排序方式： 共有6条查询结果，搜索用时 15 毫秒
1
1.
Qihao Weng Mohammad Karimi Firozjaei Amir Sedighi Seyed Kazem Alavipanah 《地理信息系统科学与遥感》2019,56(4):576-604
The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness. 相似文献
2.
Rezaei Mahmoud Amiraslani Farshad Samani Najmeh Neysani Alavipanah Kazem 《Natural Hazards》2020,100(1):363-385
Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance.
相似文献3.
Alborz Mountains host Caspian Hyrcanian forest ecoregion along the northern slopes and forest steppe ecoregion in highlands. Hyrcanian forest covers the southeastern part of Caucasus biodiversity hotspot and is of great biogeographic importance. Altitudinal pattern and correlation between woody species biodiversity (DIV), forest structure ((stem density (DEN), mean basal area (MBA) and mean height class (MHC)) and disturbance (DIS) were explored along 2,400 m altitudinal gradient in Hyrcanian relict forest, Central Alborz Mountains. Vegetation changes from lowland forest (LoF) to mid- altitude forest (MiF) and montane forest (MoF) in this area. The altitudinal gradient was divided into twelve 200 m elevational belts. Point centered quarter method (PCQM) with 96 sampling points and 83 vegetation samples by plot method (PM) were used to record field data. Shannon-Wiener index and Pearson coefficient were used for diversity and correlation analysis. The results showed that DEN decreased linearly, MBA and MHC showed relatively hump shaped and DIS showed a reverse hump shaped pattern of change along altitudinal gradient. Woody species diversity decreased non-steadily from LoF to MoF. Transitional vegetations of Carpinus-Fagus and Fagus-Quercus represented higher diversity of woody taxa compared to adjacent homogenous communities. Significant correlation was observed between altitude and all parameters: DEN with MBA, DIS and DIV; MBA with DIS; MHC with DIS along with DIV; and DIS with DIV at the study area scale. Surprisingly,correlation between studied parameters differed within each vegetation type. Altitude probably acts as a proxy for human and environmental driving forces in this area. Stability of warm and wet condition, season length, soil depth along with forest accessibility probably influences the altitudinal pattern of the studied parameters. Disturbance affects forest structure and consequently diversity; especially in lowlands. The obtained results recommend using both forest biodiversity and mensuration data in management process of forest ecosystems. 相似文献
4.
Automatic Determination of Number of Homogenous Regions in SAR Images Utilizing Splitting and Merging Based on a Reversible Jump MCMC Algorithm 总被引：1，自引：0，他引：1
Ghasem Askari Aigong Xu Yu Li Seyed Kazem Alavipanah 《Journal of the Indian Society of Remote Sensing》2013,41(3):509-521
This paper presents an algorithm dealing with initial segmentation of speckled Synthetic Aperture Radar (SAR) intensity images in order to automatically determine the number of homogeneous regions. Taking this problem into account, segmentation procedure utilizing splitting and merging is designed, iteratively. The proposed approach is based upon Bayesian inference, a maximum likelihood gamma distribution parameter estimator, and a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using of image splitting operation, SAR image is partitioned into finite regions iteratively, until all individual regions are coherent. Then each region is assigned a unique label to indicate the class to which the homogeneous region belongs. The intensities of pixels in each coherent region are assumed to satisfy identical and independent gamma distribution. Then an RJMCMC scheme is designed to simulate the posterior distribution in order to estimate the number of components and delineate an initial segmentation. Thus, the main purpose of this research is to define the number of homogeneous regions rather than a perfect segmentation, i.e. model outputs can be served for unsupervised segmentation methodologies as prior information. The results obtained from Radarsat-1/2 of SAR intensity images show that the proposed algorithm is both capable and reliable in defining the accurate number of homogeneous regions in a wide variety of SAR intensity images, comprising a high level of speckle noise. 相似文献
5.
Reza Khandan Seyed Kazem Alavipanah Arastoo Pour Biazar Maryam Gharaylou 《Asia-Pacific Journal of Atmospheric Sciences》2018,54(3):431-443
Geostationary satellites are able to nowcast Convective Initiation (CI) for the next 0–6 h. Compared to using satellite predictors only, the incorporation of satellite and Numerical Weather Prediction (NWP) predictors can provide the possibility to reduce false alarm rates in 0–1:30 Convective Initiation Nowcasting (COIN). However, the correlation among these predictors not only can cause error in COIN, but also increases the runtime. In this study for the first time, all effective predictors in Satellite Convection Analysis and Tracking version 2 (SATCASTv2) and NWP were applied over Iran from 22nd March 2015 to 9th January 2016. In applying SATCASTv2 over Iran, it was necessary to make some modifications to the algorithm, such as removing case specific thresholds of satellite predictors and rearranging COIN predictors. Then, SATCASTv2 was tested and evaluated with both the full and reduced set of predictors. The results suggested that using fixed thresholds for temporal difference predictors could miss COIN in some cases. To investigate the possibility of improving computational efficiency, a dimension reduction was conducted by Factor Analysis (FA) and the number of predictors was reduced from 22 to 11. The NWP-satellite, reduced NWP-satellite, and satellite predictors were used as input in Random Forest (RF), as a parametric machine learning method, for COIN evaluation. The Combination of NWP model and satellite predictors had lower false alarm rates in contrast with satellite predictors. This is in agreement with previous studies. The results from statistical metrics showed that the reduced NWP-satellite predictors had comparable performance to the NWP-satellite predictors over study area, but decreased the run time by almost 50%. The results indicated that Convective Inhibition (CIN) was the most significant predictor when the reduced set of predictors was used. 相似文献
6.
1