Multi-temporal Landsat TM Images Research Articles

The spatiotemporal change of cropland and its impact on vegetation dynamics in the farming-pastoral ecotone of northern China

Due to the unfavorable soil conditions and water resources, the cropland use pattern in the farming-pastoral ecotone in northern China is complex. The program named “Grain for Green” has accelerated the cropland change. However, the complex cropland and retired cropland are challenging to monitor with remote sensing due to their spatially dispersed and easily confused with spectrally similar land use classes such as nature grasslands and non-cropped fields. Taking farming-pastoral ecotone in the northern foot of the Yinshan Mountains as a case study, we explored a classification approach for complex cropland and retired cropland, which was introduced as a specific land use class by using multi-temporal Landsat TM and OLI images with Google Earth Engine. During 1990–2000, cropland increased with a sharper growth and increased with a slower growth from 2001 to 2010, and then decreased significantly from 2011 to 2019, to lead the cropland area in 2019 was smaller than an area in 1990. We analyzed the spatiotemporal trajectories of retired cropland in 2019 using the Land Use Change Trajectory method to evaluate its source. In our finding, approximately 77% of retired cropland was labelled as cropland before 2019; albeit, not all retired cropland was converted from cropland. Moreover, we qualitatively assessed the vegetation dynamics in the study area by utilizing the long-term NDVI-mean value to reveal that vegetation coverage has shown a continuously increasing trend. It is related to the decline of cropland and the increase of retired cropland at the same rate. Our results highlighted that the “Grain for Green” program had led the vegetation restoration in the farming-pastoral ecotone. Our approach for monitoring cropland and retired cropland can improve the understanding of the driving factors and consequences of these critical land use change trajectories.

Land Cover and Coastline Change Assessment of Nijhum Dwip, Bangladesh, using Geospatial Analysis

Nijhum Dwip is a southern island of Bangladesh isolated from the mainland, in the convergence of the Meghna River and the Bay of Bengal. This island has studied through overlay analysis and supervised classification by geospatial and remote sensing technique, over 38 years (1980-2018) using multitemporal Landsat MSS, TM, OLI, and TIRS satellite images with identification of historical changes. This landform is facing frequent shifting of its coastline and leading to sequential changes on the land surface. Analysis revealed substantial growth of settlement and agricultural land whereas significant lessening on vegetation cover and open space. In 1990 agricultural land was 4.47 km2 (13.29%) and improved to 9.16 km2 (19.17%) in 2018. Similarly, settlement also increased from 1.92 km2 (4.79%) in 1999 to 5.72 km2 (11.97%) in 2018. Conversely, vegetation was primarily 8.02 km2 (27.71%), 18.70 km2 (55.61%), 20.97 km2 (52.29%), 18.47 km2 (36.28%) and 15.28 km2 (31.98%) in 1980,1990,1999, 2010 and 2018, indicating declination. As well, water bodies and open space also fluctuated through the period because of geomorphological processes and human intervention. Besides, the least and highest unstable char land was 1.15 km2 (3.42%) and 1.68 km2 (5.80%) in 1990 and 1980.

Assessment of cropping intensity dynamics in Odisha using multitemporal Landsat TM and OLI images

Satisfying the global food demand of the growing population from limited cropland resources is a huge challenge for scientists and policymakers. Approximately 14 mha land remains fallow (rice-fallow) in the dry season after monsoon season crop in South Asia. Accurate geospatial information of cropping intensity is key to identifying the potential areas for crop intensification. In this regard, we aim to map and evaluate cropping intensity dynamics over Odisha, using moderate-resolution multitemporal Landsat OLI (2013 to 14 to 2017 to 18) and TM (2006 to 07) images. The study performed k-mean clustering on principal components extracted from annual surface reflectance composites to identify the presence and absence of crop in the kharif and rabi seasons. The overall accuracy was estimated as 0.92 ± 0.01, 0.91 ± 0.01, 0.92 ± 0.01, 0.89 ± 0.01, and 0.91 ± 0.01 for cropping intensity map of 2006 to 07, 2013 to 14, 2014 to 15, 2015 to 16, 2016 to 17, and 2017 to 18 years, respectively. The analysis of this study revealed that ∼70 % (3.5 mha) of the total kharif crop area remains fallow during the rabi season. The study illustrated that the dynamics in cropping intensity in the coastal region of Odisha was directly influenced by the occurrence of floods during 2013 to 14 and 2014 to 15. Whereas, extreme and moderate drought during 2015 to 16 and 2017 to 18, respectively, reduced the total crop growing area of rabi season as well as cropping intensity especially in middle and western Odisha. The results and findings of this study can be used for policy-level interventions to target improved agricultural practices for improved productivity and crop intensification. The methodology and approach used in this study can be scaled up in similar areas of South and East Asia and Africa to study the dynamics of cropping intensity.

Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China

Ecological restoration programs (ERPs) have been conducted in China since 2000 to improve ecological conditions, particularly in the farming-pastoral ecotone of Northern China. Few have studied the effects of ERPs on landscape structure. Taking West Liaohe River Basin (WLRB) as a case study, we explored how landscape dynamics were altered before and after ERPs from 1990 through 2015 by using multi-temporal Landsat TM images. We analyzed the effects of ERPs on landscape structure by exploring the relationships between landscape features and land cover change (LCC). The results indicate that dramatic changes in land cover and landscape structure occurred before and after ERPs implementation. During 2000–2015 woodlands increased with a sharper annual growth, grasslands reclamation slowed down and was restricted, whereas more croplands were converted to grasslands and woodlands. ERPs decreased landscape fragmentation and increased landscape diversity, due mostly to the portion and spatial configures of croplands, grasslands and woodlands. Landscape fragmentation was significantly correlated with mean patch size of grasslands (r = −0.677, p < 0.0001) and woodlands (r = −0.515, p < 0.0001), as well as patch number ratio of croplands to the sum of grasslands and woodlands (r = −0.414, p < 0.01). Additionally, landscape diversity had a significant negative correlation with the areal ratio of grasslands (r = −0.345, p < 0.001). Our findings indicate that the LCCs were in agreement with ERPs' key goals. The changes in landscape structure in WLRB, however, were not expected from the ERPs design. Given the importance of landscape structure in human vulnerability to environment, it seemed that EPRs from the central government should not only regulate specific land use but also focus on the health and sustainability of the landscapes. Explicit function-based local landscape management should be taken into account for the future through better design and implementations of ERPs.

Land use/cover change and urban sustainability in a medium-sized city

This study examined the trend in urban growth and vegetation loss and the implication of this on the sustainability of medium-sized cities, using Osogbo, south-west Nigeria as a case. Using multi-temporal Landsat™ images that span 30 years (1986 to 2016), the study employed supervised classification to categorise the land cover into the built-up area, vegetation and water bodies. Findings revealed that the city grew axially along major transportation corridors in the early stage but experienced in-filling, densification and radial outward growth subsequently. The built-up area increased at annual rate of 14.7%, more than the population growth rate of 2.2% while vegetation cover and water bodies recorded an annual change of ‒2.5% and ‒3.5% respectively. Rapid city growth and the resultant land use/cover conversion contribute to the depletion of wetlands and vegetation, thereby constituting a threat to sustainable urban development. Controlled urbanisation is suggested as a panacea to the unsustainable urban expansion which threatens the city's ecological equilibrium.

Evolution Characteristics of Landscape Ecological Risk Patterns in Coastal Zones in Zhejiang Province, China

Analyzing evolution characteristics of landscape ecological risk patterns would help establish ecological risk early warning mechanism, reduce the ecological risk probability and promote coastal landscape pattern optimization. In this study, landscape pattern indices were first calculated by using multitemporal Landsat TM images acquired in years 1990, 2000 and 2010, then landscape ecological risk pattern evolution models and the spatiotemporal evolutions of ecological risk patterns of coastal zones in Zhejiang province, China were constructed and analyzed. The results demonstrate that (1) greater changes have taken place in landscape structures during the two decades, and (2) the areas of cropland, sea, woodland and water surface have decreased significantly, while the areas of built-up land, unused land and aquaculture land have increased. In 1990, coastal landscapes were mainly occupied by extremely low and low ecological risk areas. However, by 2010, the low ecological risk area apparently decreased compared to that in 1990, while extremely high and high ecological risk areas increased significantly, occupying spatial regions where originally the extremely low and low ecological risk zones located. Transition rates of ecological risk grades in the second ten years (2000–2010) were significantly lower than in the first ten years (1990–2000). The result suggests that people who originally urged to accelerate coastal landscape exploitation and development have started to concern the ecological environment protection and to pursue a mode that economic development and ecological protection are synchronously conducted.

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R2= 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R2= 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas.

Change Analysis of Aboveground Forest Carbon Stocks According to the Land Cover Change Using Multi-Temporal Landsat TM Images and Machine Learning Algorithms

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.

Spatial Model of Deforestation in Jambi Province for The Periode 1990–2011

In the last 2 decades, deforestation had been an international issue due to its effect to climate change. This study describes a spatial modelling for predicting deforestation in Jambi Province. The main study objective was to find out the best spatial model for predicting deforestation by considering the spatial contexts. The main data used for the analysis were multitemporal Landsat TM images acquired in 1990, 2000, and 2011, the existing land cover maps published by the Ministry of Forestry, statistical data and ground truth. Prior to any other analyses, all districts within the study area were classified into 2 typologies, i.e., low-rate and high-rate deforestation districs on the basis of social and economic factors by using clustering approaches. The spatial models of deforestation were developed by using least-square methods. The study found that the spatial model of deforestation for low-rate deforestation area is Logit (Deforestation) = -2.7046 – 0.000397*JH90(distance from forest edge) + 0.000002*JJ(distance from road) – 0.000111*JKBN90 (distance from estate crop edge) + 0.000096 *JP90(distance from agricultural crop edge) + 0.044227*PDK90(population density) + 0.148187 *E(elevation) – 0.131178*S(slope); while for the high-speed deforestation area is Logit (Deforestation) = 9.1727 – 0.000788*JH90(distance from forest edge) – 0.000065 *JJ(distance from road) – 0.000091*JKBN90(distance from estate crop edge) + 0.000005 *JP90(distance from agricultural crop edge) – 0.070372*PDK90(population density) + 11.268539*E(elevation) – 1.495198*S(slope). The low-rate and high-rate deforestation models had relatively good ROC (Relative Operating Characteristics) values of 91.32% and 99.08%, respectively. The study concludes that the deforestation rate was significantly affected by accessibility (distance from forest edge, distance from estate crop edge, edge from agricultural land), biophysical condition (elevation and slope) as well as population density.

Investigating Sprawl Along China’s Urban Fringe from a Spatio-Temporal Perspective

Compared to the urban sprawl in western countries, China’s version varies, although it retains a dispersed development pattern, which is an important feature. A new framework is provided in this paper to allow for a deeper understanding of China’s spatio-temporal sprawl patterns along its urban fringes. In this framework, Shannon’s entropy analysis and discontinuity index have been employed to quantify the spatial patterns of urban sprawl. Furthermore, a new approach, Spatial Change Frequency Analysis (SCFA), has been proposed in this paper to analyze the temporal patterns of urban sprawl related to individual pixel based on multi-temporal Landsat TM images. With two indicators, namely Spatial Change Frequency (SCF) and Spatial Change Intensity (SCI), this approach appears to effectively reveal the abnormal land use changes in the urban fringes. Jiangning District of Nanjing City, Jiangsu Province, a fast growing urban area, was selected as the study area due to its unique characteristics of urban fringes. The results indicate that the effectiveness of urban land-use planning and implementation of land-use planning policies play important roles in mitigating the negative effects of urban sprawl. Finally, some suggestions are proffered to support sustainable development at the urban fringes of Nanjing City.

Geo-spatial approach for soil salinity mapping in Sego Irrigation Farm, South Ethiopia

Soil salinization is a major problem affecting productivity of irrigated lands in arid and semi-arid areas. Managing salinity to minimize environmental impact is a prerequisite for sustainable irrigated agriculture. The objective of this study was to assess the level of salinity in Sego irrigated farm, Ethiopia and to map temporal and spatial distribution of salt affected soils to support management programmes. The study employed normal image classification, and developing models from ECe vs NDSI and thematic layers to map soil salinity, using geospatial tools. Multi-temporal Landsat TM images of 1984, 1995 and 2010 were used to detect salinity affected areas in the study area. Results of the study revealed that around 6ha of the area gets moderately to strongly salinized per year. Empirical model developed from ECe vs NDSI of 2010 image using regression analysis revealed the coefficient of relation as 66%. The model was extended for the whole area, and revealed that 2.0% of the study area was strongly saline and 54.7% of the area was non-saline. Overlay model developed from water table, land form, land management type and land-cover has revealed that 11.2% of the study area was non-saline, whereas 47.2% and 3.0% was moderately and strongly saline, respectively. Validation of the models was made to test their predication capability and the overlay model has revealed better correlation coefficient of 67.3% to the measured ECe. Most of the salt affected areas were on shallow water table. Cambisols and fluvisols were greatly affected by salinity. The results indicate that long-term irrigation activities would affect agricultural potentiality of the area in the future, and geospatial tools are efficient and feasible for detecting salt affected areas from satellite images and thematic maps.

基于LSMM与MSPA的深圳市绿色景观连通性研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于LSMM与MSPA的深圳市绿色景观连通性研究 DOI: 10.5846/stxb201306101563 作者: 作者单位: 北京市海淀区房屋管理局,中国地质大学(北京) 土地科学技术学院,中国地质大学(北京) 土地科学技术学院,中国地质大学(北京) 土地科学技术学院,中国地质大学(北京) 土地科学技术学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金青年基金(41101175);国家自然科学面上基金(41171440)资助 Landscape connectivity dynamics of urban green landscape based on morphological spatial pattern analysis (MSPA) and linear spectral mixture model (LSMM) in Shenzhen Author: Affiliation: Haidian Real Estate Management Bureau, Beijing,,School of Land Science and Technology, China University of Geosciences (Beijing),, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于线性光谱混合模型(LSMM, Linear Spectral Mixture Model),引入形态学空间格局分析(MSPA,Morphological Spatial Pattern Analysis)进行城市地域绿色景观连通性评价。根据城市绿色景观特点和MSPA方法中的7种连通性类型的涵义,定义了城市绿色景观连通性功能类型。以深圳市1986年、1995年、2000年、2005年及2010年五期Landsat TM影像为数据源,应用线性光谱混合模型提取植被覆盖率,得到深圳市植被覆盖图。在此基础上,提取出高、全植被覆盖作为目标像元进行MSPA处理,分析植被覆盖状况与绿色景观功能类型的时序总体特征及空间梯度动态。结果表明:深圳市绿色景观破碎程度较高,表现为对结构连通性贡献最小的斑块类型总数最大。城市内部东西部连通性呈现出不同变化的趋势;右侧外圈层的大鹏半岛结构连通性最佳;在同一城市化发展梯度上,东部的样带连通性水平比西部要好。在城市化过程中,深圳市高、全覆被植被像元连通性大小受以下因素的影响:城市化程度,地形因素及区域定位。在同一城市化程度上,地形因素对景观连通性的影响较大。从整体的时间变化和空间梯度动态分析可知,在快速城市化过程中植被覆盖率和连通性功能均下降,而到稳定城市化阶段植被覆盖率和连通性均得到改善。研究表明线性光谱混合模型与形态学空间格局分析相结合可以较好的表征城市绿色景观连通性类型时空分布特征,进而明晰城市化过程与区域内绿色景观数量及连通性动态变化关系。 Abstract:Urban area is the main environment where human are living. The stability of internal ecosystem of a city is highly relevant with its sustainable development. Besides, the connectivity of urban green landscapes is a symbol of the integrity and stability of regional ecological functions. Urbanization has brought a dramatic transformation to urban landscape connectivity. The research on the dynamic changes of landscape connectivity is not only significant to the stability of an urban ecosystem, but also provides a basis for regional biodiversity conservation, urban planning, and land use planning and management. However, current connectivity indicators have obvious limitations, for example, indices over different landscape patterns may show familiar values; graph theory requires a human interpretation because of redundancy data, and research on large-scale landscape may cause data extinction during processing. In this paper, Linear Spectral Mixture Model (LSMM) was integrated into Morphological Spatial Pattern Analysis (MSPA) to evaluate the spatial and temporal dynamics of green landscape connectivity in Shenzhen. According to the urban landscape characteristics and MSPA theory, 7 types of connectivity were defined for urban green landscapes, and then the change features among different urbanization gradients were analyzed. We defined urbanization gradients as three different circle layers based on urbanization density, with a decrease of dense values from the first circle to the third. The main steps followed: 1) LSMM was applied to extract the vegetation coverage information from multi-temporal Landsat TM images. On that, the high and full covered vegetation pixels were defined as the foreground pixels (green landscape) in MSPA approach. 2) 7 types of connectivity were utilized to reveal the temporal and spatial variations of green landscapes in the process of urbanization. The results demonstrated that: 1) over 27% of green landscapes in Shenzhen did not contribute to connectivity during 24a. 2) The transition matrix of connectivity-pattern categories from 1986 to 2010 indicated that the connectivity areas were sharply fluctuated during 24a, and the majority of classes changed into non-green landscapes. Except for the core category, the areas of other connectivity categories showed an upward trend. The connectivity of internal urban landscapes showed different trends between eastern part and western part, and, the Dapeng Peninsula in the third circle showed the best connectivity among the whole city. The peak interval of connected categories showed that the eastern part of Shenzhen had more connectivity providers than the western part. 3) The overall connectivity of Shenzhen's green landscapes followed a change of "decrease-increase" in sequence. Comparing with Shenzhen's urbanization process, it is proved that the quantity and connectivity of green landscapes were affected by the following factors: urbanization level, topographic factor and regional policy. Additionally, it is found that the topographic factor had the greatest influence within the same urbanization level. The results from temporal variations and spatial gradients demonstrated that both vegetation coverage and connectivity showed a downward trend in rapid urbanization process, while the two have been improved in steady urbanization stage. The experimental results prove that the jointly analytical framework is efficiently applied to reveal the spatial and temporal dynamics of connectivity characteristics for urban green landscapes during the process of urbanization. Furthermore it enables us to know the relationship between urbanization and urban green landscape connectivity. This research can be applied in practice and provide benefits for monitoring urban green landscapes. 参考文献 相似文献 引证文献

Modeling and Mapping of Urban Sprawl Pattern in Cairo Using Multi-Temporal Landsat Images, and Shannon’s Entropy

Cairo city, being the Egypt’s industrial and cultural center, has a problem of rapid urban sprawl. The city has an extremely high population density which is continuously increasing through informal settlements that grow by sprawling due to migration from the Nile Delta villages and the high population growth rates. The present study attempts to understand, detect and quantify the spatial pattern of Cairo’s urban sprawl using Shannon’s entropy and multi-temporal Landsat TM and ETM images acquired for the period from 1984 to 2013. Supervised classification was applied to extract the built-up areas and to measure the changes in the urban land-use class among the city wards. Shannon’s entropy was applied to model the city’s urban sprawl, trend and spatial change. The entropy values for the city’s electoral wards were modeled and used in an interpolation function to create an entropy surface (index) for each acquired temporal image. Such index indicates the spatial pattern of the urban sprawl and provides a visual comparison of the entropy phenomenon in such wards. Results indicate that Shannon’s entropy index increased from (1.4615) in year 1984 to (2.1023) in year 2013, indicating more dispersed urban growth, a sign of urban sprawl. The maximum entropy values are found in the eastern wards namely El Nozha, Awal Nasr District, Thany Nasr-District, El Salam, El Marg and El Bassatein. A regression analysis was carried for the population growth rate and the built-up areas. Findings help in understanding the sprawl patterns and dynamics among Cairo’s electoral wards and provide a visual comparison. The applied methodology provides explanations and facilitates tracing and measuring the urban sprawl which is needed by decision makers and city planners of mega cities.

The Use of Remote Sensing Technique to Predict Gross Domestic Product (GDP): An Analysis of Built-Up Index and GDP in Nine Major Cities in Canada

Abstract. City/regional authorities are responsible to design and structure the urban morphology based on the desired land-use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as Gross Domestic Product (GDP). In Canada, the gross national income mainly relies on mining and manufacturing industries. In order to facilitate new city development, this study aims to utilize remote sensing and GIS techniques to assess the relationship between the industrial area and the reported GDP in nine major cities in Canada. Free archive multi-temporal Landsat TM images and land use vector data were obtained for year 2005 and 2010 during the summer season, where the socio-economic data, such as GDP, population, and total employment are obtained from Metropolitan Housing Outlook for the same duration. The Landsat TM images were first atmospherically corrected and the built-up values were computed using the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) from the Landsat images. The high built-up values within the industrial areas were acquired for further analysis. Finally, a correlation analysis was conducted between the GDP, Population, and Total Employment with respect to the built-up areas. Preliminary findings show that the R2 between the percentage of built-up areas and industrial area within the corresponding city is 0.82. In addition, the R2 between the built-up areas and GDP ranges from 0.73 to 0.78. Consistent findings are observed in the similar correlation between the built-up areas and population, as well as the built-up areas and the employment, where the R2 is within 0.72 to 0.73. With the correlation found, we believe that results can be used as a generic indication for the federal/municipals authorities, which are aiming or target for a specific GDP with respect to the planned industrial area.

Shoreline mapping with cellular automata and the shoreline progradation analysis in Shanghai, China from 1979 to 2008

This paper presents a cellular automata (CA) algorithm to extract shorelines from remote sensing images by analysing the edge directional information of the images. Using this algorithm, the tide-coordinated shorelines along the entire coast in Shanghai Municipality of China were extracted and analysed using the multi-temporal Landsat TM images from 1979 to 2008. The shorelines of four sub-regions, including the mainland and three islands (Changxing, Hengsha and Chongming) were analysed along with six areas experiencing drastic shoreline changes. The results show a total progradation of 551.7 km2 along the coastal area of Shanghai over the past 30 years, due to both long-term sediment deposition and short-term land reclamation. Furthermore, both horizontal and vertical displacements along the shorelines were identified. Fractal analyses between the length of the shorelines and the spatial resolution of the images achieved goodness-of-fit (R2) values above 0.6 for the shorelines of the entire Shanghai as well as for each of the four subregions, indicating that the relationship between the length of the shorelines and the spatial resolution of the images accord with the power laws. The fractal dimension values indicate that the shorelines of both Changxing and Chongming Islands were getting regular. The paper also demonstrates that the CA-based shoreline extractor can detect shoreline information of both artificial and muddy coasts from remote sensing images. The shoreline extraction and change analysis tool is valuable not only for shoreline mapping but also for comprehensive coastal management.

Normalization of time series DMSP-OLS nighttime light images for urban growth analysis with Pseudo Invariant Features

Previous studies demonstrated that DMSP-OLS stable nighttime light data are useful data source for delineating urban areas. However, the nighttime light data acquired in different years are not directly comparable, due to the variations in atmospheric condition from year to year and the periodic change in satellite sensor. This makes it difficult to use the time series nighttime light data for urban growth analysis. This paper presents a novel technique for normalizing time series DMSP/OLS nighttime light data and deriving urban detection threshold using Pseudo Invariant Features (PIFs). Our technique consists of three steps: (1) estimate an optimal threshold value for urban area detection for a reference year, when high resolution image data are available for some local areas. (2) Based on the irreversible nature of urbanization process, determine a set of PIFs, which are deemed as urban areas and did not exhibit significant change in nighttime light condition during the study period. (3) Normalize the time series DMSP-OLS data sets based on the PIFs and linear regression, determine optimal threshold values for urban area detection for all years based on the reference year threshold value, and extract urban areas accordingly. This technique was successfully applied to time series DMSP-OLS nighttime light images of the Central Liaoning region in China. Patterns of this urban agglomeration's spatial–temporal evolution from 2000 to 2010 were mapped and analyzed. The reliability and spatial accuracy of this technique were evaluated with multitemporal Landsat TM images. The technique was proved accurate and effective.

Object-based identification of vegetation cover decline in irrigated agro-ecosystems in Uzbekistan

The sustainability of irrigated croplands in Central Asia is threatened by degradation of their productive function. To quantify the extent of the cropland degradation, this paper combines object-based change detection and spectral mixture analysis for vegetation cover decline mapping in irrigated agro-ecosystems in Uzbekistan based on multitemporal Landsat TM images from 1998 to 2009. The results of the change detection reveal that the cadastral field parcels, characterized by vegetation cover decrease, occupied 18% (52,938 ha) of the cropland area. Spatial distribution of fields with decreased vegetation cover was mainly associated with abandoned cropland and land with inherently low-fertility soils located on the outreaches of the irrigation system and bordering natural sandy deserts. The comparison with the land degradation map based on trend analyses of 250 m MODIS NDVI time series 2000–2010 yielded an overall agreement of 93%. The proposed approach is a useful supplement to the commonly applied trend analysis for detecting land degradation in cases when plot-specific data are needed but satellite time series of high spatial resolution are not available. The derived parcel-specific, spatial information supports better informed decisions on cropland rehabilitation measures.

Driving forces analysis of reservoir wetland evolution in Beijing during 1984–2010

The reservoir wetland, which is the largest artificial wetland in Beijing, constitutes one of the important urban ecological infrastructures. Considering two elements of natural environment and socio-economy, this paper established the driving factor indexing system of Beijing reservoir wetland evolution. Natural environment driving factors include precipitation, temperature, entry water and groundwater depth; social economic driving factors include resident population, urbanization rate and per capita GDP. Using multi-temporal Landsat TM images from 1984 to 2010 in Beijing, the spatial extent and the distribution of Beijing reservoir wetlands were extracted, and the change of the wetland area about the three decade years were analyzed. Logistic regression model was used to explore for each of the three periods: from 1984 to 1998, from 1998 to 2004 and from 2004 to 2010. The results showed that the leading driving factors and their influences on reservoir wetland evolution were different for each period. During 1984-1998, two natural environment indices: average annual precipitation and entry water index were the major factors driving the increase in wetland area with the contribution rate of Logistic regression being 5.78 and 3.50, respectively, and caused the wetland growth from total area of 104.93 km2 to 219.96 km2. From 1998 to 2004, as the impact of human activities intensified the main driving factors were the number of residents, groundwater depth and urbanization rate with the contribution rate of Logistic regression 9.41, 9.18, and 7.77, respectively, and caused the wetland shrinkage rapidly from the total area of 219.96 km2 to 95.71 km2. During 2004–2010, reservoir wetland evolution was impacted by both natural and socio-economic factors, and the dominant driving factors were urbanization rate and precipitation with the contribution rate of 6.62 and 4.22, respectively, and caused the wetland total area growth slightly to 109.73 km2.

Analyzing dune dynamics at the dune-field scale based on multi-temporal analysis of Landsat-TM images

Studying dune dynamics at the dune-field scale requires intensive fieldwork and a relatively huge dataset. However, this is not always possible with the high cost and the limited availability of data. Here, we present a technique based on the analysis of multi-temporal Landsat-TM images for studying the dynamics of different dune morphologies from five dune-field sites around the world. A pair of Landsat-TM images for each site has been used after performing several steps of image pre-processing and enhancement (i.e. image co-registration, radiometric calibration and histogram equalization). RGB-clustering is a commonly used algorithm for data compression and iso-clustering. In this study, we implemented this technique as an unconventional change-detection algorithm with 32 clustering partitions per band. A bi-temporal layerstack of the near-infrared bands was the input for the RGB-clustering process. RGB-clustering produced thematic maps that gave an immediate insight into the dune dynamics at the dune-field scale given the availability of Landsat images. The technique gave the best results with dunes developed under uni-directional wind pattern. In addition, it was also easier to detect migration of simple dunes than in compound or complex dunes. These maps can be used as a bi-temporal digital atlas of large dune-systems and help in defining areas for detailed field measurements on dune dynamics and migration. Moreover, it can be implemented in analyzing aeolian systems where field measurements are practically impossible (i.e. large and extra-terrestrial aeolian systems).

THE APPLICATION OF THE KNOWLEDGE-BASED DECISION TREE CLASSIFICATION METHOD TO THE EXTRACTION OF LAND TYPES IN MINING AREAS: A CASE STUDY OF DAYE AREA, HUBEI PROVINCE

Simple and clear, the knowledge-based decision tree classification method can choose the best band composition and characteristic parameters of different ground objects so as to get the highly accurate classification results. Based on the spectral characteristics and the spatial knowledge of the ground objects in Daye which served as a training area, the authors used image composite, iron oxide index, normalized difference vegetation index (NDVI) and digital elevation model and employed the decision tree classification method with multitemporal Landsat TM (ETM) images. The classification algorithm was applied to all the Landsat TM (ETM) data so as to detect temporal and spatial changes in the mining areas, which, in turn, were divided into ten classes. The characteristics of the highly accurate classification results enable us to perform highly accurate change detection and quantitative analysis of such features in different mining areas as waste,water bodies, change of land use, reclamation process and estimation of vegetation cover in affected places. From the change detection results, it is observed that the decreasing vegetation and land degradation caused by mining activities in the study area are serious, and that only about 35% of the abandoned mining area was reclaimed from 1986 to 2002.