Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon,central Vietnam

This study integrates the use of multi-source and multi-resolution remote sensing, topographic and field-based datasets to quantify land-use and land-cover (LULC) changes along a coastal stretch of Thua Thien Hue Province (central Vietnam). The LULC change analysis involves the Tam Giang-Cau Hai lagoon, the largest lagoon system in Southeast Asia, which is running nearly 70 km along the coast and having about 22,000 ha of water surface.The LULC change analysis was performed by computer-aided visual interpretation for 5 years (1965, 1989, 2000, 2006 and 2014) using satellite imagery from LANDSAT MSS, TM, ETM+ and 8, ASTER and SPOT5. National topographic maps were also used for the 1965 and 2000 years.To adequately represent the LULC features and peculiarities of central Vietnam coastal areas, an adapted CORINE Land Cover nomenclature was used where new 3rd and 4th level classes were adopted. Due to their intrinsic relative high spatial and radiometric resolution, SPOT5 images from 2006 were assumed as a reference for interpretation keys and first delineation. Changes were mapped by editing those vectors representing features which underwent LULC change prior or after 2006. Spatial and temporal changes were analyzed by post-classification approach and validated by ground truth information. High detail object-based classification was finally performed to infer the capability of medium spatial resolution imagery for extracting cadastral scale pond maps. The accuracy of classification was checked by a polygon by polygon comparison with an existing aquaculture facility inventory.Five LULC maps were obtained by applying a legend of 21 classes including two newly defined: “Aquaculture ponds” and “Mangrove forest”. The overall classification accuracy of the LULC map is 85% while the KHAT statistics 0.81 for the year 2006. Accuracy of the object-based aquaculture facilities classification is 84% or better for the SPOT5 imagery and 47.9% for the ASTER imagery. The study provides a synoptic LULC representation for the largest lagoon system of Southeast Asia and delivers quantitative estimates of main changes occurred during the last 50 years. Moreover, it reveals the adaptability of the CORINE Land Cover method outside European environment. Finally, SPOT5 provides good results to map aquaculture features at cadastral scale, even if in some circumstances (e.g. tidal areas), the integration with higher spatial resolution multispectral sensors should be envisaged.     

Integrating land-cover data with different ontologies: identifying change from inconsistency

Spatially coincident land-cover information frequently varies due to technological and political variations. This is especially problematic for time-series analyses. We present an approach using expert expressions of how the semantics of different datasets relate to integrating temporal time series land-cover information where the classification classes have fundamentally changed. We use land-cover mapping in the UK (LCMGB and LCM2000) as example data sets because of the extensive object-based meta-data in the LCM2000. Inconsistencies between the two datasets can arise from random, gross and systematic error and from an actual change in land cover. Locales of possible land-cover change are inferred by comparing characterizations derived from the semantic relations and meta-data. Field visits showed errors of omission to be 21% and errors of commission to be 28%, despite the accuracy limitations of the land-cover information when compared with the field survey component of the Countryside Survey 2000.     

Combining spatial data with survey data improves predictions of boundaries between settlements

Studies of land-use change often require the combination of socioeconomic survey data with spatially continuous maps of land-cover change. One approach is to define maps of land ownership, assuming that all land-use change can be attributed to the owners or managers of each parcel of land. Unfortunately, records of administrative boundaries between towns and villages are commonly unavailable in developing countries and prohibitively costly or time consuming to map for individual projects. However, point locations of the settlements themselves can be obtained easily from existing maps or remotely-sensed imagery. In this paper we compare three methods – circular buffers, unweighted Voronoi polygons (sometimes referred to as Thiessen polygons) and multiplicatively weighted Voronoi polygons – for estimating boundaries between villages in an agricultural landscape in West Africa. The benefits and limitations of each approach are discussed, and their accuracy assessed using 98 independently collected GPS coordinates of village boundaries. We present a novel method for generating and optimising weights for multiplicatively weighted Voronoi polygons using survey data of village sizes from a subset of villages. By using both spatial information and survey data from villages, we show that multiplicatively weighted Voronoi polygons outperform other methods of predicting village boundaries, and increase the correlation coefficient between surveyed village area and mapped areas from 0.18 to 0.68 compared with more commonly used unweighted Voronoi polygons. Our method of weighting Voronoi polygons can be implemented with data and software commonly available to researchers and non-governmental organisations.     

土地利用/土地覆被变化研究:寻求新的综合途径

土地利用/土地覆被变化是很复杂的现象,参与该项目的研究人员要避免“瞎子摸大象”那样的片面性,必须寻求新的综合研究途径。为此,不能简单地沿袭传统土地利用研究的思路和方法,需要不断提出新的研究论题;对土地利用变化驱动力必须有一种普遍的、综合的认识;需要将多个案例研究联结为一个可代表区域空间异质性的网络,需要作多空间尺度的研究,从而将地方尺度和区域尺度的土地覆被动态联系起来;需要发展新的研究方法,并将从农户调查到遥感数据的各种信息综合起来;尤其需要形成关于土地利用/土地覆被变化的综合科学理论框架     

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.     

Thematic land-cover map assimilation and synthesis: the case of locating potential bioenergy feedstock in eastern Ontario,Canada

Insufficient spatial coverage of existing land-cover data is a common limitation to timely and effective spatial analysis. Achieving spatial completeness of land-cover data is the most challenging for large study areas which straddle ecological or administrative boundaries, and where individuals and agencies lack access to, and the means to process, raw data from which to derive spatially complete land-cover maps. In many cases, various sources of secondary data are available, so that land-cover map assimilation and synthesis can resolve this research problem. The following paper develops a reliable and repeatable framework for assimilating and synthesizing pre-classified data sets. Assimilation is achieved through data reformatting and map legend reconciliation in the context of a specific application. Individual maps are assessed for accuracy at various geographic scales and levels of thematic precision, with an emphasis on the ‘area of overlap’, in order to extract information that guides the synthesis process. The quality of the synthesized land-cover data set is evaluated using advanced accuracy assessment methods, including a measure describing the ‘magnitude of disagreement’. This method is applied to derive a seamless thematic map of the land cover of eastern Ontario from two disparate map series. The importance of assessing data quality throughout the process using multiple reference data sets is highlighted, and limitations of the method are discussed.     

Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty

In this study, we propose, describe, and demonstrate a new geovisualization tool to demonstrate the use of exploratory and interactive visualization techniques for a visual fuzzy classification of remotely sensed imagery. The proposed tool uses dynamically linked views, consisting of an image display, a parallel coordinate plot, a 3D feature space plot, and a classified map with an uncertainty map. It allows a geoscientist to interact with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions and fuzzy transition zones of land-cover classes. The purpose of this tool is to improve insight into fuzzy classification of remotely sensed imagery and related uncertainty. We tested our tool with a visual fuzzy land-cover classification of a Landsat 7 ETM+ image of an area in southern France characterized by objects with indeterminate boundaries. Good results were obtained with the visual classifier. Additionally, a focus-group user test of the tool showed that insight into a fuzzy classification algorithm and classification uncertainty improved considerably.     

Multi-faceted land cover and land use change analyses in the Yellow River Basin based on dense Landsat time series: Exemplary analysis in mining,agriculture, forest,and urban areas

The Yellow River Basin is one of China's most dynamic regions, where past and recent anthropogenic land use activities and polices have had a remarkably impact on the basin's surface. Over the past decades, the rapid socio-economic development has increased the pressure on the prevailing water and land resources with various repercussions on the environment and society. Counteracting ecological degradation in the basin, large-scale conservation and restoration plans have been initiated to expand vegetation coverage on deteriorated land, simultaneously fostering rural sustainable agriculture production. In this context, we derived precise spatial thematic products from long-term satellite time-series about high-frequency temporal dynamics. This information, available in a consistent and repeatable fashion is rare and relevant for many regional and local stakeholders and must be monitored annually to capture the rapid rate of change. Such information serves as a valuable base for decision-making processes. In this study, we used all the archived Landsat images between 2000 and 2015 (4520 scenes) to computed annually the spatially continuous spectral-temporal and textual metrics based on dense Landsat time-series to derive annual maps showing the most prominent land-cover change types related to mining, agriculture, forestry, and urbanization in four sub-regions spread over the Yellow River Basin. These novel land cover/use products provide new insights into recent regional and local dynamics. For final classification, we employed random forest classifiers for each thematic focus-region, trained and tested based on a stable-pixels data set. The resulting maps achieved high accuracies and show afforestation on the Loess Plateau and urbanization as the most prominent drivers of land use/cover dynamics. Agricultural land remained stable, showing local small-scale dynamics. Our study highlights the great potential of using consistent spectral-temporal metrics derived from dense Landsat time-series data together with a stable pixels reference set, allowing for local and regional land surface dynamics mapping at high spatial resolution and the prediction of implications of future change for effective and sustainable basin management.     

基于模糊集的土地利用变化自动发现方法探讨

传统的逐像素比较法忽略了地理对象的模糊性,缺乏区分噪声与真实变化信息的能力,难于处理空间数据的复杂性和不确定性。基于模糊集理论,根据邻域像元的空间位置关系确定变化的程度,并利用滑窗和景观指数表达比较不同时相遥感数据(TM、ETMI、KONOS)的分类专题图。试验结果表明,模糊集的描述更接近真实情况。     

信息重分类在土地覆盖动态监测中的应用——以澜沧江梯级电站库区为例

根据士地覆盖动态监测的需求,设计了重分类方法,对遥感土地覆盖分类图及坡度分类图进行了分类码的设计和GIS数据建库的讨论,通过信息叠加可以得到所需要的信息,进而可以分析不同土地类型及在不同坡度类型下地类之间的相互转化规律.方法应用于澜沧江梯级电站库区,对1988年、1996年和2000年的土地覆盖资料进行了分析,并通过野外实地调查资料进行验证,得出该区土地覆盖时空变化规律,进而可以为该区人类活动和环境之间相互关系与产沙机理提供分析依据,表明信息重分类方法在土地覆盖动态变化监测及其他相关领域具有十分重要的应用价值.     

Longitudinal analysis of the road network development and land-cover change in Lop Buri province, Thailand, 1989-2006

Land use and land-cover change (LUCC) is mainly a consequence of human activities such as road network development. In Thailand, the explicit objective of road network development has been to foster economic and social development. The extent of change in roads and land-cover change in Lop Buri between 1989-2006 is analyzed. We hypothesized that road development is a key driver of land-cover change and will cause, substantial changes in areas of forest cover, cultivated food and cash crops. We used land-cover classifications of Landsat imagery and resultant class trajectories to measure change. We then analyzed the relationships between roads, land-cover types, and land-cover trajectories. Overall, cash crops increased while forest and food crops declined between 1989-2006. The relationships between distance to roads and land- cover trajectories indicate that as the distance to roads increase, there are fewer changes in LUCC. The results suggest that in this case study, an increase in road network contributes to an increase in upland crops. In turn, the increase in upland crop production is one of the factors linked to economic development.     

Modeling of urban growth in tsunami-prone city using logistic regression: Analysis of Banda Aceh,Indonesia

The urban development of Banda Aceh, Indonesia was very rapid after the tsunami in 2004, posing critical challenges in planning for its future sustainable development. Scientifically-derived information about its land change patterns and the driving factors of its rapid urbanization might provide vital information. However, the spatio–temporal patterns of its urban land use/cover (LUC) changes have not been examined. Hence, this study aims to: (1) detect and analyze the spatio–temporal changes in the urban LUC of Banda Aceh between 2005 and 2009; and (2) examine the driving factors that influence urban growth. The 2005 and 2009 LUC maps were derived from remote sensing satellite images using a supervised classification method (maximum likelihood). Both LUC maps contained four categories, namely built-up area, vegetation, water body, and wet land. The 2005 LUC map had an overall accuracy of 77.8%, while the 2009 LUC map had 89.4%. The two LUC maps were re-classed into two categories (i.e. built-up area and non built-up area) to facilitate logistic regression analysis. A total of seven variables or potential driving factors of urban growth were identified and examined, including two socio-economic factors (population density and distance to central business district) and five biophysical factors (distances to green open space, historical area, river, highway, and coastal area). The results showed that the LUC of Banda Aceh has changed drastically between 2005 and 2009, particularly its built-up area, which increased by 90.8% (1016.0 ha) at the expense of the other LUC categories. The socio-economic factors showed positive influence to the growth of the city, whereas the biophysical factors showed negative effect, except the distance to coastal areas. The importance of the findings for future landscape and urban planning for Banda Aceh is discussed.     

梭磨河流域气候波动和土地覆被变化对径流影响的模拟研究

利用一个集总式水文模型(CHARM),模拟了不同的气候与土地覆被条件下长江上游梭磨河流域的水量平衡,定量区分气候波动和土地覆被变化对水文影响的“贡献率”。模型模拟的初步结果表明,从20世纪60年代到80年代,径流深增加了45.7mm,其中,由气候波动所引起的年平均径流深的增加占63.9%,由土地覆被变化所引起的径流深的增加占20.8%,其他条件的变化所引起的径流深的增加占15.3%。     

Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization

This study evaluates land use/cover changes and urban expansion in Greater Dhaka, Bangladesh, between 1975 and 2003 using satellite images and socio-economic data. Spatial and temporal dynamics of land use/cover changes were quantified using three Landsat images, a supervised classification algorithm and the post-classification change detection technique in GIS. Accuracy of the Landsat-derived land use/cover maps ranged from 85 to 90%. The analysis revealed that substantial growth of built-up areas in Greater Dhaka over the study period resulted significant decrease in the area of water bodies, cultivated land, vegetation and wetlands. Urban land expansion has been largely driven by elevation, population growth and economic development. Rapid urban expansion through infilling of low-lying areas and clearing of vegetation resulted in a wide range of environmental impacts, including habitat quality. As reliable and current data are lacking for Bangladesh, the land use maps produced in this study will contribute to both the development of sustainable urban land use planning decisions and also for forecasting possible future changes in growth patterns.     

Forest dynamics and the importance of place in western Honduras

Analyses of landscape change using remotely sensed satellite imagery constitute a large component of forest transition research, allowing for assessments of large areas. In the western highlands of Honduras is an area of complex forest dynamics (～45,000 ha) that has seen significant forest regeneration in recent years. However, analysis of the larger region (～500,000 ha) shows net forest loss. The comparative aspects highlight the importance of site selection and scale in forest transition analysis, a process often ignored in the land-use and land-cover change (LULCC) and forest transition literature. Results also highlight the importance of analyzing human-induced fragmentation at a variety of selected sites and a range of spatial scales, and producing quality, accurate forest cover and change maps.     

Comparing historical and contemporary maps - a methodological framework for a cartographic map comparison applied to Swiss maps

Topographic maps are powerful tools for the purpose of identifying land use and cover change (LUCC) as they are among the most reliable representations of past landscapes for the time prior to the existence of aerial photography. In light of the increased availability of historical maps, we argue that there is a need for a standardized process to assess map comparability in a systematic way in order to avoid, or at least minimize, the detection of spurious landscape changes due to incompatible map series. A full understanding of map quality, background and error distributions is fundamental to attain reliable LUCC results. The conceptual framework presented in this study considers the context, distortion and cartographic generalization of topographic maps. Furthermore, it includes an approach to homogenize the level of generalization of landscape elements (e.g. forests) from maps with different scales. To demonstrate its application, we assessed the comparability of seven topographic maps from Canton Zurich covering a time span of 336 years (1664–2000). Overall, for the maps of Canton Zurich, a wall-to-wall comparison of forest cover based on the topographic maps presented here can be problematic for the oldest map from 1664. Based on the results, a wall-to-wall comparison with the later maps is not recommended, due to its substantial distortion. Yet, after re-generalization of natural landscape elements, such as forests, a comparison based on landscape indices is possible, even for the oldest map. Furthermore, our results demonstrate that maps from the mid-19^th century onward possess an acceptable level of accuracy. This framework can be applied to a wide range of maps at regional, national, or global levels, providing the opportunity to look at land cover history over multiple centuries.     

海岸带城市土地利用变化分析—以江苏盐城市为例

土地利用/覆被变化（LUCC）是全球变化和区域可持续发展研究的核心问题。海岸带是陆地海洋相互作用和人类活动最敏感的区域,海岸带LUCC的研究对适应环境和自然的挑战具有重要意义。本文以江苏沿海具有代表性的盐城市作为研究区,选取1978-2007年5期Landsat MSS/TM影像作为数据源,在参考国内外遥感分类系统的基础上,结合研究区实际,建立遥感分类系统,使用主成分分析（PCA）遥感分类方法和分类后比较变化检测方法,并在GIS分析技术支持下获取盐城市近30年的土地利用变化数据,分析其结构、数量、速率及格局变化。结果表明：（1）从土地利用结构上看,研究时段内盐城市土地利用变化较大,但利用结构相对稳定;土地利用类型以耕地为主,但近几年城市建设用地大量增加,比重不断攀升;（2）从土地利用变化数量、速率上看,不同时期土地利用类型变化剧烈,一级地类数量增长最大的是建设用地,其中城镇建设用地由1978年的167.01 km2扩张至2007年的725.19 km2,增长至原来的4.34倍,城镇建设用地变化速率在2004-2007年高达15%以上;（3）从空间格局变化看,盐城市中心城区特别是开发区和新辟为市区的盐都区发展速度最快,而滨海港、大丰港的建设和周边国有盐场的改制也显著促进了沿海地区的发展。     

A review of historical reconstruction methods of land use/land cover

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects: (1) Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. (2) Historical maps and pictures offer visual and spatial quantitative land-cover information. (3) Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. (4) Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. (5) Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.