The geography of large-scale land acquisitions: Analysing socio-ecological patterns of target contexts in the global South

This paper analyses local geographical contexts targeted by transnational large-scale land acquisitions (>200 ha per deal) in order to understand how emerging patterns of socio-ecological characteristics can be related to processes of large-scale foreign investment in land. Using a sample of 139 land deals georeferenced with high spatial accuracy, we first analyse their target contexts in terms of land cover, population density, accessibility, and indicators for agricultural potential. Three distinct patterns emerge from the analysis: densely populated and easily accessible croplands (35% of land deals); remote forestlands with lower population densities (34% of land deals); and moderately populated and moderately accessible shrub- or grasslands (26% of land deals). These patterns are consistent with processes described in the relevant case study literature, and they each involve distinct types of stakeholders and associated competition over land. We then repeat the often-cited analysis that postulates a link between land investments and target countries with abundant so-called “idle” or “marginal” lands as measured by yield gap and available suitable but uncultivated land; our methods differ from the earlier approach, however, in that we examine local context (10-km radius) rather than countries as a whole. The results show that earlier findings are disputable in terms of concepts, methods, and contents. Further, we reflect on methodologies for exploring linkages between socio-ecological patterns and land investment processes. Improving and enhancing large datasets of georeferenced land deals is an important next step; at the same time, careful choice of the spatial scale of analysis is crucial for ensuring compatibility between the spatial accuracy of land deal locations and the resolution of available geospatial data layers. Finally, we argue that new approaches and methods must be developed to empirically link socio-ecological patterns in target contexts to key determinants of land investment processes. This would help to improve the validity and the reach of our findings as an input for evidence-informed policy debates.     

中国历史时期土地覆被数据集地理空间重建进展评述

重建长时间序列具有空间属性的土地覆被数据集,对研究历史时期土地利用/土地覆被变化及其气候和生态效应具有重要意义。近年来,国内外学者就定量重建中国区域历史土地覆被数据集进行了积极探索。但由于历史时期土地利用数据来源多元、重建方法多样、验证方式各异等原因,不同学者的重建结果迥异,其中重建方法是导致差异形成的重要原因之一。本文从重建思路、假设和方法、结果验证等方面对覆盖中国区域的主要空间数据集进行了综合评述,结果表明：①基于历史记录的还原法和基于地理空间模型的重建法是历史土地覆被空间重建的主要方法,而根据建模过程,后者又可进一步分为“自上而下”的配置模型和“自下而上”的演化模型法。②基于数量重建进行空间重建是当前历史土地覆被数据集重建的主流,在缺少充分、客观历史数据的条件下,对基础数据、分布控制因素和限制因子进行合理假设是取得合理结果的重要条件。③为提高研究成果的解释力,需要对重建结果进行检验,直接验证法虽较为准确,但受时空尺度限制,具有显著的局限性,间接验证法可作为有效的补充。     

An algebraic approach to automated geospatial information fusion

This paper presents a new technique for information fusion. Unlike most previous work on information fusion, this paper explores the use of instance‐level (extensional) information within the fusion process. This paper proposes an algorithm that can be used automatically to infer the schema‐level structure necessary for information fusion from instance‐level information. The approach is illustrated using the example of geospatial land‐cover data. The method is then extended to operate under uncertainty, such as in cases where the data are inaccurate or imprecise. The paper describes the implementation of the fusion method within a software prototype. Finally, the paper discusses several key topics for future research, including applications of this work to spatial‐data mining and the semantic web.     

Spatio-Temporal Analysis of Land Use/Land Cover Changes in an Ecologically Fragile Area—Alappuzha District,Southern Kerala,India

Concomitant with careless human interference in the delicate environmental balance, the Earth’s surface is witnessing a variety of changes in land use and land cover (LULC). Acquisition of a sound understanding of LULC is an important aspect of maintaining a sustainable, benign, healthy environment. The present work highlights a spatiotemporal study on the LULC features of Alappuzha District, an ecologically fragile area in southern Kerala, a state in South India. The study area faces diverse environmental challenges including decline of landforms, rising sea levels, population expansion and anthropogenic encroachments on the ecological balance. This investigation compiles an audited account of the modifications, in each class of LULC, using geospatial technologies. We interpreted satellite imagery from the Landsat 8 and the Landsat multispectral scanner for the years 1973 and 2017. The LULC aspects were categorized into seven classes: waterbody, waterlogged area, mixed vegetation, built-up land, uncultivated area, paddy field and sandy area. Our findings affirm that the expansiveness of the built-up land area is directly proportional to the growth of the population. Advanced technologies such as remote sensing and geographic information system accentuate alterations in land use patterns over time and the extent to which the changes affect the human population and the natural habitat. We verified the results of our research by assessment of accuracy and ground truth confirmation of the LULC features.

     

基于CR-BDI模型的农户作物种植行为模拟——以陕西省米脂县姜兴庄为例

运用模拟平台模拟农户土地利用行为,对揭示农户土地利用变化机制和调整土地利用决策具有重要意义。本文选择生态脆弱区一乡一业、一村一品典型村陕西省米脂县姜兴庄为例,以信念—愿望—意图模型(BDI)为基础,构建有限理性能力与资源(CR-BDI)模型。基于NetLogo平台对CR-BDI模型和传统BDI模型的模拟情况进行对比分析,结果表明：①CR-BDI模型更适于微观尺度农户土地利用决策研究。2014年该模型面积失误率为6.78%,比传统BDI模型低8.48%,空间准确率比传统BDI模型高10.10%,2015年CR-BDI模型和传统BDI模型的整体空间正确率分别为78.8%和69.2%;②CR指数有利于体现农户的有限理性,与实际种植决策较为符合;③利用NetLogo平台有利于直观再现农户土地利用行为,揭示土地利用变化的微观机理,可作为研究土地利用变化互动机理的良好平台。     

基于遥感时序特征的地表覆被信息提取

地表覆被作为自然过程和人类活动共同作用的重要地表景观特征,对全球或局地气候、水热循环、物质传输及陆面生态系统多样性等影响深远.利用年内时序遥感影像自动提取不同地表覆被类型的方法,以新疆阜康地区为研究目标,组织2016年植被全生长季的Landsat 8 OLI地表反射时序影像,研究不同物候期植被冠层的纹理响应信息,考察研...     

澜沧江流域云南段土地覆盖状况及其模式分类

基于澜沧江流域运动段土地覆盖状况的基础数据库,结合现有ＧＩＳ图形空间数据,通过集成处理,就澜沧江流域综合开发中,有关云南段土地覆盖状况及其模式分类展开研究。初步研究结果为：１、研究区土地覆盖状况随流域沿线空间延伸而变化,不同覆盖类型变化态势各具特点,但多与当地自然、社会、经济整体发展水平密切联系;２．基于土地覆盖中主要土地覆盖类型比例值的累计,设定适当阈值,将流域土地覆盖划分为５种类型即林地覆盖型     

全球尺度多源土地覆被数据融合与评价研究

精确的全球及区域尺度土地覆被遥感分类数据是全球变化、陆地表层过程模拟、生态文明建设及区域可持续发展等研究的重要基础数据。本文以5套全球土地覆被数据集GLCC、UMD、GLC2000、MODIS LC、GlobCover为研究对象,结合MODIS VCF、MODIS Cropland Probability以及AVHRR CFTC数据集,设计一种基于模糊逻辑思想的证据融合方法实现上述多源土地覆被信息的决策融合,生成一套依据植物功能型分类的全球1 km土地覆被融合数据SYNLCover。结果显示,与5套源土地覆被数据集相比：① 在总体一致性精度上,SYNLCover的8个生物形态类型和12个目标类型的平均总体一致性精度最高,分别约为65.6%和59.4%,其次依次是MODIS LC、GLC2000、GLCC和GlobCover,UMD的最低,分别约为48.9%和42.6%,而且SYNLCover与5套源土地覆被数据集两两相比的总体一致性都是最好的;② 在类型一致性精度上,除灌丛类型外,SYNLCover中包括森林、草地、耕地、湿地、水体、城镇建筑和其他7种生物形态类型,以及森林类型的5种叶属性的平均一致性精度也是最高的,如其他类型的平均一致性精度可达67.73%;③ 除灌丛和湿地类型外,SYNLCover的其余6种生物形态类型的平均一致性精度均比其在5套源数据中相应的一致性精度的最大值提高了10%~15%左右;森林类型的5种叶属性的一致性精度也提高了约10%。SYNLCover分类精度的提高反映了本研究设计的多源数据融合方法的可行性和有效性。     

广西柳江县荒山荒地资源开发利用研究

通过对广西柳江县荒山荒地资源的特点及开发条件分析、土地适宜性评价，在 此基础上提出荒山荒地资源开发利用原则和对策．     

基于MODIS数据的北京西北部地区土地覆盖分类研究

本文主要基于MODIS 16天合成的NDVI时间序列数据、8天合成 LST数据、1∶5万DEM数据以及其他辅助数据相结合,进行北京西北部地区土地覆盖分类的研究。首先选取适合于MODIS数据分类的土地覆盖分类系统,然后用PCA方法对NDVI时间序列数据进行信息增强与压缩处理,以排除各种干扰因素,提高分类精度。最后结合LST数据、DEM数据及降雨温度数据,利用?齂-均值非监督分类法,进行研究区的土地覆盖分类,经过分类后处理,得到北京西北部地区的土地覆盖分类图。分类结果表明,使用250m分辨率MODIS数据,结合本文所用方法,能够实现较大区域的土地覆盖分类,并且能达到较高的分类精度。     

Using geospatial technology to strengthen data systems in developing countries: The case of agricultural statistics in India

Despite significant progress in the development of quantitative geography techniques and methods and a general recognition of the need to improve the quality of geographic data, few studies have exploited the potential of geospatial tools to augment the quality of available data methods in developing countries. This paper uses data from an extensive deployment of geospatial technology in India to compare crop areas estimated using geospatial technology to crop areas estimated by conventional methods and assess the differences between the methods. The results presented here show that crop area estimates based on geospatial technology generally exceed the estimates obtained using conventional methods. This suggests that conventional methods are unable to respond quickly to changes in cropping patterns and therefore do not accurately record the area under high-value cash crops. This finding has wider implications for commercializing agriculture and the delivery of farm credit and insurance services in developing countries. Significant data errors found in the conventional methods could affect critical policy interventions such as planning for food security. Some research and policy implications are discussed.     

基于蜂群算法的城市土地利用变化建模

通过引入人工蜂群算法用于构建土地利用变化的驱动力模型,分析土地利用变化的驱动力机制。算法原理通过模仿蜜蜂采蜜行为,自动搜索和提取土地利用变化样本中不同土地变化类型所对应的驱动力分类规则。分类规则的构建采用“IF…THEN”形式,并选取3种不同的适应度函数分别进行模拟验证。研究案例基于UCI实验数据集和美国纽卡斯尔市真实土地利用变化数据集。由实验结果可知,采用蜂群算法模型的总体精度和Kappa系数评价优于其它算法,表明蜂群算法应用于土地利用变化建模具有可行性。     

首套全球30 m分辨率土地覆被产品区域尺度精度评价——以河南省为例

以河南省为研究区,对全球首套30 m分辨率土地覆盖产品GlobleLand30进行区域尺度精度评价。首先,以中国1\begin{array}{c}:\end{array}10万土地利用数据（CHINA-2010）为参考,分析两种产品的空间一致性;而后,通过Google Earth样本分析GlobleLand30在空间不一致区域的制图精度;最后,利用野外实地考察样本对GlobleLand30进行总体精度评价,并从土地覆被复杂度、高程等方面分析影响精度的原因,结果表明：① GlobleLand30与CHINA-2010空间一致性达80.20%。两种产品对耕地、林地、人工地面一致性高,对草地、水体、灌木、湿地、未利用土地的一致性低。② 在空间不一致区域,GlobleLand30的总体分类正确率略低于CHINA-2010,但两者对不同地类的优势不同。③ 经野外实地考察验证可知,GlobleLand30的总体精度达83.33%。④GlobleLand30与CHINA-2010的空间一致性随土地覆被复杂度的增加而降低,并在高程过渡带较低。     

利用TM数据提取干旱区土地覆被信息的方法比较

以柴达木盆地香日德绿洲作为研究实验区,对该区域ETM遥感数据经过空间分辨率融合、主成分分析等方法进行空间信息增强及专题信息增强处理,组合最佳视觉背景图像,分别在不同背景图像上选择训练样本,利用最大似然法监督分类方法(MLC)、多空间尺度分层聚类(SSHC)和基于知识的模糊聚类方法(KFC)等分类器,分别用各自训练样本初始化各类别信息特征值,形成类别特征值模式库,分别以此为基础对待分样本进行分类,对初分类的结果经过类别合并、碎斑滤除以及重新编码赋色等分类后处理,得到最终分类结果及分类精度评价结果。从所获数据可以得出如下结论：从总体精度和Kappa值可知,SSHC和．KFC分类方法所获结果精度较高,总体精度比MLC分类结果约高于3％,SSHC之结果精度略高于KFC之结果;SSHC、KFC和MLC三种分类方法对该区域地表覆被信息的提取分类中,SSHC分类方法对耕地、石砾地、河滩和荒漠分类结果较好,KFC分类方法对耕地、沙地、河滩和荒漠分类结果较好,MLC分类方法对耕地、河滩和荒漠分类结果较好,三种分类方法对耕地、河滩和荒漠等三种地类的分类精度较高,用户精度都在80％以上,而对沙地和石砾地的分类结果其用户精度大都低于80％。     

地学数据特征分析

地理信息系统的广泛应用使地学数据的使用大为扩展,但关于地学数据的一些基本认识,如地学数据概念,地学数据的基本特征等并没有达成共识,在分析地学数据概念和来源的基础上,对地学数据的基本特征,如多尺度特征,分布式特征,空间拓扑特征等进行了详细的说明。     

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.     

Hydrologic variation with land use across the contiguous United States: Geomorphic and ecological consequences for stream ecosystems

Using daily discharge data from the USGS, we analyzed how hydrologic regimes vary with land use in four large hydrologic regions that span a gradient of natural land cover and precipitation across the continental United States. In each region we identified small streams (contributing area < 282 km²) that have continuous daily streamflow data. Using a national database, we characterized the composition of land cover of the watersheds in terms of aggregate measures of agriculture, urbanization, and least disturbed (“natural”). We calculated hydrologic alteration using 10 ecologically-relevant hydrologic metrics that describe magnitude, frequency, and duration of flow for 158 watersheds within the Southeast (SE), Central (CE), Pacific Northwest (NW), and Southwest (SW) hydrologic regions of the United States. Within each watershed, we calculated percent cover for agriculture, urbanized land, and least disturbed land to elucidate how components of the natural flow regime inherent to a hydrologic region is modified by different types and proportions of land cover. We also evaluated how dams in these regions altered the hydrologic regimes of the 43 streams that have pre- and post-dam daily streamflow data. In an analysis of flow alteration along gradients of increasing proportion of the three land cover types, we found many regional differences in hydrologic responses. In response to increasing urban land cover, peak flows increased (SE and CE), minimum flows increased (CE) or decreased (NW), duration of near-bankfull flows declined (SE, NW) and flow variability increased (SE, CE, and NW). Responses to increasing agricultural land cover were less pronounced, as minimum flows decreased (CE), near-bankfull flow durations increased (SE and SW), and flow variability declined (CE). In a second analysis, for three of the regions, we compared the difference between least disturbed watersheds and those having either > 15% urban and > 25% agricultural land cover. Relative to natural land cover in each region, urbanization either increased (SE and NW) or decreased (SW) peak flows, decreased minimum flows (SE, NW, and SW), decreased durations of near-bankfull flows (SE, NW, and SW), and increased flow variability (SE, NW, and SW). Agriculture had similar effects except in the SE, where near-bankfull flow durations increased. Overall, urbanization appeared to induce greater hydrologic responses than similar proportions of agricultural land cover in watersheds. Finally, the effects of dams on hydrologic variation were largely consistent across regions, with a decrease in peak flows, an increase in minimum flows, an increase in near-bankfull flow durations, and a decrease in flow variability. We use this analysis to evaluate the relative degree to which land use has altered flow regimes across regions in the US with naturally varying climate and natural land cover, and we discuss the geomorphic and ecological implications of such flow modification. We end with a consideration of what elements will ultimately be required to conduct a more comprehensive national assessment of the hydrologic responses of streams to land cover types and dams. These include improved tools for modeling hydrologic metrics in ungauged watersheds, incorporation of high-resolution geospatial data to map geomorphic and hydrologic drivers of stream response to different types of land cover, and analysis of scale dependence in the distribution of land-use impacts, including mixed land uses. Finally, ecological and geomorphic responses to human alteration of land cover will have to be calibrated to the regional hydroclimatological, geologic, and historical context in which the streams occur, in order to determine the degree to which stream responses are region-specific versus geographically independent and broadly transferable.     

A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology

ABSTRACT

Big data have shifted spatial optimization from a purely computational-intensive problem to a data-intensive challenge. This is especially the case for spatiotemporal (ST) land use/land cover change (LUCC) research. In addition to greater variety, for example, from sensing platforms, big data offer datasets at higher spatial and temporal resolutions; these new offerings require new methods to optimize data handling and analysis. We propose a LUCC-based geospatial cyberinfrastructure (GCI) that optimizes big data handling and analysis, in this case with raster data. The GCI provides three levels of optimization. First, we employ spatial optimization with graph-based image segmentation. Second, we propose ST Atom Model to temporally optimize the image segments for LUCC. At last, the first two domain ST optimizations are supported by the computational optimization for big data analysis. The evaluation is conducted using DMTI (DMTI Spatial Inc.) Satellite StreetView imagery datasets acquired for the Greater Montreal area, Canada in 2006, 2009, and 2012 (534 GB, 60 cm spatial resolution, RGB image). Our LUCC-based GCI builds an optimization bridge among LUCC, ST modelling, and big data.     

Comprehensive Evaluation of the Suitability of Agricultural Land in Myanmar

Myanmar is a country with an economy based on agriculture. It has rich agricultural resources and great potential for development. The development of agriculture in Myanmar is becoming increasingly important to international food security. Assessments of agricultural land resources in Myanmar are the basis for the country’s agricultural development and for food security evaluations. In this paper we used the MaxEnt model to analyze the relationship between the suitability of land for agricultural reclamation and the main environmental variables in Myanmar, and then constructed a model to comprehensively evaluate the suitability of land for agriculture in Myanmar. The results show that: 1) the overall accuracy of the MaxEnt model is high (AUC>0.8), which means there is a high correlation between the database of selected environmental indicators and the true distribution of cultivated land in Myanmar. 2) Soil depth is the most important factor affecting the suitability of land for agriculture in Myanmar. When the thickness of soil layer is less than 100 cm, the suitability of land for agriculture is low. With respect to topographic conditions, slope is the main factor affecting suitability. When the slope is greater than 20 degrees, the suitability of land for agriculture is low. With respect to climate conditions, precipitation is the main influencing factor. There is a positive correlation between river network density and land suitability. 3) Currently, 400 000 km² of the land resources in Myanmar are suitable for agriculture, and of this amount 290 000 km² are highly suitable, accounting for nearly 40% of the country's land area. The highly suitable land is distributed mainly in Magway, Sagaing, Ayeyarwady and Yangon provinces. The provinces are also important grain production areas in Myanmar, and this serves to validate the effectiveness of the method used in this paper.     

不同生计类型农户的土地利用——三峡库区典型村的实证研究

采用分层随机抽样、参与式农村评估、地块调查等方法,研究了重庆市涪陵区珍溪镇3 个典型村样本农户的土地利用和土地投入的差异。共调查了227 户,2250 个地块。结果发现：① 除了6.17%的非农户土地撂荒、退耕或出租外,纯农户、一兼户、二兼户的土地利用类型均以集约为主。纯农户因年龄结构老龄化,劳动力供应不足,相对粗放的耕地面积为48.95%。一兼户相对集约利用的面积比例为71.08%。二兼户相对集约利用的面积比例为67.20%。② 土地投入有明显差异。纯农户每公顷农业劳动力投入少,劳动力老龄化;由于缺乏资金,农业机械及省工性投入少,多选择农家肥及磷肥、碳铵等价格较低的传统肥料。一兼户劳动力投入的数量与质量都最高,有长期投资农业的意愿与动力,农业机械投资量最大,保持了较高的农家肥施用量,且各种化肥配合施用,用量适中;二兼户每公顷农业劳动力投入大,以老人与妇女为主;由于收入高,农业机械及省工性投入比纯农户多,首选价格高、省劳力的化肥。非农户无土地投入。`