Mapping and monitoring of land use changes in post-Soviet western Ukraine using remote sensing data

While agriculture is expanded and intensified in many parts of the world, decreases in land use intensity and farmland abandonment take place in other parts. Eastern Europe experienced widespread changes of agricultural land use after the collapse of the Soviet Union in 1991, however, rates and patterns of these changes are still not well understood. Our objective was to map and analyze changes of land management regimes, including large-scale cropland, small-scale cropland, and abandoned farmland. Monitoring land management regimes is a promising avenue to better understand the temporal and spatial patterns of land use intensity changes. For mapping and change detection, we used an object-based approach with Superpixel segmentation for delineating objects and a Random Forest classifier. We applied this approach to Landsat and ERS SAR data for the years 1986, 1993, 1999, 2006, and 2010 to estimate change trajectories for this time period in western Ukraine. The first period during the 1990s was characterized by post-socialist transition processes including farmland abandonment and substantial subsistence agriculture. Later on, recultivation processes and the recurrence of industrial, large-scale farming were triggered by global food prices that have led to a growing interest in this region.     

塔里木盆地南缘和田地区土地利用变化的遥感研究

中国西部干旱区土地利用变化显著,是全球土地变化科学研究的热点区,为此论文基于1990—2016年4个时段的遥感卫星数据,采用面向对象的分层分类影像解译方法完成了塔里木盆地南缘和田地区(简称和田塔里木地区)土地利用调查,重点分析土地利用变化特征、发展模式和区域差异性。结果表明：1990—2016年,和田塔里木地区耕地持续加速扩张(增长率为2.9%/a),呈现渐进式扩张和骤变式开垦2种发展模式;建设用地面积增加(12.1%/a),主要表现为城市化发展、农村居民用地和交通用地的增加;耕地和建设用地扩张导致林灌草地和未利用地面积减少;和田地区县域土地利用发展不均衡,和田市建设用地比例最高,和田县与墨玉县的农业和建设用地扩张总量和速率最大,其次是洛浦县和于田县,皮山县、策勒县和民丰县农业和建设用地增长相对缓慢;总体上,和田塔里木地区耕地和建设用地的扩张在新疆处于一个较快的发展水平,今后一段时期快速的土地利用变化可能引起的生态环境问题需要重点关注。     

Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data

The potential impact of deforestation in the Brazilian Amazon on greenhouse gas emissions to the atmosphere calls for policies that take account of changes in forest cover. Although much research has focused on the location and effects of deforestation, little is known about the distribution and reasons for the agricultural uses that replace forest cover. We used Landsat TM-based deforestation and agricultural census data to generate maps of the distribution and proportion of four major agricultural land uses throughout the Brazilian Amazon in 1997 and 2007. We built linear and spatial regression models to assess the determinant factors of deforestation and those major agricultural land uses - pasture, temporary agriculture and permanent agriculture - for the states of Pará, Rondônia, and Mato Grosso. The data include 30 determinant factors that were grouped into two years (1996 and 2006) and in four categories: accessibility to markets, public policies, agrarian structure, and environment. We found an overall expansion of the total agricultural area between 1997 and 2007, and notable differences between the states of Pará, Rondônia, and Mato Grosso in land use changes during this period. Regression models for deforestation and pasture indicated that determinant factors such as distance to roads were more influential in 1997 than in 2007. The number of settled families played an important role in the deforestation and pasture, the effect was stronger in 2007 than 1997. Indigenous lands were significant in preventing deforestation in high-pressure areas in 2007. For temporary and permanent agricultures, our results show that in 1997 the effect of small farms was stronger than in 2007. The mapped land use time series and the models explain empirically the effects of land use changes across the region over one decade.     

基于多期数据集的中亚五国土地利用/覆盖变化分析

针对目前中亚地区土地利用变化和分布格局方面的信息相对匮乏,现有资料较为陈旧且零散,无法满足中亚生态与环境变化研究需求的现状,利用全球的UMD, DISCover,GLC2000,GlobCover2005和GlobCover2009的5期土地覆被遥感数据集,提取中亚地区长时间序列土地覆被信息。并针对上述4个土地覆被分类系统无法进行直接对比和变化分析的问题,分别将其综合为4类土地覆被类型：耕地、自然植被、水体和其他,以分析近30 a中亚土地利用/土地覆被变化趋势。中亚土地利用类型多样,草地、裸地、农田、灌丛占绝对优势。自前苏联解体以后,20世纪90年代初至2000年期间,耕地面积大幅度减少,至2010年尽管有所恢复,但仍无法达到20世纪90年代初水平。而自然植被表现出了相反的趋势,这说明在此时间段内,由于社会政治制度的变化和市场经济的建立,耕地发生了较大规模的弃耕,弃耕地通常转换为草地、灌丛等自然植被。近10 a由于社会经济条件的变化,前苏联解体后所弃耕的土地又被收复和重新开发为耕地。90年代初至2000年期间,水体呈现先减少后增加的趋势。利用全球基于多期不同信息源获得的中亚土地覆被数据,尽管分类体系不统一,但均可较好地表征当时地表覆被状况。这在一定程度上弥补了中亚地区土地覆被数据不足的现状。通过对耕地、自然植被、水体及其他土地覆被类型进行大类合并,可基本体现中亚土地覆被的宏观特征和变化趋势。     

Remote sensing and GIS-based regional geomorphological mapping—a tool for land use planning in developing countries

Land use planning and necessary supporting data are crucial to developing countries that are usually under severe environmental and demographic strains. Approaches and methods to map the variability of natural resources are important tools to properly guide spatial planning. In this paper, we describe a method to quickly map terrain at reconnaissance (1:250,000) and semi-detailed (1:50,000) levels. This method can be utilized as a basis for further land evaluation and land use planning in large territories. The approach was tested in the state of Michoacan, central-western Mexico, currently undergoing rapid deforestation and subsequent land degradation.Results at the reconnaissance level describe the geographic distribution of major landforms and dominant land cover, and provide a synoptic inventory of natural resources. Results at the semi-detailed level indicate how to nest individual landforms to major units and how they can be used to run procedures for land evaluation. If combined with appropriate socioeconomic data, governmental guidelines for land use planning can be formulated on the basis of reconnaissance and semi-detailed terrain analysis.     

Measuring rural settlement expansion in Uzbekistan using remote sensing to support spatial planning

Population growth demands sustainable spatial planning strategies for settlements in Uzbekistan, Central Asia, especially in rural areas that are inhabited by approximately 64 percent of the country's population. Where can settlements expand in rural Uzbekistan and does settlement growth affect valuable agricultural land? SPOT-5 data with a resolution of 2.5 m was utilized for mapping building layers and assessing settlement growth between 2006 and 2011 at the example of 53 communities located in the Khorezm province in North-West Uzbekistan. Object based image analysis was conducted, i.e. a multi-scale segmentation for the derivation of building contours, followed by a random forest (RF) classification of the object's spectral and spatial characteristics. A geographical information system (GIS) was used for identifying settlement densification and expansion processes, and for quantifying agricultural land parcels of different soil quality occupied during settlement growth.A calibration routine based on indices of segmentation quality enabled the selection of optimal segmentation parameters. After GIS-based refinements of the RF classification results, the overall accuracy (OA) of the building layers of both years exceeded 95%. The OA of the change map was 92.7%. The results revealed that the building area increased by 20%, whilst settlement expansion amounted to 10% in 2006–2011. Settlements widely expanded in accordance with the existing rules prohibiting the conversion of agricultural land to housing areas. Nevertheless, about 20% of the settlement growth occurred on agricultural production areas, also on those with highly productive soils. The results indicated both, the pressure on land resources for settlement growth and – in face of continuous population growth – an increasing demand for comprehensive spatial planning in rural Uzbekistan. The elaborated methodological approach can be extrapolated to regions throughout Central Asia with similar environmental conditions.     

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.     

Monitoring land changes in an urban area using satellite imagery,GIS and landscape metrics

Monitoring land changes is an important activity in landscape planning and resource management. In this study, we analyze urban land changes in Atlanta metropolitan area through the combined use of satellite imagery, geographic information systems (GIS), and landscape metrics. The study site is a fast-growing large metropolis in the United States, which contains a mosaic of complex landscape types. Our method consisted of two major components: remote sensing-based land classification and GIS-based land change analysis. Specifically, we adopted a stratified image classification strategy combined with a GIS-based spatial reclassification procedure to map land classes from Landsat Thematic Mapper (TM) scenes acquired in two different years. Then, we analyzed the spatial variation and expansion of urban land changes across the entire metropolitan area through post classification change detection and a variety of GIS-based operations. We further examined the size, pattern, and nature of land changes using landscape metrics to examine the size, pattern, and nature of land changes. This study has demonstrated the usefulness of integrating remote sensing with GIS and landscape metrics in land change analysis that allows the characterization of spatial patterns and helps reveal the underlying processes of urban land changes. Our results indicate a transition of urbanization patterns in the study site with a limited outward expansion despite the dominant suburbanization process.     

Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing

The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.