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1.
Fuel type mapping of the wildland-urban interface (WUI) in support of fire spread simulation modelling should include both natural and urban features. The objective of this study was to evaluate the utility of (1) Light Detection and Ranging (LiDAR) structural data, (2) ortho-image data and (3) a combination of both as input to an object-based classification approach for mapping fuels within two WUI areas in San Diego, California. A separability analysis was utilized to determine the surface topographical and spectral layers most influential for discriminating WUI fuels. An accuracy assessment revealed that the combination of LiDAR and ortho-image data inputs substantially increased classification accuracy by 20–30% and achieved overall accuracies?>?80%. Results from the study provide knowledge on how reliable fuel types within the WUI can be mapped using high-resolution LiDAR and ortho-image data while presenting new insights into fuel type mapping. 相似文献
2.
A. V. Suresh Babu V. Venkateshwar Rao I. V. Muralikrishna 《Journal of the Indian Society of Remote Sensing》2007,35(1):81-91
Water Utilisation Index (WUI) defined as area irrigated per unit volume is a measure of water delivery performance and constitutes
one of the important spatial performance indicators of an irrigation system. WUI also forms basis for evaluating the adequacy
of seasonal irrigation supplies in an irrigation system (inverse of WUI is delta, i.e. depth of water supplied to a given
irrigation unit). In the present study WUI and adequacy indicators were used in benchmarking the performance of Nagarjunasagar
Left Canal Command (NSLC) in Andhra Pradesh. Optimised temporal satellite data of rabi season during the years 1990–91 and
1998–99 was used in deriving irrigated crop areas adopting hierarchical classification approach. Paddy is the predominant
crop grown and cotton, chillies, sugarcane etc. are the other crops grown in the study area. Equivalent wet area (paddy crop
area) was estimated using the operationally used project specific conversion factors. WUI was estimated at disaggregated level
viz., distributary, irrigation block, irrigation zone level using the canal discharge data. At project level, WUI estimated
to be 65 ha/MCM and 92 ha/MCM during rabi season of 1990–91 and 1998–99 years respectively. A comparison of total irrigated
area and discharges corresponding to both the years indicate that irrigation service is extensive and sub optimal during 1998–99
and it is intensive and optimal in 1990–91. It was also observed that WUI is lesser in blocks of with higher Culturable Command
Area (CCA) compared to the blocks of lower CCA. All the disaggregated units were ranked into various groups of different levels
of water distribution performance. The study demonstrates the utility of WUI as spatial performance indicator and thus useful
for benchmarking studies of irrigation command areas. The WUI together with satellite data derived spatial irrigation intensity,
crop productivity constitutes important benchmarking indices in irrigation command areas. 相似文献
3.
Land cover mapping forms a reference base for resource managers in their decision-making processes to guide rural/urban growth and management of natural resources. The aim of this study was to map land cover dynamics within the Upper Shire River catchment, Malawi. The article promotes innovation of automated land cover mapping based on remote sensing information to generate data products that are both appropriate to, and usable within different scientific applications in developing countries such as Malawi. To determine land cover dynamics, 1989 and 2002 Landsat images were used. Image bands were combined in transformations and indices with physical meaning; together with spatial data, to enhance classification accuracy. A maximum likelihood classification for each image was computed for identification of land cover variables. The results showed that the combination of spatial and digital data enhanced classification accuracy and the ability to categorise land cover features, which are relatively inhomogeneous. 相似文献
4.
Natural and semi-natural landscape cover is heterogeneous. Ideally, mapping land cover requires an approach that represents both gradients and land covers spatiotemporal variability. These aspects can be visualized and depicted by applying a new spatio-temporal analysis based Landscape Heterogeneity Mapping (LaHMa) method to natural and semi-natural landscapes. Using MODIS NDVI 16-day imagery (February 2000–July 2009) for Crete, a 65-cluster image was selected from ISODATA classification results using the separability values of the divergence statistics. The 65 clusters appropriately generalize the spatial and temporal variability in land cover. Using classified outputs from 10 to 65 clusters, the frequency of pixels identified as boundaries of homogeneous land cover classes was translated into the form of a landscape heterogeneity map, which was then validated using field data. The results show that the heterogeneity map had moderate correlation (R2 = 0.60 and 0.63 in two transects) with the sum of differences between neighbouring transect pixels in all land cover components. In general, the study found this new approach (LaHMa) to be suitable for mapping landscape heterogeneity in the natural and semi-natural landscape of Crete, Greece. The new method appears to be of potential use for informing gradient analyses in landscape ecological studies. 相似文献
5.
本论文尝试讨论两个主题：主题一为利用主成分分析PCA方法应用于像元阶层资料融合技术的研究。主题二为应用Dempster-Shafer evidence theory方法于特征阶层数据融合技术的研究。在第一个主题中，由于合成孔径雷达的数据具有全偏极特性，在此选取了对植被较为敏感的HV极化合成孔径雷达数据，与具有光谱特性的光学SPOT数据做数据融合处理以利接下来的地物分类。首先，本研究利用小波转换技术来滤除合成孔径雷达斑驳噪声，在接下来融合步骤中，主成分分析出来的第一部分（PCI）是用做完滤除噪声后的合成孔径雷达取代，在数据融合后，进行地物分类是采用最大似然法来分类融合影像。在第二个主题中，利用全偏极雷达数据的极化特性结合SPOT数据的光谱特性，其主要目的是为了增加分类的精确度。首先使用李式滤波器滤除全偏极雷达数据噪声，接下来同样是使用采用最大似然法来分类融合影像，（不同的在于全偏极雷达影像使用Wishart几率分布，在光学影像采用multivariate Gaussian几率分布）将每个类别中每个像元属于某个类别的几率值计算出来，再利用Dempster-Shafer evidence theory来结合这些类别的机率值。最后产生出一张新的分类影像。实验的结果显示分类的精确度比较于未融合的资料都有明显提升的效果，也证明了此两个数据融合方法对于不同数据特性的融合都是很成功的。 相似文献
6.
土地覆被作为地表自然和人工建造物的综合体,是开展土地科学相关研究的重要基础,在遥感大数据背景下,准确、快速、自动化进行土地覆被提取技术一直是遥感研究中的重点。本文基于eCognition软件,采用面向对象的多尺度分割法,综合考虑地物在遥感影像上的光谱、形状和纹理特征,建立多种地物提取规则。通过模糊函数、支持向量机（SVM）和阈值法对研究区的土地覆被进行分类提取,并与研究区的FROM-GLC10数据和土地利用变更数据进行了对比分析。结果表明：①研究区土地覆被分类的总体精度为97%,Kappa系数为0.96,分类精度较高;②基于10 m分辨率影像,综合使用形状、纹理、光谱信息对于道路的提取具有较好的效果,道路提取Kappa系数为0.84;③分类结果在面积和空间分布上都优于FROM-GLC10数据,与研究区实际土地变更数据保持较好的一致性。基于面向对象与规则的分类方法提取地物能够有效利用多种遥感影像特征,分类精度高,对于处理高分辨率遥感数据具有很好的优势。 相似文献
7.
Multitemporal land cover classification over urban areas is challenging, especially when using heterogeneous data sources with variable quality attributes. A prominent challenge is that classes with similar spectral signatures (such as trees and grass) tend to be confused with one another. In this paper, we evaluate the efficacy of image point cloud (IPC) data combined with suitable Bayesian analysis based time-series rectification techniques to improve the classification accuracy in a multitemporal context. The proposed method uses hidden Markov models (HMMs) to rectify land covers that are initially classified by a random forest (RF) algorithm. This land cover classification method is tested using time series of remote sensing data from a heterogeneous and rapidly changing urban landscape (Kuopio city, Finland) observed from 2006 to 2014. The data consisted of aerial images (5 years), Landsat data (all 9 years) and airborne laser scanning data (1 year). The results of the study demonstrate that the addition of three-dimensional image point cloud data derived from aerial stereo images as predictor variables improved overall classification accuracy, around three percentage points. Additionally, HMM-based post processing reduces significantly the number of spurious year-to-year changes. Using a set of 240 validation points, we estimated that this step improved overall classification accuracy by around 3.0 percentage points, and up to 6 to 10 percentage points for some classes. The overall accuracy of the final product was 91% (kappa = 0.88). Our analysis shows that around 1.9% of the area around Kuopio city, representing a total area of approximately 0.61 km2, experienced changes in land cover over the nine years considered. 相似文献
8.
Mahendra S. Lodhi Prasanna K. Samal Shivaji Chaudhry L. M. S. Palni P. P. Dhyani 《Journal of the Indian Society of Remote Sensing》2014,42(2):461-467
Since last few decades RS-GIS is playing vital role in studying and mapping spatiotemporal responses of land cover, however, as a matter of fact, the mapping outputs largely depend on the expert's/user's preferences because location specific and people specific land cover classification systems are adopted autonomously for image classification in GIS. This may actually lead to an ambiguous definition of a particular land cover type when such different maps are compared at global level. In 1993, FAO and UNEP started efforts for development of a software tool know as LCCS which is a comprehensive standardized tool capable of providing land cover characterization to all possible land cover types in the world regardless of spatial relevance, mapping scale, data collection method etc. Adding to the global efforts of land cover legend harmonization and mapping, this study presents development of harmonized land cover legends for Namdapha National Park located in north-eastern Indian Himalayan region using LCCS and subsequent mapping. The potential of Remote Sensing (RS) and Geographical Information Systems (GIS) in forest/land cover mapping is very well recognized. Therefore, adopting the developed harmonized legends for the study area, land cover mapping was done using RS-GIS approach. 相似文献
9.
Pasture land occupies extensive areas and is increasingly of interest for sustainable intensification, land use diversification, greenhouse gas emission mitigation, and bioenergy expansion. Accurate maps of pasture and other managed land covers are needed for monitoring, intercomparison, assessing potential uses, and planning. Yet, land maps can be generated from different types of classification datasets – i.e. as a land use or land cover type – as well as different sources. In this study our aim was to assess and compare land use and land cover definitions for pasture, and examine variability in the resulting pasture land classification maps. First, we conducted a review of pasture definitions in commonly used mapping databases. We then performed a case study involving Brazil, a dominant global producer of pasture-based livestock. Six geospatial databases were harmonized and compared to each other and to MODIS land cover for Brazil including the Cerrado and Amazon biomes, which are internationally recognized for their ecological value. Total pasture area estimates for Brazil ranged by a factor greater than four, from about 430,000 km2 to over 1.7 million km2. Our analysis showed high variability in pasture land maps depending on the definitions, methods and underlying datasets used to generate them. The results are illustrative of a symptomatic problem for all manage land datasets, demonstrating the need for land categories studies and geospatial data resources that fully define land terms and describe measurable management attributes. Additionally, the suitability of individual geospatial datasets for different types of land mapping must be better described and reported. These recommendations would help bring more consistency in the consideration of managed lands in research, reporting, and policy development, as demonstrated here for pasture land using six case study datasets from multiple sources. 相似文献
10.
基于自然间断点分级法的土地利用数据网格化分析 总被引：4，自引：0，他引：4
土地利用在自然资源统一管理中扮演着重要角色,面对不同区域和年份的数据,统一分析比对口径尤为重要,同时也应反映出相互之间的差异。本文以宜兴市2009年和2017年土地利用现状数据为数据源,首先使用统一的分类标准提取用地类型中的3大类,通过不同大小的单元划分尝试和结果分析,发现适用于该数据的网格尺度大小;然后基于自然间断点分级法进行分级范围划定,对宜兴市三类用地类型的分布和变化趋势进行综合分析,较为真实地反映了宜兴市用地情况;最后通过选用合适的空间尺度和分级范围划定方法,进而构建一个兼具操作性和科学性的土地利用数据网格化方法,为自然资源部门统筹管理和综合治理提供依据。 相似文献
11.
KennedyOkelloWERE 《遥感学报》2010,14(1):180-196
<正>Land cover is a fundamental variable that links many facets of the natural environment and a key driver of global environmental change.Alterations in its status can have significant ramifications at local,regional and global levels.Hence,it is imperative to map land cover at a range of spatial and temporal scales with a view to understanding the inherent patterns for effective characterization,prediction and management of the potential environmental impacts.This paper presents the results of an effort to map land cover patterns in Kinangop division,Kenya,using geospatial tools.This is a geographic locality that has experienced rapid land use transformations since Kenya's independence culminating in uncontrolled land cover changes and loss of biodiversity.The changes in land use/cover constrain the natural resource base and presuppose availability of quantitative and spatially explicit land cover data for understanding the inherent patterns and facilitating specific and multi-purpose land use planning and management.As such,the study had two objectives viz.(i) mapping the spatial patterns of land cover in Kinangop using remote sensing and GIS and;(ii) evaluating the quality of the resultant land cover map.ASTER satellite imagery acquired in January 23,2007 was procured and field data gathered between September l0 and October 16,2007.The latter were used for training the maximum likelihood classifier and validating the resultant land cover map.The land cover classification yielded 5 classes,overall accuracy of 83.5%and kappa statistic of 0.79,which conforms to the acceptable standards of land cover mapping. This qualifies its application in environmental decision-making and manifests the utility of geospatial techniques in mapping land resources. 相似文献
12.
Floodplain wetlands in the China side of the Amur River Basin (CARB) undergone consistent decreases because of both natural and anthropogenic drivers. Monitoring floodplain wetlands dynamics and conversions over long-time periods is thus fundamental to sustainable management and protection. Due to complexity and heterogeneity of floodplain environments, however, it is difficult to map wetlands accurately over a large area as the CARB. To address this issue, we developed a novel and robust classification approach integrating image compositing algorithm, objected-based image analysis, and hierarchical random forest classification, named COHRF, to delineate floodplain wetlands and surrounding land covers. Based on the COHRF classification approach, 4622 Landsat images were applied to produce a 30-m resolution dataset characterizing dynamics and conversions of floodplain wetlands in the CARB during 1990–2018. Results show that (1) all floodplain land cover maps in 1990, 2000, 2010, and 2018 had high mapping accuracies (ranging from 90 %±0.001–97%±0.005), suggesting that COHRF is a robust classification approach; (2) CARB experienced an approximately 25 % net loss of floodplain wetlands with an area declined from 8867 km2 to 6630 km2 during 1990–2018; (3) the lost floodplain wetlands were mostly converted into croplands, while, there were 111 km2 and 256 km2 of wetlands rehabilitated from croplands during periods of 2000–2010 and 2010–2018, respectively. To our knowledge, this study is the first attempt that focus on delineating floodplain wetlands at a large-scale and produce the first 30-m spatial resolution dataset demonstrating long-term dynamics of floodplain wetlands in the CARB. The COHRF classification approach could be used to classify other ecosystems readily and robustly. The resultant dataset will contribute to sustainable use and conservation of wetlands in the Amur River Basin and provide essential information for related researches. 相似文献
13.
Land cover classification using Landsat 8 Operational Land Imager data in Beijing,China 总被引：1，自引：0，他引：1
The successful launch of Landsat 8 provides a new data source for monitoring land cover, which has the potential to significantly improve the characterization of the earth’s surface. To assess data performance, Landsat 8 Operational Land Imager (OLI) data were first compared with Landsat 7 ETM + data using texture features as the indicators. Furthermore, the OLI data were investigated for land cover classification using the maximum likelihood and support vector machine classifiers in Beijing. The results indicated that (1) the OLI data quality was slightly better than the ETM + data quality in the visible bands, especially the near-infrared band of OLI the data, which had a clear improvement; clear improvement was not founded in the shortwave-infrared bands. Moreover, (2) OLI data had a satisfactory performance in terms of land cover classification. In summary, OLI data were a reliable data source for monitoring land cover and provided the continuity in the Landsat earth observation. 相似文献
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Yusuf Ahmed Yusuf Biswajeet Pradhan Mohammed O. Idrees 《Journal of the Indian Society of Remote Sensing》2014,42(4):829-837
Alteration in climatic pattern has resulted to a steady decline in quality of life and the environment, especially in and around urbanized areas. These areas are faced with increasing surface temperature arising mostly from human activities and other natural sources; hence land surface temperature has become an important variable in global climate change studies. In this paper, Landsat TM/ETM imagery acquired between 1997 and 2013 were used to extract ground brightness temperature and land use/land cover change in Kuala Lumpur metropolis. The main objective of this paper is to examine the effectiveness of quantifying UHI effects, in space and time, using remote sensing data and, also, to find the relationship between UHI and land use change. Four land use types (forest, farmland, built-up area and water) were classified from the Landsat images using maximum likelihood classification technique. The result reveals that Greater KL experienced an increase in average temperature from 312.641°K to 321.112°K which was quite eminent with an average gain in surface temperature of 8.4717°K. During the period of investigation (1997–2013), generally high temperature is been experienced mostly in concentrated built-up areas, the less concentrated have a moderate to intermediate temperature. Again, the study also shows that low and intermediate temperature classes loss more spatial extent from 2,246.89 Km2 to 1,164.53 Km2 and 6,102.42 Km2 to 3,013.63 Km2 and a gain of 4,165.963 Km2 and 307.098 Km2 in moderate and high temperature respectively from 1997 to 2013. The results of this study may assist planners, scientists, engineers, demographers and other social scientists concerned about urban heat island to make decisions that will enhance sustainable environmental practices. 相似文献
16.
“统一行使全民所有自然资源资产所有者职责,统一行使所有国土空间用途管制和生态保护修复职责”是自然资源部组建后对时空大数据组织分类提出的新需求。本文在梳理总结国内大数据分类体系的演化特点和解析中国现行时空大数据分类体系存在的问题的基础上,结合自然资源部对大数据分类的需求,通过历史继承、合并和添加,提出了一套既能服务于自然资源部“两统一”职责,又能服务于全社会的时空大数据分类体系。研究建立的时空大数据分类体系为3级分类,共6种类型,分为17个一级类型,68个二级类型,24个三级类型,该分类较之前最明显的变化是将自然资源数据单独分为一大类,该分类对于大数据的组织管理具有重要意义。 相似文献
17.
尾矿库是矿山企业选矿的必要设施,同时对周围环境也是一个重大的危险源。为研究流域范围内尾矿库的溃坝路径以及对矿区地表造成的风险,以赤城县为例,利用GF-1高分辨率遥感影像,基于遥感RS（Remote Sensing）及地理信息系统GIS（Geographic Information System）手段对尾矿库的流域风险进行了监测分析。首先,通过分析尾矿库在遥感影像上的纹理、色调、形状以及大小等特征,制作了用于目标检测的尾矿库样本集,然后,在原始SSD目标检测网络基础上添加了反卷积模块和连接模块构建多尺度融合目标检测算法MSF_SSD,在目标检测结果基础上使用PSPnet算法实现尾矿库结构分割,得到了尾矿库内部结构—坝体以及库区,运用RS与GIS技术对尾矿库的上游汇水面以及事故可能径流进行提取,进而基于Arc Hydro模型模拟尾矿库的溃坝路径。最后,通过构建溃坝路径的缓冲区,得到尾矿库发生溃坝所造成的地物影响范围及面积。研究结果表明：赤城县尾矿库的溃坝路径总体是从西向东,从北向南,受溃坝影响的地物总面积达到480 km2。其中,林地176.52 km2,耕地175.52 km2,城市建设用地43.74 km2,农村建设用地2.47 km2,水体17.72 km2,草地和牧场分别为3.60 km2、1.22 km2。研究成果可用于分析尾矿库溃坝造成的地物损失以及影响范围面积等信息,提升尾矿库的风险管理水平及应急响应能力,为有关部门制定决策提供理论依据。 相似文献
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《地理信息系统科学与遥感》2013,50(3):237-250
The use of remotely sensed imagery to generate land cover models is common today. Validation of these models typically involves the use of an independent set of ground-truth data that are used to calculate an error matrix resulting in estimates of omission, commission, and overall error. However, each estimate of error contains a degree of uncertainty itself due to: (1) conceptual bias; (2) location/registration and co-registration errors; and (3) variability in the sample sites used to produce and validate the model. In this study, focus was not placed upon describing land cover mapping techniques, but rather the application of bootstrap resampling to improve the characterization of classification error, demonstrate a method to determine uncertainty from sample site variability, and calculate confidence limits using statistical bootstrap resampling of 500 sample sites acquired within a single Landsat 5 TM image. The sample sites represented one of five land cover categories (water, roads, lava, irrigated agriculture, and rangelands), with each category containing 100 samples. The sample set was then iteratively resampled (n = 200) and 65 sites were randomly selected (without replacement) for use as classification training sites, while the balance (n = 35) were used for validation. Imagery was subsequently classified using a maximum likelihood technique and the model validated using a standard error matrix. This classification-validation process was repeated 200 times. Confidence intervals were then calculated using the resulting omission and commission errors. Results from this experiment indicate that bootstrap resampling is an effective method to characterize classification uncertainty and determine the effect of sample bias. 相似文献
20.
Kamleshan Pillay Naeem Hoosen Agjee Srinivasan Pillay 《Journal of the Indian Society of Remote Sensing》2014,42(1):51-60
Land is the basic resource that is needed by man in order to survive: It provides humans with living space, nutrition and energy resources. The rapid growth of the human population, climate change and pollution on a catastrophic scale has caused the quality of land resources to be compromised. Remote sensing is a useful tool in land cover change detection providing information to decision makers. The aim of this study was to evaluate land cover changes in the Mtunzini area in South Africa over the past 18 years; determine why changes have occurred and predict land cover patterns for future years. In this study a supervised classification was used to detect land cover classes of the Mtunzini area from 1992 to 2009 using four Landsat images in the time series analysis. The supervised classification had an accuracy of 80.80 % which was used to model land cover changes. Commercial sugar cane and forest plantation classes increased throughout the time series. It was estimated in the modelling procedure that bushland (42.11 %) and bare soil (35 %) would be changed to commercial sugar cane. This is indicative of the expanding agriculture sector in Mtunzini. Natural vegetation is predicted to be disturbed: 18 % of bushland and 15.07 % of dense bush are expected to be replaced by rural dwellings. This is owing to a potential increase in the rural population and a reduced local economic growth. This study highlights the need for increased vigilance of the forestry industry and commercial sugar cane farms which may be encroaching on natural vegetation and livelihoods of local residents. Strategic planning and proper management of natural vegetation types is needed as these land cover types are decreasing rapidly. 相似文献