Using neural networks and GIS to forecast land use changes: a Land Transformation Model

The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan's Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales.     

Trends in night-time city lights and vegetation indices associated with urbanization within the conterminous USA

Two datasets that depict the night-time light emitted from the conterminous USA during 1992/1993 and 2000 were compared for changes in light emission. The locations of observed differences in night-time light during this interval were examined for differences observed in a time-integrated vegetation index associated with net primary production. Just over 13% of the land area within the study region exhibited greater night-time light emitted in 2000 compared to 1992/1993. The locations of greater emitted light were found to have decreased values of the time-integrated vegetation index compared to locations that did not exhibit significant increases in emitted light. The observed decrease in the time-integrated vegetation index within the regions of greater emitted light is likely to be due to the change in land cover (increased urbanization) during this interval. The results suggest that the emitted light data were more useful for assessment of urban growth than the integrated vegetation index data.     

A new land cover map of central Africa derived from multi-resolution,multi-temporal AVHRR data

We describe a new map of the central Africa region that was derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) observations using a fusion of Local Area Coverage (LAC, 1 km), Global Area Coverage (GAC, 8 km), and ancillary information. The land cover map, produced for the Central Africa Regional Program for the Environment (CARPE), offers a synoptic view of the extent of central African dense humid forests, at relatively fine spatial resolution. Land cover types include dense humid forest, disturbed or degraded forest and various savanna classes. Ancillary information includes political and park boundaries, settlements, rivers and roads. Map validation was performed using a combination of field visits and finer resolution imagery (Landsat Multi-Spectral Scanner (MSS)). Forest cover type mapping errors were at most 20 per cent. The resulting map is useful for addressing a number of resource management issues, a few of which are examined.     

太湖湖滨敏感区的土地利用遥感分类研究

近年来太湖流域水体污染日趋严重,土地利用是重要的环境变化影响因子,对太湖湖滨敏感区土地利用分类研究具有重要意义。研究基于2010年ALOS多光谱遥感影像,以太湖流域上游的武进港、直湖港流域为研究区,根据研究区实际状况和研究目的,建立太湖流域上游湖滨敏感区的土地利用/土地覆被分类系统,并用于该地区的面向对象遥感分类,研究通过影像的多尺度分割,获得不同层次的影像对象,在不同层次设置对应的分类规则,以充分利用影像中地物的光谱、纹理和不同层对象相互关系等信息,从而提高分类效果。研究表明:在面向对象多尺度影像分割的基础上,基于决策树建立多个分类规则的分类方法,能够有效提取建设用地、道路、水体等几类信息,分类总体精度达到88.00%;同时,该地区主要土地利用类型如耕地、农村居民点和城镇居民点的分类精度也较高,这也表明该分类方法对整个太湖流域以及其他平原河网地区的土地利用相关研究具有一定的实用价值。     

Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model

A stepwise procedure has been developed in Python to extract information from OpenStreetMap (OSM) for hydrological and hydraulic models using existing and newly developed tools. The procedure focuses on the extraction of paved areas and water bodies. Road density is used to fill in gaps in OSM polygon coverage. Furthermore, it includes automatic downloading of Shuttle Radar Topography Mission (SRTM) elevation data and improving the elevation model with man-made landscape features such as elevated roads that are sampled from OSM. This is useful for hydraulic modelling in data scarce flood plain areas, where sharp elevation differences are dominated by man-made elevated elements. Test cases in Europe, South East Asia and East Africa demonstrate the potential of the procedure, although large differences in completeness of OSM coverage suggest it is best used in combination with other data sources.     

From landscapes to waterscapes: A PSE for landuse change analysis

We describe the design and implementation of L2W – a Problem Solving Environment (PSE) for landuse change analysis. L2W organizes and unifies the diverse collection of software typically associated with ecosystem models (hydrological, economic and biological). It provides a web-based interface for potential watershed managers and other users to explore meaningful alternative land development and management scenarios and view their hydrological, ecological, and economic impacts. A prototype implementation for the Upper Roanoke River Watershed in Southwest Virginia, USA is described.     

A hierarchical approach to Compact Airborne Spectrographic Imager (CASI) high-resolution image classification of Little Miami River Watershed for environmental modelling

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed in Ohio, USA, which was one of the largest hyperspectral image acquisitions. A hierarchical approach was employed using two different classification algorithms: ‘image object segmentation’ for level 1 and ‘spectral angle mapper’ (SAM) for level 2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land‐use/land‐cover (LULC) classes were urban/built, corn, soya bean, wheat, forest, dry herbaceous, grass, lentic, lotic, urban barren, rural barren and unclassified. The final phase of processing was completed after an extensive quality assurance and quality control (QA/QC) phase with 902 points. The overall accuracy was 83.9%. The data set was made available for public research and application; certainly, this product represents an improvement over more commonly utilized, coarser spatial resolution data sets such as National Land Cover Data (NLCD).     

基于GIS的中国农村居民点用地分析

利用90年代初期至末期中国土地利用矢量数据提取农村居民点用地及动态变化数据，将其与自然区划图叠加，分析了各自然区农村居民点用地及其动态变化情况。中国农村居民点用地主要集中在华北平原、鲁中山地丘陵、辽东胶东山地丘陵、淮河与长江中下游及东北平原。这些地区是重要的农业区，城镇发达。农村居民点密集，人口密度高。淮河与长江中下游、华北平原、粤桂闽丘陵平原、华北山地丘陵是农村居民点用地扩展最快的地区，也是农村居民点扩展造成耕地资源流失的主要地区。农村居民点用地扩展造成的耕地流失甚至大于城镇用地扩展造成的耕地流失，并且是在农村人口绝对量逐渐减少的情况下发生的。因此，制定相关政策，加强农村居民点用地管理对于节约土地资源具有重要意义。     

Recent land‐cover/use change associated with land degradation in the Lake Baringo catchment,Kenya, East Africa: evidence from Landsat TM and ETM+

Many parts of East Africa are experiencing dramatic changes in land‐cover/use at a variety of spatial and temporal scales, due to both climatic variability and human activities. Information about such changes is often required for planning, management, and conservation of natural resources. Several methods for land cover/change detection using Landsat TM/ETM+ imagery were employed for Lake Baringo catchment in Kenya, East Africa. The Lake Baringo catchment presents a good example of environments experiencing remarkable land cover change due to multiple causes. Both the NDVI differencing and post‐classification comparison effectively depicted the hotspots of land degradation and land cover/use change in the Lake Baringo catchment. Change‐detection analysis showed that the forest cover was the most affected, in some sections recording reductions of over 40% in a 14‐year period. Deforestation and subsequent land degradation have increased the sediment yield in the lake resulting in reduction in lake surface area by over 10% and increased turbidity confirmed by the statistically significant increase (t = ?84.699, p<0.001) in the albedo between 1986 and 2000. Although climatic variations may account for some of the changes in the lake catchment, most of the changes in land cover are inherently linked to mounting human and livestock population in the Lake Baringo catchment.     

基于城镇居民用地再分类的人口数据空间化方法研究——以长江中游4省为例

基于土地利用数据的人口统计数据空间化方法,在处理过程中会出现同一土地利用类型下人口难以细分的情况,从而影响人口空间数据精度。引入夜间灯光信息并提出了一种基于夜间灯光强度对城镇居民地再分类的人口空间化方法,以改善人口空间数据精度。基于DMSP/OLS夜间灯光及土地利用数据,以长江中游4省为研究区进行方法试验。研究结果显示:利用夜间灯光数据对城镇居民地再分类后,各分区模型的调整R2都提高到了0.8以上,人口空间数据总体平均相对误差较重分类前降低了12.32%。说明该方法在提高传统人口数据空间化模型精度的基础上能够细化城镇居民地人口空间分布。     

Support vector machine approach to identifying buildings using multi-temporal ALOS/PALSAR data

Accurate detection of built-up areas is valuable for quantifying the level of urbanization and monitoring the decreasing amount of agricultural land. In this study, three-temporal, dual-polarization Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) images were integrated to map buildings in the Yangtze River Delta of East China, where land has been intensively used. The results show that the support vector machine (SVM) classifier performs well in identifying buildings, with an accuracy of 90% in urban areas and 95% in rural areas, even with only a small number of training samples. Buildings in urban areas are more likely to be underestimated (commission error of 15%) than those in a rural environment. Visual inspection and quantitative analysis confirmed that the Local Sigma Filter considerably reduced random speckle noise in the PALSAR imagery. Thus, the filter is suitable for enhancing feature extraction of future multi-polarization and multi-temporal SAR imagery. Overall, the buildings identification approach proposed in this study could serve as a valuable tool for operational monitoring of rural land use change and urban sprawl.     

A Reduced Parameter Stream Temperature Model (RPSTM) for basin-wide simulations

Water temperature is a crucial variable that shapes biological communities and controls rates of ecosystem processes in rivers. Fully parameterized heat balance models have been used to provide accurate estimates, but high parameterization costs make them difficult to apply at basin-wide scales. As parts of a collaborative modeling project to address future impacts of climate and land-use management on the Muskegon River, we developed a Reduced Parameter Stream Temperature Model (RPSTM), a mechanistic, spatially explicit but easier to parameterize model. Here we describe and test RPSTM's applicability by conducting a series of daily water temperature simulations (1985–2005). RPSTM performed well along the river network. The predictions were most sensitive to air temperature, depth, and solar radiation, but relatively insensitive to rates of surface runoff. This modeling approach is easily integrated into complex multi-modeling systems to evaluate effects of long-term changes in watershed hydrology, climate, and land management across river networks.     

From qualitative to quantitative environmental scenarios: Translating storylines into biophysical modeling inputs at the watershed scale

Scenarios are increasingly used for envisioning future social-ecological changes and consequences for human well-being. One approach integrates qualitative storylines and biophysical models to explore potential futures quantitatively and maximize public engagement. However, this integration process is challenging and sometimes oversimplified. Using the Yahara Watershed (Wisconsin, USA) as a case study, we present a transparent and reproducible roadmap to develop spatiotemporally explicit biophysical inputs [climate, land use/cover (LULC), and nutrients] that are consistent with scenario narratives and can be linked to a process-based biophysical modeling suite to simulate long-term dynamics of a watershed and a range of ecosystem services. Our transferrable approach produces daily weather inputs by combining climate model projections and a stochastic weather generator, annual narrative-based watershed-scale LULC distributed spatially using transition rules, and annual manure and fertilizer (nitrogen and phosphorus) inputs based on current farm and livestock data that are consistent with each scenario narrative.     

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically-distributed every 0.5° over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.     

Developing land use/land cover parameterization for climate–land modelling in East Africa

Regional climate modeling studies now have numerous choices in selecting land use/land cover (LULC) products to provide land surface parameter information. The various LULC products were developed with different objectives, methods and data sources. Not all new LULC products have land classes that match the land class types defined in climate models. More importantly, when used in regional climate models, simulation results can vary significantly depending on the LULC products. Thus, developing appropriate LULC parameterization for climate models becomes critical depending on objectives and efforts. The objective of this paper is to develop the most accurate LULC scheme possible for East Africa for implementation in the Regional Atmospheric Modeling System (RAMS). A crosswalk procedure, based on assessments of various LULC products, was performed connecting land class types in RAMS and the newly created LULC scheme. No simulations are discussed here; rather, we present an outline of the procedures that were carried out to take advantage of the strengths of currently available LULC products, Africover and Global Land Cover 2000, for the purpose of conducting regional climate simulations.     

Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan,China

Cellular Automata (CA) are widely used to model the dynamics within complex land use and land cover (LULC) systems. Past CA model research has focused on improving the technical modeling procedures, and only a few studies have sought to improve our understanding of the nonlinear relationships that underlie LULC change. Many CA models lack the ability to simulate the detailed patch evolution of multiple land use types. This study introduces a patch-generating land use simulation (PLUS) model that integrates a land expansion analysis strategy and a CA model based on multi-type random patch seeds. These were used to understand the drivers of land expansion and to investigate the landscape dynamics in Wuhan, China. The proposed model achieved a higher simulation accuracy and more similar landscape pattern metrics to the true landscape than other CA models tested. The land expansion analysis strategy also uncovered some underlying transition rules, such as that grassland is most likely to be found where it is not strongly impacted by human activities, and that deciduous forest areas tend to grow adjacent to arterial roads. We also projected the structure of land use under different optimizing scenarios for 2035 by combining the proposed model with multi-objective programming. The results indicate that the proposed model can help policymakers to manage future land use dynamics and so to realize more sustainable land use patterns for future development. Software for PLUS has been made available at https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model     

Remote-sensing assessment of forest damage by Typhoon Saomai and its related factors at landscape scale

The remote-sensing technique is a cost-effective tool for monitoring large-scale forest damage sustained by typhoon events. Taking Cangnan County as the study area, this study aimed to extract the spatial pattern of damaged forest and determine the influencing factors of Typhoon Saomai in 2006, using Landsat Enhanced Thematic Mapper Plus (ETM+) data before and after the typhoon event. The results showed that 183 km² of forest land were damaged by Typhoon Saomai. There was no obvious diverse influence on forest damage within 25 km of Saomai’s path, after that the area of damaged forest decreased rapidly. For the land uses of construction, crop, and grass, decrease in normalized difference vegetation index was considerable under 100 m elevation and the number of damaged forest pixels showed positive correlation with vegetation aggregation, because trees standing in isolation, alongside roads, or in small groupings were easily damaged. For forest land, the number of damaged forest pixels decreased with higher elevation and relative aspect; when the relative aspect was in the range 0–40°, the number of damaged forest pixels was highest. Considering the interactive effects of these factors on forest damage caused by the typhoon, vegetation aggregation had the strongest influence followed by elevation, land use, relative aspect, and distance from the typhoon’s path.     

Comparison of landscape patterns between metropolises and small-sized cities: a gradient analysis with changing grain size in Shanghai and Zhangjiagang,China

Quantifying the urban landscape pattern and its change is fundamental for monitoring and assessing the ecological and socio-economic consequences of urbanization. Using Indian Remote Sensing Panchromatic (IRS-PAN) imagery of 2002 and combining gradient analysis with landscape metrics, we compared the landscape patterns between metropolises and small-sized cities with increasing grain size. Landscape metrics were computed along a 51?×?9 km transect cutting across Shanghai and a 16?×?2 km transect cutting across Zhangjiagang with a moving window. The results showed that fragmentation indices (patch density (PD), total edge (TE), landscape division (LD)) in land use transects of the two cities had many similarities, indicating the common characteristics of urbanization in different-sized cities. There were, however, some striking dissimilarities between the two cities, which did not support the hypothesis that PD increases exponentially along a landscape modification gradient. These differences may have three explanations: the proportion of urban roads area, the width of urban roads and land use change in suburban and rural areas. The proportion of urban road area and the width of urban roads, which should be considered in future research, are two main factors that have been identified to influence the landscape pattern analysis. In younger and smaller cities, spatial competition between local interest groups and imperfect land managerial systems result in an increase in fragmentation in the suburbs, and policymakers should pay more attention to future city planning and management. A bridge linking the urbanization landscape pattern and the process of urban fragmentation will be a key to urban landscape studies and planning.     

Integrating statistical and hydrological models to identify implementation sites for agricultural conservation practices

Watershed models are scarcely used by watershed managers due to their complexity. This study facilitates information transfer by introducing simpler techniques related to easily obtained watershed characteristics, including distance to the watershed outlet and stream order. The Soil and Water Assessment Tool (SWAT) was calibrated for the Saginaw River Watershed, Michigan. Five agricultural best management practices (BMPs) were implemented in SWAT one at a time in each subbasin. Five statistical models were used to determine the pollution reduction at the watershed outlet using distance and BMP type, with results suggesting that a mixed effects model (model 5) was optimal. This model included subbasin as a random effect, while distance to watershed outlet and BMP type were fixed effects. Native grass and strip cropping were the most effective BMPs for reducing sediment and nutrient transport. Subbasins containing stream orders 1–3 were ideal for BMP implementation throughout the watershed.     

基于遥感的祖厉河流域土地分类及其分布空间分析

以陇西黄土高原的祖厉河流域为研究区,利用1993和2007年两期TM影像数据对研究区土地利用类型进行分类,在此基础上结合数字高程模型(Digital Elevation Model,DEM)、多年平均降水量的空间分布数据,利用地理信息系统(GIS)空间分析方法,重点分析坡耕地、林地和草地的气候特征空间与地形特征空间,研究得出：①14 a内研究区大约有 214.82 km²坡耕地被治理,但2007年仍有145.08 km²处于临界坡度以上。②林地大致分布在降水量386~517 mm之间,14 a内人工林地有所增多。③在人为活动的干扰下,草地的覆盖度普遍降低。植被的恢复和重建是流域治理的切入点,该项研究目的为退耕还林还草提供科学支撑,服务于建立祖厉河流域生态系统的良性循环。