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1.
An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.  相似文献   

2.
Identifying effective vegetation biophysical and spectral parameters for investigating light to moderate grazing effects on grasslands improves management practices on grasslands. Using mixed grasslands as a case study, this paper compares responses of vegetation biophysical properties and spectral parameters derived from satellite images to grazing intensity, and identifies the suitable biophysical and spectral parameters to detect grazing effects in these areas. Biophysical properties including cover, canopy height and Leaf area index (LAI) were measured in three sites with different grazing managements and one benchmark site in 2008 and 2009 in Grasslands PlaceTypeNational Park and surrounding provincial pastures, Canada. Thirteen vegetation spectral indices, calculated by statistically combining different spectral information, were evaluated. The results indicate that canopy height and the ratio of photosynthetically active vegetation cover to non-photosynthetically active vegetation cover (PV/NPV) showed significant differences between ungrazed and grazed sites. All spectral vegetation indices except the canopy index (CI) show significant differences between grazing treatments. Red-Near infrared (Red-NIR) based vegetation indices, such as Modified Triangular Vegetation Index 1 (MTVI1), Soil-adjusted Vegetation Index (SAVI), are significantly correlated to the PV/NPV. Green/Mid-infrared (Green/MIR) related vegetation indices, i.e. Plant Senescence Reflectance Index (PRSI) and Normalized Canopy Index (NCI), show significant correlation with canopy height. Models based on a linear combination of MTVI1 and SAVI were developed for PV/NPV and PRSI and NCI for canopy height. Models that simulated PV/NPV and canopy height show significant correlations with grazing intensity, suggesting the feasibility of remote sensing to quantify light to moderate grazing effects in mixed grasslands.  相似文献   

3.
The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R 2 value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.  相似文献   

4.
The rapid development of cities in developing countries results in deteriorating of agricultural lands. The majority of these agricultural lands are converted to urban areas, which affects the ecosystems. In this research, an integrated model of Markov chain and cellular automata models was applied to simulate urban land use changes and to predict their spatial patterns in Tripoli metropolitan area, Libya. It is worth mentioning that there is not much research has been done about land use/cover change in Libyan cities. In this study, the performance of integrated CA–Markov model was assessed. Firstly, the Markov chain model was used to simulate and predict the land use change quantitatively; then, the CA model was applied to simulate the dynamic spatial patterns of changes explicitly. The urban land use change from 1984 to 2010 was modelled using the CA–Markov model for calibration to compute optimal transition rules and to predict future land use change. In validation process, the model was validated using Kappa index statistics which resulted in overall accuracy more than 85 %. Finally, based on transition rules and transition area matrix produced from calibration process, the future land use changes of 2020 and 2025 were predicted and mapped. The findings of this research showed reasonably good performance of employed model. The model results demonstrate that the study area is growing very rapidly especially in the recent decade. Furthermore, this rapid urban expansion results in remarkable continuous decrease of agriculture lands.  相似文献   

5.
Mangrove forest stores large organic carbon stocks in a setting that is highly vulnerable to climate change and direct anthropogenic influences. As such there is a need to elucidate the causes and consequences of land use change on these ecosystems that have high value in terms of ecosystem services. We examine the areal pattern of land types in a coastal region located in southern Iran over a period of 14 years to predict future loss and gain in land types to the year 2025. We applied a CA–Markov model to simulate and predict mangrove forest change. Landsat satellite images from 2000 to 2014 were used to analyze the land cover changes between soil, open water and mangroves. Major changes during this period were observed in soil and water which could be attributed to rising sea level. Furthermore, the mangrove area in the more seaward position was converted to open water due to sea-level rise. A cellular automata model was then used to predict the land cover changes that would occur by the year 2025. Results demonstrated that approximately 21 ha of mangrove area will be converted to open water, while mangroves are projected to expand by approximately 28 ha in landward direction. These changes need to be delineated to better inform precise mitigation and adaptation measures.  相似文献   

6.
Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the Zagros region. Yet, areas vulnerable to forest conversion are unknown. This study aims to predict the spatial distribution of deforestation in western Iran. Landsat images dated 1988, 2001, and 2007 are classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. Meanwhile, in order to examine deforestation factors’ investigation, deforestation maps with physiographic and human spatial variables are entered into the model. Areas vulnerable to forest changes in the Zagros forest region are predicted by a multilayer perceptron neural network (MLPNN) with a Markov chain model. The results show that about 19,294 ha forest areas are deforested in the last 19 years. The predictive performance of the model appears successful, which is validated using the actual land cover map of the same year from Landsat data. The validated map is found to be 94 % accurate. The validation is also tested using the relative operating characteristic approach which yielded a value of 0.96. The model is then further extended to predict forest cover losses for 2020. The MLPNN approach was found to have a great potential to predict land use/land cover changes because it permits developing complex, nonlinear models.  相似文献   

7.
The integration of remote sensing, geographic information system, landscape ecology and statistical analysis methods was applied to study the urban thermal environment in Guangzhou. Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-up Index (NDBI), Normalized Difference Barren Index (NDBaI) and Modified Normalized Difference Water Index (MNDWI) were used to analyze the relationships between land surface temperature (LST) and land use/land cover (LULC) qualitatively. The result revealed that, most urban built-up lands were located in the middle part, and high LST areas mostly and were in the middle and southern parts. Therefore, the urbanization and thermal environment in the middle and southern parts need to be determined. Land surface temperature increased with the density of urban built-up and barren land, but decreased with vegetation cover. The relationship between MNDWI and LST was found to be negative, which implied that pure water would decrease the surface temperature and the polluted water would increase the surface temperature. A multiple regression between LST and each indices as well as the elevation was created to elevate the urban thermal environment, which showed that NDVI, NDBI, NDBaI, MNDWI were effective indicators for quantifying LULC impacts on LST.  相似文献   

8.
This study aims to assess the potential of several ancillary input data for the improvement of unsupervised land cover change detection in arid environments. The study area is located in Central Iraq where desertification has been observed. We develop a new scheme based on known robust indices. We employ Landsat (multispectral scanner, thematic mapper, and enhanced thematic mapper) satellite data acquired in 1976, 1990, and 2002. We use the Normalized Deferential Vegetation Index, Normalized Differential Water Index (NDWI), Salinity Index (SI), and Eolian Mapping Index. Two new equations were applied for the SI and the NDWI indices. Validation was performed using ground truth data collected in 16 days. We show that such an approach allows a robust and low-cost alternative for preliminary and large-scale assessments. This study shows that desertification has increased in the study area since 1990.  相似文献   

9.
黔桂岩溶山区土地利用变化的社会经济因素分析   总被引：1，自引：1，他引：0  
基于黔桂岩溶山区1990、2015年的土地利用数据和社会经济统计资料,利用典范对应分析方法对1990-2015年25年间的土地利用变化及社会经济驱动因素进行了实证分析。结论如下:（1）黔桂岩溶山区土地利用变化存在明显的时空分异现象;（2）贵州高原区以草地大幅减少和林地增加为主要特征;黔桂峰丛洼地区以水域面积增加和草地减少为主要特征;广西丘陵区的主要特征是耕地减少和建设用地增加。（3）贵州高原区土地利用类型变化以经济和人口因素为主要驱动力;广西丘陵区土地利用类型变化以经济因素和农业结构水平为主要驱动力;农业因素对黔桂峰丛洼地区的土地利用变化驱动作用明显。未来应提高贵州高原和黔桂峰丛洼地区岩溶山区草地生态功能重要性的认识,在加强森林植被恢复的同时加强草地资源保护;城镇化发展背景下黔桂岩溶山区应减少优质耕地占用,开发低丘缓坡土地,建设山地特色城市。   相似文献   

10.
Land use change quantified for the last 50 years within and near a fast growing agricultural land in Neka River Basin, using geographic information systems. Land cover and land use change was projected for the next decade using topography, geology, land use maps and remote sensing data of the study area. The study explored the relationships between agricultural land growth and landscape changes. The land use changes assessed among the different land cover classes. It is important to mention that conducting of the present study a very severe land cover changes taken place as the result of agricultural land development. These changes in land cover led to the forest degradation of the study area. Relationship between land-use changes and agricultural growth offered a more robust prediction of soil erosion in Neka watershed. This study aims to find the relationships between land use pattern, erosion and the sediment yield in the study area. The land use coefficient has applied in the model of erosion potential method to forecast the effect of the land type to reduce the erosion. The results of this study indicated that the total sediment yield of the study area has notably decreased to 89.24 % after an appropriate land use/cover alteration. The estimated special erosion for the southern Neka Basin is about 144465.1 m3/km2 where after management policy is predicted 15542.9 m3/km2/y. Therefore, the total difference for the study area has estimated about 128922.2 m3/km2/y.  相似文献   

11.
This study characterized and compared changes in vegetation condition in areas with different gradients during the past three decades across the entire Loess Plateau. For this purpose, changes in vegetation type and vegetation coverage at sites with 0 – 15° and >15° slope gradients were determined by analyzing land use data and Normalized Difference Vegetation Index (NDVI) data, respectively. The software Arc/Info 9.3, land use transformation matrix, linear regression analysis, and Mann–Kendall test were used for the data processing and analysis. Policy influences, human impacts, and climate variability were also taken into account to find the reasons for vegetation condition change. The results indicated that the “Grain-For-Green” project achieved initial success. Areas of farmland and grassland changed most extensively, and far greater areas of farmland were transformed into forest and grassland than vice versa. Moreover, the conversion of farmland to forest and grassland mainly occurred in areas where slopes exceeded 15°, while grassland was mainly changed to farmland in areas with gentle slopes. Vegetation coverage on the Loess Plateau exhibited overall increases after the implementation of “Grain-For-Green” project. Regions with sparse vegetation have declined sharply, mostly in steeply sloped areas. Vegetation coverage has increased significantly in most regions, particularly in the parts traversed by the principal sediment source of the Yellow River, which could help to control the severe soil and water losses. However, regions with sparse vegetation on the Loess Plateau still covered 71.1 % of the total area in 2010. Therefore, it is important to further increase vegetation coverage in the future.  相似文献   

12.
Land cover and vegetation in Lake Baikal basin (LBB) are considered to be highly susceptible to climate change. However, there is less information on the change trends in both climate and land cover in LBB and thus less understanding of the watershed sensitivity and adaptability to climate change. Here we identified the spatial and temporal patterns of changes in climate (from 1979 to 2016), land cover, and vegetation (from 2000 to 2010) in the LBB. During the past 40 years, there was a little increase in precipitation while air temperature has increased by 1.4 °C. During the past 10 years, land cover has changed significantly. Herein grassland, water bodies, permanent snow, and ice decreased by 485.40 km2, 161.55 km2 and 2.83 km2, respectively. However, forest and wetland increased by 111.40 km2 and 202.90 km2, respectively. About 83.67 km2 area of water bodies has been converted into the wetland. Also, there was a significant change in Normalized Difference Vegetation Index (NDVI), the NDVI maximum value was 1 in 2000, decreased to 0.9 in 2010. Evidently, it was in the mountainous areas and in the river basin that the vegetation shifted. Our findings have implications for predicting the safety of water resources and water eco-environment in LBB under global change.  相似文献   

13.
发展中的板块边界:天山-贝加尔活动构造带   总被引：5，自引：0，他引：5  
不同的土地利用方式可使土地理化性质产生一系列的变化和差异,从而影响到岩溶作用的方向和强度。通过野外溶蚀试片实验法,对金佛山典型岩溶区碧潭泉和水房泉两泉域岩溶生态系统的5种典型土地利用方式下的土壤溶蚀速率进行雨季短时间尺度变化的野外观测。2006年7月中旬开始,重庆地区罕遇43天高温无雨的特殊天气,测试结果表明不同土地利用方式甚至同一土地利用方式下不同海拔的岩溶区石灰岩试片溶蚀速率都存在较大差异,碧潭泉域雨季绝对溶蚀量仅为水房泉域的13.3%,6个测试点土下溶蚀量由大到小依次为水房泉竹林地、水房泉林地、水房泉草地、碧潭泉林地、碧潭泉灌草丛、碧潭泉耕地。在研究时间内降雨量、温度和土壤CaCO3含量差异的基础上,金佛山两泉域岩溶作用主要有两个控制因素:土壤CO2浓度、土壤有机质。  相似文献   

14.
Zhang  Jiawen  Liesch  Tanja  Chen  Zhao  Goldscheider  Nico 《Hydrogeology Journal》2023,31(5):1197-1208

Karst areas contain valuable groundwater resources and high biodiversity, but are particularly vulnerable to climate change and human impacts. Land-use change is the cause and consequence of global environmental change. The releases of the Climate Change Initiative-Land Cover (CCI-LC) and World Karst Aquifer Map (WOKAM) datasets have made it possible to explore global land-use changes in karst areas. This paper firstly analyses the global karst land-use distribution in 2020, as well as the land-use transition characteristics between 1992 and 2020. Then, two indicators, proportion of land-use change and dominant type of land-use change, are proposed to identify the spatial characteristics of land-use change in global karst areas. Finally, three examples of land-use change in karst areas are analyzed in detail. Land-use types and proportions of the global karst areas from large to small are as follows: forest (31.78%), bare area (27.58%), cropland (19.02%), grassland (10.87%), shrubland (7.21%), wetland (1.67%), ice and snow (1.16%) and urban (0.71%). The total area of global karst land-use change is 1.30 million km2, about 4.85% of global karst surface. The land-use change trend of global karst is dominated by afforestation, supplemented by scattered urbanization and agricultural reclamation. The tropical climate has a higher intensity of land-use change. Regions of agricultural reclamation are highly consistent with the population density. These results reflect the impact of human activities and climate change on land-use changes in global karst areas, and serve as a basis for further research and planning of land resource management.

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15.
Land degradation reduces the ability of the land to perform many biophysical and chemical functions. The main aim of this study was to determine the status of land degradation in the Budgam area of Kashmir Himalaya using remote sensing and geographic information system. The satellite data together with other geospatial datasets were used to quantify different categories of land degradation. The results were validated in the field and an accuracy of 85% was observed. Land use/land cover of the study area was determined in order to know the effect of land use on the rate of land degradation. Normalized differential vegetation index (NDVI) and slope of the area were determined using LANDSAT-enhanced thematic mapper plus (ETM+) data, advanced space borne thermal emission and reflection radiometer, and digital elevation model along with other secondary data were analysed to create various thematic maps, viz., land use/land cover, geology, NDVI and slopes used in modelling land degradation in the Kashmir Himalayan region. The vegetation condition, elevation and land use/land cover information of the area were integrated to assess the land degradation scenario in the area using the ArcGIS ‘Spatial Analyst Module’. The results reveal that about 13.19% of the study area has undergone moderate to high degradation, whereas about 44.12% of the area has undergone slight degradation.  相似文献   

16.
为了解森林退化的原因,利用2000-2015年的MODIS NDVI数据,在分析贵州省植被变化趋势的基础上识别了归一化植被指数（NDVI)显著下降的区域,并在NDVI显著下降区选取面积大于10 km2的森林图斑为兴趣区,分析其内气候变化趋势及对森林NDVI值的影响。研究表明:197个兴趣区主要分布在贵州省西北部的赤水—习水、东北部的梵净山和东南部的非喀斯特区域;区内春、夏季NDVI变化趋势与年NDVI值变化趋势一致,下降速率达到-0.01·yr-1,冬季与其他季节变化趋势相反,呈不显著升高趋势;区内春季和夏季气温升高显著,降水和日照时间无明显变化,整体气候变化呈暖干趋势;夏季温度升高是NDVI降低的主要驱动因素。   相似文献   

17.
内蒙古中部地区土地荒漠化遥感调查及环境质量评价   总被引：6，自引：2，他引：4  
选择内蒙古中部地区作为典型荒漠化区域,根据荒漠化土地分类体系,确定决策树的结构以及各类地物在树形中的位置。基于各类地物的光谱反射特性和图像数据反映的综合特征,采取相应的识别和提取方法,以最大限度地利用遥感数据源。在研究过程中,采用从多光谱及多时相TM和ETM+遥感数据中提取出的光谱反射特性、土壤调节植被指数、土壤亮度指数以及土壤湿度指数等复合指数进行不同地物的分类和提取。结果表明:利用决策树分层提取法可以有效地排除和避免提取地物时多余信息的干扰及影响,目标明确。然后,通过图像代数检测法及目视解译法确定1990-2000年土地荒漠化的变化信息,对内蒙古中部地区的荒漠化演变规律做出分析。最后,结合研究区气候数据,在GIS支持下,通过对研究区生态环境质量变化和气候因子变化的相关性分析,表明研究区影响环境变化的主要因子是降水量与蒸发量(干燥度)。  相似文献   

18.
Land use/land cover change is a global phenomenon which reflects natural resources degradation and/or utilization. Remote sensing and GIS have been widely used to monitor such changes at watershed level. The present study evaluates the LU/LC change during 1989 - 2001 in a semi-arid watershed of central India. Geocoded satellite data of 1989 and 2001 on 1:50,000 scale, were visually interpreted to prepare thematic maps which were later digitized using ArcGIS softwares. The analysis shows that vast tracts of cultivated land have become uncultivated and at some places even converted to wasteland. However, the land under dense forest and open forest has decreased due to expansion of built-up land and other anthropogenic activities. Increase in area of uncultivated land, wasteland and decrease in cultivated land and open scrub is also supported by rainfall analysis, which shows a declining trend and a fall of 186.93 mm in average annual rainfall for 1986-2003 period. The change detection map prepared using land use/land cover of 1989 and 2001 as inputs shows that out of the total geographical area of the watershed, 25.78% of the watershed area has seen a change from one land use category to another, however rest 74.22% has remained unchanged.  相似文献   

19.
Gridded population distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate population distribution data. The effectiveness of the population distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving population distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded population datasets across large areas.  相似文献   

20.
Sajjad  Asif  Lu  Jianzhong  Chen  Xiaoling  Chisenga  Chikondi  Mazhar  Nausheen  Nadeem  Basit 《Natural Hazards》2022,110(3):2207-2226

The Multan district is mainly prone to riverine floods but has remained understudied. Chenab flood-2014 was the worst flood that this district experienced in recorded history. This study applies remote sensing (RS) techniques to estimate the extent, calculate duration, assess the major causes and resulting impacts of the flood-2014, using Landsat-8 OLI images. These images were obtained for pre-flood, during-flood and post-flood instances. Secondary data of flood causing factors were obtained for comprehensive analysis. Spatially trained and validated datasets were obtained through Google Earth platform and Global positioning system. The supervised classification with maximum likelihood algorithm was used to classify land use and land cover of the study area. The Modified Normalized Difference Water Index was utilized to detect flood inundation extent and duration, and Normalized Difference Vegetation Index was utilized to monitor vegetation coverage and changes. The analysis allowed us to assess flood causes, and calculate the extent of the flooded areas with duration and recession, as well as damages to standing crops and built-up areas. The results revealed that the flood-2014 occurred due to heavy rains in early September in upper Chenab catchment. The flood inundation continued for around two months, which heavily affected agriculture and built-up areas. The present study introduces practical use of RS techniques to provide basis for effective flood inundation mapping and impact assessment, as an application for early flood response and recovery in the world.

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