不同遥感影像的城市热岛空间分布特征分析

针对传统的地面观测方式很难全面掌握城市地面热岛的空间分布情况的问题,该文将HJ-1B卫星热红外图像代表的亮温图和采用HJ-1B卫星反演的温度图像正规化,利用量化等级体系对同一地区的亮温分布图和温度分布图进行划分,提出并采用间隙度指数对不同影像的高温区进行空间分布进行评价。实验结果表明:基于热红外影像的城市热岛空间分布比基于热红外影像反演的温度图的热岛空间分布更为集中,为研究不同影像之间的城市热岛空间分布提供了一种评价方法。     

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

Assessing soil salinity using WorldView-2 multispectral images in Timpaki,Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed EC_e values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high EC_e values and provide a simple and effective operational alternative for saline topsoil detection and mapping.     

高光谱遥感在农作物长势监测中的应用

该研究是加拿大Saskatchewan Scott农作物轮作系统(ACS)研究的一部分.研究始于1994年,历时18 a,评价9个可耕种农作物产量系统的可靠性.由3种处理水平(organic,reduced,high)和3种作物多样性水平(low,diversified annual grains,diversified annual perennials)结合而产生的9个农作物产量系统,被用于监测和评价加拿大牧场不同处理和不同作物种植轮作下可耕种农作物的产量.在2003年生长季共收集了3次叶面积指数和光谱反射率的数据:生长季前期(6月)、生长季旺盛期(7月)、生长季后期(8月).叶面积指数是由LAI-2000植物冠层分析仪监测的,光谱测量是由覆盖了350～2500 nm波长范围共2215个波段的ADS便携式高光谱仪完成的.结果显示,光学测量可以用于监测农作物生长状况的差异.从生长季的早期到中期,光谱和叶面积指数在不同处理下有显著差异.7月中期是用遥感资料监测农作物长势的最佳季节;红光波段与近红外波段反射率的比值和基于这两个波段构造的归一化植被指数,是检测农作物长势的最佳植被指数.     

Ecological quality of some Tunisian coast and lagoon locations,by using benthic community parameters and biotic indices

Three Tunisian coastal and lagoon sites, variously affected by human activities, and differing according to their degree of communication with the sea, were studied. The total organic matter content and the granulometry of the sediment were measured and the macro-invertebrates were sampled and identified. Currently due to the lack of an efficient single biotic index for quality assessment, indications from these measurements can contribute to enrich our knowledge of the southern Mediterranean areas. Several biotic indices (AMBI, BENTIX, BOPA, BOPA modified, I2EC, ITI), most of which have been used in the context of the European Water Framework Directive, and some other structural parameters were used. The results show that the Bizerte lagoon, an almost closed area characterized mainly by extreme salinity/temperature and with a strong fluctuation of nutrients, appears biologically more impoverished. The Dkhila coast, which is more open to the sea, however, appears to be less dominated by the principal species. Biotic indices showed that the most polluted areas included the region exposed to urban inputs of Bizerte, the mouth of Hamdoun wadi and the harbour areas. In these locations, benthic macrofauna is dominated by opportunistic species like Capitella capitata, Scolelepis fuliginosa, Prionospio malmgreni, Polydora sp., Cirratulus cirratus and Cirriformia tentaculata. Other stations, which are farther from the sources of disturbance such as the mouths of the principal Wadis, the south of the Bizerte ship canal and the south of the Tunis bay, appear to be in good ecological condition and hosted a variety of sensitive species, Other stations were classified differently by the biotic indices used mainly because of the lack of a real inter-calibration of the various classification methodologies used by these indices.     

四川省上市公司可持续发展财务问题分析

财务指标是企业盈利能力的反映。根据近几年的相关数据,通过对财务指标综合分析,探讨四川省上市公司的可持续发展状况。     

Greenness Index from Phenocams Performs Well in Linking Climatic Factors and Monitoring Grass Phenology in a Temperate Prairie Ecosystem

Near-surface remote sensing (e.g., digital cameras) has played an important role in capturing plant phenological metrics at either a focal or landscape scale. Exploring the relationship of the digital image-based greenness index (e.g., Gcc, green chromatic coordinate) with that derived from satellites is critical for land surface process research. Moreover, our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited. This paper investigated the response of grass Gcc to daily environmental factors in 2018, such as soil moisture (temperature), air temperature, and solar radiation. Thereafter, using a derivative-based phenology extraction method, we evaluated the correspondence between key phenological events (mainly including start, end and length of growing season, and date with maximum greenness value) derived from Gcc, MODIS and VIIRS NDVI (EVI) for the period 2015-2018. The results showed that daily Gcc was in good agreement with ground-level environmental variables. Additionally, multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation, but not by air temperature. High frequency Gcc time series can respond immediately to precipitation events. In the same year, the phenological metrics retrieved from digital cameras and multiple satellites are similar, with spring phenology having a larger relative difference. There are distinct divergences between changing rates in the greenup and senescence stages. Gcc also shows a close relationship with growing degree days (GDD) derived from air temperature. This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors. This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.     

Pricing Weather Derivatives Index based on Temperature: The Case of Bahir Dar,Ethiopia

In this paper we present a stochastic model for daily average temperature to calculate the temperature indices upon which temperature-based derivatives are written. We propose a seasonal mean and volatility model that describes the daily average temperature behavior using the mean-reverting Ornstein-Uhlenbeck process. We also use higher order continuous-time autoregressive process with lag 3 for modeling the time evolution of the temperatures after removing trend and seasonality. Our model is fitted to 11 years of data recorded, in the period 1 January 2005 to 31 December 2015, Bahir Dar, Ethiopia, obtained from Ethiopia National Meteorological Services Agency. The analytical approximation formulas are used to price heating degree days (HDD) and cooling degree days (CDD) futures. The suggested model is analytically tractable for derivation of explicit prices for CDD and HDD futures and option. The price of the CDD future is calculated, using analytical approximation formulas. Numerical examples are presented to indicate the accuracy of the method. The results show that our model performs better to predict CDD indices.     

Comparative study of otolith and sulcus morphology for stock discrimination of yellow drum along the Chinese coast

Otolith morphology is widely used for fish stock identification. The sulcus, a structure on the medial side of the otolith, is an important feature in morphological analysis. This study was conducted to evaluate the feasibility of using sulcus morphology for stock identification and to compare its performance with commonly used otolith morphology analysis. Otoliths were collected and analyzed from three geographical groups (the Huanghe (Yellow) River estuary, HHE;the Jiaozhou Bay, JZB;and the Changjiang (Yangtze) River estuary, CJE) of yellow drum Nibea albiflora. The results show that the analysis of sulcus morphology based on shape indices (SIs), elliptic Fourier coefficients (EFc), and a combination of the two parameters identified stocks at overall classification rates of 51.0%, 72.5%, and 73.2%, respectively. These classification rates are similar to those obtained using otolith morphology analysis (57.0%, 73.8%, and 76.5% by SIs, EFc, and their combination, respectively). The findings suggest that sulcus morphology is comparable to the commonly used otolith morphology for identifying stocks of sciaenids, such as the yellow drum. For both otolith and sulcus morphology, EFc could identify the stocks more efficiently than SIs, while the combination of SIs and EFc was even better.