共查询到20条相似文献，搜索用时 15 毫秒
1.
结合逻辑回归方法和元胞自动机模型构建了逻辑回归CA模型,模型中的逻辑回归方法能够很方便地获取影响因子的权重,再设置起止条件等,便可以模拟出溢油的动态变化情况。把模型应用到Deep Spill项目的溢油模拟实验中,结果表明,从模型结果的形态看,模拟结果与检验结果吻合程度较高,能够很好地模拟出溢油扩散与漂移等重要的溢油特性。从统计分析中可知,模拟结果的总精度可达96.8%,Kappa系数达到0.834。 相似文献
2.
Mbulisi Sibanda Timothy Dube Victor M. Bangamwabo Onisimo Mutanga Cletah Shoko Webster Gumindoga 《国际地球制图》2016,31(9):1006-1018
The objective of this study was to understand the factors that explain the spatial distribution of elephant poaching activities in the areas of the mid-Zambezi Valley, Zimbabwe using geographic information system (GIS) and remotely sensed data integrated with spatial logistic regression. The results showed that significant (α = 0.05) elephant poaching hot spots are located closer to wildlife protected areas. Results further demonstrated that resource availability (water and forage) are the main factors explaining elephant poaching activities in the mid-Zambezi Valley. For example, the majority of poaching activities were found to occur in areas with high vegetation fractional cover (high forage) and close to waterholes. The results also showed that poaching incidences were more prevalent during the dry season. The findings of this study highlight the significance of integrating GIS, remotely sensed data and spatial logistic regression tools for understanding and monitoring elephant poaching activities. This information is critical if poaching activities are to be minimized and it is also important for planning, monitoring and mitigation of poaching activities in similar protected areas across the sub-Saharan Africa. 相似文献
3.
ABSTRACT Inner Mongolia is an important ecological zone of northern China and 67% of its land area is grassland. This ecologically fragile region has experienced significant vegetation degradation during the last decades. Although the spatial extents and rates of vegetation change have previously been characterized through various remote sensing and GIS studies, the underlying driving factors of vegetation changes are still not well understood. In this study, we first used time-series MODIS NDVI data from 2000 to 2016 to characterize the temporal trend of vegetation changes. These vegetation change trends were compared with climate and socioeconomic variables to determine the potential drivers. We used a set of statistical methods, including multiple linear regression (MLR), spatial correlation analysis, and partial least squares (PLS) regression analyzes, to quantify the spatial distribution of the driving forces and their relative importance to vegetation changes. Results show that the main driving factors and their impact magnitude (weight) are in the order of human activities (r = -0.785, p < 0.01, VIP = 1.37), precipitation (r = 0.541, p < 0.05, VIP = 0.89), temperature (r = -0.319, p > 0.05 VIP = 0.59). The area affected by human activities was 10.57%. Specific human activities, such as coal mining and grazing were negatively associated with vegetation cover, while eco-engineering projects had positive impacts. This study provided thorough quantification of driving forces of vegetation change and enhanced our understanding of their interactions. Our integrated geospatial-statistical approach is particularly important for sustainable development of ecosystem balance in Chen Barag Banner and other areas facing similar challenges. 相似文献
4.
考虑建筑物荷载的变形监测数据处理方法 总被引：1，自引：0，他引：1
在建筑物变形分析中,多元回归分析方法有利于建立变形值与多个影响因子之间的关系。本文将多元回归模型用于建筑物变形监测数据处理,阐述了回归模型中影响因子的确定、回归模型的最小二乘参数估计以及回归模型的显著性检验。通过对建筑物沉降观测数据进行分析计算,说明了此方法的全过程,最后给出实测与预测曲线的比较以及模型的改进。 相似文献
5.
Xiaoli Wang Xiaoli Zhang Guomin Zhou 《Journal of the Indian Society of Remote Sensing》2017,45(5):785-794
The rice disease is one of the most serious injurious factors that cause major loss of rice production and subsequent economy in agricultural industry. This study explored a new method for obtaining information of the rice disease in a short term through model regression methods. The spectrum characteristics of rice leaves under different disease damage were firstly analyzed for its relationship with rice disease level. The sensitive bands of the spectrum for accurately supervising rice diseases were selected with principal component analysis (PCA). The stepwise regression method and BP neural network were both used to establish the spectrum-based models for recognizing rice diseases. Results showed that five major characteristic bands were determined by PCA (990, 1850, 660, 1921, and 1933 nm) for monitoring foliar rice diseases, among which the edge area for red light had the best correlation with rice disease level was also selected as the parameter to establish the model. Specifically, the composite reflectivity of wavelengths between 990 and 1933 nm was negatively related to rice brown spot diseases stress, which was then used to establish the model. Parameters of the red edge area and the ranged reflectivity between 660 and 990 nm were used to establish models for monitoring rice sheath blight diseases. Totally, there were 60 samples employed to build models for identifying the two diseases by the stepwise regression method and the BP neural network method, and the rest 41 ones were used for further model verification. Compared with the stepwise regression analysis, BP neural network was evaluated to perform better with characteristic bands at 660, 990, and 1933 nm. In conclusion, the establishment of the function model in our study can be implemented to monitor rice diseases, which provided a theoretical basis for indirect and rapid monitoring rice diseases. 相似文献
6.
Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) data from different observation modes were analysed to determine (1) which observation mode most accurately retrieves tropical forest biomass information and (2) whether different modes, when considered together, yield improved results in comparison to identical data-sets analysed independently. We performed regression analysis to estimate above-ground forest biomass using PALSAR backscatter data for natural and planted forests in south-eastern Bangladesh. The coefficient of determination (r 2) was lower or equal to 0.499 (n = 70) when PALSAR data from different observation modes were separately considered, but increased sharply when one class (rubber) is dropped and average backscatter of fine beam single (FBS) and polrimetric (PLR) modes are used in the analysis. The results presented in this article are useful for both regional and global forest biomass inventories and fixing acquisition modes for planned L-band SAR missions. 相似文献
7.
Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R2 = 0.54; p < 0.0075). A significant statistical relationship (r = ?0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management. 相似文献
8.
Spatial variability of soil erodibility and its correlation with soil properties in semi-arid mountainous watershed,Saudi Arabia 总被引：2，自引：0，他引：2
Soil erodibility values are best estimated from long-term direct measurements on runoff-plots; however, in lack of field tests, these values can be estimated using relationships based on physico-chemical soil properties. The study objective was to assess the erodibility and its correlation with soil properties. The average erodibility value was estimated 0.043 t ha h ha?1 MJ?1 mm?1. The areas with heavy textured soil and low organic matter content had the lowest values of erodibility. The erodibility decreases as the sand content increases, whereas silt showed a positive correlation. The erodibility factors and its relation to soil properties were evaluated using multiple regression analysis. Results revealed that sand and organic matter content of soil combinedly explained 78% of variation. Altitudinal increases also seem to affect the soil texture. This study has demonstrated that soil properties and erodibility values can be used as assistance for soil conservation practices and modelling of landscape processes. 相似文献
9.
Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model 总被引：1，自引：0，他引：1
Abubakr A. A. Alsharif Biswajeet Pradhan 《Journal of the Indian Society of Remote Sensing》2014,42(1):149-163
The main objective of this paper is to analyze urban sprawl in the metropolitan city of Tripoli, Libya. Logistic regression model is used in modeling urban expansion patterns, and in investigating the relationship between urban sprawl and various driving forces. The 11 factors that influence urban sprawl occurrence used in this research are the distances to main active economic centers, to a central business district, to the nearest urbanized area, to educational area, to roads, and to urbanized areas; easting and northing coordinates; slope; restricted area; and population density. These factors were extracted from various existing maps and remotely sensed data. Subsequently, logistic regression coefficient of each factor is computed in the calibration phase using data from 1984 to 2002. Additionally, data from 2002 to 2010 were used in the validation. The validation of the logistic regression model was conducted using the relative operating characteristic (ROC) method. The validation result indicated 0.86 accuracy rate. Finally, the urban sprawl probability map was generated to estimate six scenarios of urban patterns for 2020 and 2025. The results indicated that the logistic regression model is effective in explaining urban expansion driving factors, their behaviors, and urban pattern formation. The logistic regression model has limitations in temporal dynamic analysis used in urban analysis studies. Thus, an integration of the logistic regression model with estimation and allocation techniques can be used to estimate and to locate urban land demands for a deeper understanding of future urban patterns. 相似文献
10.
Tawanda W. Gara Tiejun Wang Andrew K. Skidmore Shadrack M. Ngene Timothy Dube Mbulisi Sibanda 《国际地球制图》2017,32(11):1243-1253
Understanding factors affecting the behaviour and movement patterns of the African elephant is important for wildlife conservation, especially in increasingly human-dominated savanna landscapes. Currently, knowledge on how landscape fragmentation and vegetation productivity affect elephant speed of movement remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explains elephant speed of movement in the Amboseli ecosystem in Kenya. We used GPS collar data from five elephants to quantify elephant speed of movement for three seasons (wet, dry and transitional). We then used multiple regression to model the relationship between speed of movement and landscape fragmentation, as well as vegetation productivity for each season. Results of this study demonstrate that landscape fragmentation and vegetation productivity predicted elephant speed of movement poorly (R2 < 0.4) when used as solitary covariates. However, a combination of the covariates significantly (p < 0.05) explained variance in elephant speed of movement with improved R2 values of 0.69, 0.45, 0.47 for wet, transition and dry seasons, respectively. 相似文献
11.
This study aims to analyse the processes and patterns of peri-urbanization using diurnal earth observation data-sets from onboard DMSP–Operational Linescan System. In this study, multiple correlation, simple and conditional linear regression are used to find out the degree of relationship and spatial behavioural pattern of the factors responsible for the urbanization. All the factors are standardized using the Analytical Hierarchy Process (AHP) coupled fuzzy membership functions. AHP is used to derive the weighting of the factors to produce the urbanity index. In total three functional zones – urban, rural and urban shadow are generated based on factor standardization and spatial contiguity index. Urban fringe is sharing ≥ 60% of Urbanity Index followed by rural fringe (39.50–60% of urbanity index) and urban shadow <39.50% of urbanity index. Shape index indicates that the city is going through unplanned development following cross to star shape growth. 相似文献
12.
以安居客网站爬取的2018年10月894个南昌市住宅小区二手房价格为研究对象,利用地理加权回归模型探讨了建筑特征、邻里特征、区位特征等方面各影响因子对住宅价格的作用差异。研究结果表明:1)地理加权回归(GWR)模型的拟合结果优于OLS模型,将回归系数结果空间可视化发现南昌市二手房价格影响因子具有空间异质性。2)不同因子对价格影响程度不同,其中对南昌市二手房价格影响较大的因子是房龄、绿化率以及与CBD的距离。3)同一因子对住房价格的影响在不同空间也具有差异性。其中主要是绿化率、容积率、重点学校、购物中心及地铁对新开发区的二手房价格影响比较大,对老城区影响较小;商务中心区和三甲医院对南昌县二手房价的影响最大;而房龄和旅游景点对老城区影响比较大。 相似文献
13.
加权平均温度(Tm)是全球卫星导航系统技术反演大气可降水量的关键参数,影响着水汽反演的精度。针对传统的Bevis模型运用在中国区域精度不高的问题,该文提出新的增加时空参数的Tm多元线性回归模型。根据2013—2015年中国86个探空站点的探空资料,分析了Tm的时空特征;然后根据2013年站点资料,利用线性回归建模方法建立了中国区域的Tm单因子回归模型和增加了时空参数的Tm多因子回归模型,并利用2014—2015年的探空数据进行验证。Tm单因子回归模型和Tm多因子回归模型的精度分别为3.1 K和2.6 K,比Bevis模型(精度3.3 K)分别提高了约6.0%和21.2%。考虑到季节对Tm的影响,将Tm多因子回归模型按季节分段,得到按季节分段的Tm多因子回归模型,其精度与Tm多因子回归模型大致相当,但能更细致表达出不同季节Tm的精度情况。结果表明增加了时空参数的Tm多因子回归模型更加适合中国区域的加权平均温度Tm的计算。 相似文献
14.
The land use and land cover pattern of a region is a consequence of natural and socio-economic factors and their utilization by man in time and space. In this study, we hypothesized that land use and land cover change patterns in the Lake Chivero catchment, Zimbabwe, were related to its human population dynamics. Using nonparametric correlation coefficients (Spearman’s rho, ρ), we found that bareland, cropland and built-up land had positive relations with human population growth of ρ = 0.7, ρ = 0.9 and ρ = 1, respectively. Grassland/shrubland, water and forest, on the other hand, had a negative relationship with human population growth of ρ = ?0.9, ρ = ?0.7 and ρ = ?0.667, respectively. However, these relationships were only significant (p < 0.05) for cropland, grassland/shrubland and built-up land. Human population dynamics in the Lake Chivero catchment could be one of the major drivers of land use and land cover change in the catchment between 1986 and 2014. 相似文献
15.
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
16.
In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R2 ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error. 相似文献
17.
Water depth estimation using optical remote sensing offers a reliable and efficient means of mapping coastal zones. Here, we aim to find a suitable model for fast and practical bathymetry of an estuary using Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor (LISS-3) images. The study examines three different models; (1) least square regression model, (2) spectral band-ratio method and (3) multi-tidal bathymetry model. The findings are supported with in situ observed depth values and statistical estimates. Although the least square regression model has provided best results with root mean square error (RMSE) of 0.4 m, it requires a large number of observed data points for absolute depth estimation. Spectral band-ratio and multi-tidal model provides results with RMSEs 2.1 and 0.9 m, respectively. The present investigation demonstrates that multi-date imagery exploitation at disparate tide levels is the best estimation technique for recursive shallow water bathymetry where in situ observation is not possible. 相似文献
18.
19.
Analysing regional industrialisation in Jiangsu province using geographically weighted regression 总被引：6，自引：0，他引：6
Industry is the most important sector in the Chinese economy. To identify the spatial interaction between the level of regional
industrialisation and various factors, this paper takes Jiangsu province of China as a case study. To unravel the existence
of spatial nonstationarity, geographically weighted regression (GWR) is employed in this article. Conventional regression
analysis can only produce `average' and `global' parameter estimates rather than `local' parameter estimates which vary over
space in some spatial systems. Geographically weighted regression (GWR), on the other hand, is a relatively simple, but useful
new technique for the analysis of spatial nonstationarity. Using the GWR technique to study regional industrialisation in
Jiangsu province, it is found that there is a significant difference between the ordinary linear regression (OLR) and GWR
models. The relationships between the level of regional industrialisation and various factors show considerable spatial variability.
Received: 4 April 2001 / Accepted: 17 November 2001 相似文献
20.
The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models – and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) – for the Mumbai region over the period 1973–2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future. 相似文献