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对投影变形问题进行了分析,结合实例,探讨了抵偿高程面任意带高斯投影对控制投影变形的实用性。 相似文献
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基于人工神经网络面插值的方法研究 总被引:20,自引:2,他引:20
前人研究表明三层前向人工神经网络不仅能以任意精度逼近任意函数,还能以任何精度逼近其各阶导数。根据这一特性,本文将反向传播网络(Back-Propagation,简称BP网络)应用于面插值。本文认定地理要素的空间分布可以用一复杂的非线性函数模拟,该函数是由多种因素综合作用的结果,即地理要素的值是这些因素的函数,如果以各因素的输入、对应地理要素值为期望输出,对网络进行训练可对地理要素的空间分布进行模拟 相似文献
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Previous research has shown that forest roads are an important feature in many landscapes and have significant effects on wildfire ignition and cessation. However, forest road effects on burn severity have not been studied at the landscape level. Therefore, the overarching goal of our study is to identify the influences of road edge effects on the spatial patterns of burn severity. We analyzed six fires within the Okanogan–Wenatchee National Forest on the eastern slope of the Cascades mountain range of central Washington.We generated two categories for assessing road variables: (1) Primary Road Effect Zone (area within 150 m of the nearest road) and (2) Secondary Road Effect Zone (area from 150 m to 300 m to the nearest road). A regular sampling grid including one out of every 9 cells was created for each fire.These grids were intersected with burn severity data in the form of the Relative Differenced Normalized Burn Ratio (RdNBR), road distance category, stream distance, elevation, slope, terrain shape index, heat load index, canopy cover, and fuel type. We fit spatial regression models with RdNBR as the dependent variable.We found that high burn severity is less likely to occur in the Primary Road Effect Zone for most fires, although one fire exhibited the opposite relationship. Forest road edge effects were hypothesized to be an important determinant of burn severity because fragmentation created by roads alters the roadside fuel profile and environment and because road corridors create barriers to fire spread. Recognizing roadside effects on burn severity patterns highlights the need for further study of the range of effects that roads have on fuels and the fire environment and the potential for incorporating road effects into landscape-level assessments of fire risk. 相似文献
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Salt affected soils occupy significant areas in western and central India manifested by the arid and semiarid climate, sandy/clayey soil texture, absence of natural drainage, and inadequate infrastructure and irrigation development. These soils are productive following reclamation and appropriate management. The National Remote Sensing Agency, Hyderabad (India) published state-wise maps of salt affected soils in India on 1:250,000 scale using a legend that includes physiography, soil characteristics, and the aerial extent of the mapping units. In the analogue form, voluminous data contained in such maps were difficult to handle by users of varied backgrounds. An attempt was made to prepare a computerized database of salt affected soils for easy access, retrieval, and manipulation of spatial and attribute data useful for management of salt affected soils. The salt affected soils maps were prepared, for Rajasthan, Gujarat, Madhya Pradesh, and Maharashtra states, overlaying digitized layers of SAS polygons and the Survey of India basemap using the ILWIS (Integrated Land and Water Information System) software. GIS was used to prepare a composite (master) database of western and central India that showed the extent and distribution of salt affected soils. A relational database was prepared combining the digitized polygons with soil characteristics such as nature and degree of salinity (presence of higher concentration of neutral salts and neutral soil reaction), sodicity (presence of higher concentration of basic salts and alkaline reaction) and ground coverage. The regional and zonal databases of salt affected soils were prepared at a suitable scale overlaying agro-climatic regions agro-climatic zones. Spatial relation of salt affected soils with physiography, climate, geology, and agro-eco-sub-regions were evaluated employing map calculations in GIS. Saline soils were prevalent in Gujarat, and Rajasthan while sodic soils were dominant in Maharashtra and Madhya Pradesh. These were distributed primarily in the arid (B) plain of Rajasthan, alluvial (A) and coastal (D) plains of Gujarat, and peninsular plain (F) of Maharashtra and Madhya Pradesh. It occupied 2,596,942 ha (78%) in the western (Rajasthan and Gujarat) and 733,608 ha (22%) in the central (Madhya Pradesh and Maharashtra) regions. The SAS occupied 3.3 million ha in the western and central region constituting 50% of the total salt affected soils in India. The saline and sodic soils occupied 2,069,285 ha (62%) and 1,261,266 ha (38%), respectively. 相似文献
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通过GPS技术获取的空间基线向量和坐标信息是建立在WGS-84坐标系下的,无法直接应用于工程实际。因此使用GPS基线向量网必须将其从WGS-84坐标系转换到测区的平面坐标系统中,这就需要构造一个具有抵偿面的任意带高斯投影模型,来控制和减小边长投影变形。 相似文献
18.
H.B. Chauhan R.M. Dwivedi 《International Journal of Applied Earth Observation and Geoinformation》2008,10(2):181-1
Coastal zone assumes importance due to high productivity of ecosystems, man-made developmental activities, natural hazards and dynamic nature of the coast. As costal ecosystems are unique and fragile, understanding the impact of developmental activities on the sustainability of the coastal zone is very important. Remote sensing, because of repetitive and synoptic nature is an ideal tool for studying this. Time series data analyses for monitoring coastal zone require different type of sensors. Present study deals with atmospheric correction of satellite data, reflectance, selection of coastal features like, mudflat, mangroves, vegetated dune, coastal water, etc. and their inter-comparison using different sensor data of RESOURCESAT sensors. Reflectance values give better separateability for various coastal features in comparison to DN values. LISS IV can be used in place of LISS III or merged (LISS III + PAN) for long-term coastal zone studies. 相似文献
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城市局部气候分区对地表温度的影响 ——以大连市区为例 总被引:1,自引:0,他引:1
以大连市区建筑数据、SPOT 5和Landsat 8遥感数据为基础,运用局部气候带分类、地表温度反演方法,研究大连市局部气候带分区类型和特征,进而分析不同局部气候带对城市地表温度的影响。研究结果表明:①大连市建筑类型高度主要为低层建筑、多层建筑和中高层建筑,建筑密度为中密度和较高密度,甘井子区分布大量森林绿地,中山区具有丰富的公园绿地;②地表温度整体偏高,空间上呈现东高西低的趋势,其中半数以上地区地表温度达到28℃~31℃,并且有超过1%的地区地表温度大于38℃,该区域出现极端高温;③同一建筑高度上,密度越大的建筑区域,地表温度越高;同一建筑密度,多层建筑高度覆盖区地表温度较高。森林绿地地表温度最低,公园绿地和社区绿地地表温度几乎一致,附属绿地地表温度最高。 相似文献