LAI was giving reasonable estimates for LAI for most cover types and land use types, but that major overestimation of LAI occurs in some eastern Australian open forests and woodlands. The six biome structural land cover classification showed some significant deviations in class allocation compared to the SCAV particularly where grasslands are allocated to shrubland, savanna woodlands are allocated to shrubland, savanna and broadleaf forest, and open forests are allocated to savanna and broadleaf forest. The land cover and LAI products could benefit from some additional examination of Australian data addressing the structural representation of Eucalypt canopies in the “space of canopy realisation” for savanna and broadleaf forest classes.