天绘一号03星三线阵/多光谱载荷影像质量评价

2015年10月26号发射的天绘一号03星是中国传输型立体测绘卫星天绘一号系列的第3颗卫星。三线阵和多光谱影像数据是天绘一号卫星的重要数据,用于立体测绘、彩色融合、遥感观测等多种用途。因此,为了推动天绘一号03卫星影像数据早日为相关领域提供服务,本文采用客观评价的方法,对天绘一号03星的三线阵和多光谱影像质量进行了全面的评价分析。评价结果显示(天绘一号)03星的清晰度、对比度、细节能量、边缘能量、功率谱、信息容量指标值远高于天绘一号01、02星。说明天绘一号03星三线阵/多光谱影像在地物细节和边缘纹理特征的描述上优于天绘一号01、02星,同时载荷接收的信息量远高于天绘一号01、02星。天绘一号03星较高的信噪比指标说明其载荷抑制噪声的能力优于天绘一号01、02星。边缘辐射畸变和增益调整畸变表明在辐射均匀程度上,天绘一号03星三线阵/多光谱影像介于天绘一号01和02星之间。因此,与同类天绘一号01,02星的三线阵与多光谱影像相比,天绘一号03星影像质量有着显著的提高。     

Integrated multi-scalar analysis of vulnerability to environmental hazards: Assessing extreme flooding in western Amazonia

Environmental change is significantly altering hydrological systems worldwide, with substantial impacts for the people who live on floodplains and depend on rivers for their livelihoods and lifeways. Amazonia is a region significantly affected by these changes, particularly more severe flooding. This paper proposes a multi-scalar approach to vulnerability, applying it empirically to the analysis of household vulnerability to the 2011 flood—the second largest flood event along the Ucayali River in 30 years—in terms of exposure, impacts, and responses. Locally relevant indicators for assets, social identity, and social networks at the community and household levels are used to examine their role in shaping flood vulnerability, the interplay of community-level and household-level factors, and differential vulnerability across exposure, impacts, and responses to the same hazard event. We find that the most common impacts of severe flooding in rural Amazonia are on agriculture and that fishing is the dominant response strategy. This study suggests that covariate shocks, like floods, can have distinct idiosyncratic impacts and responses among households. We demonstrate that more integrated approaches to vulnerability analysis offer potential for better understanding differential vulnerabilities within populations as well as for drawing comparisons across hazard events and different settings.     

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%.     

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.     

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。     

Gender analysis for better coastal management – Increasing our understanding of social-ecological seascapes

Although highly recognized as needed, studies linking gender and coastal/marine management are scarce. This research illustrates the importance of gender analysis in natural resource management by linking gender and coastal management i.e. Marine Spatial Planning. The research was conducted in various Zanzibar seascapes (Unguja Island, Tanzania). Using a typology comprising gender structure, symbolism and identity; the results show a clear gendered division of labor, highly associated with a gender symbolism in which traditional roles of women as responsible for reproduction activities played a major role. Men used the whole seascape for their activities, while women remained in coastal forests and shallow areas collecting wood, invertebrates and farming seaweed. These activities allowed women to combine productive and reproductive work. Ecosystem importance for subsistence decreased with distance from land for both genders, while the importance for income increased with distance for men. Both genders acknowledged seagrasses as very important for income. Income closely followed the universal pattern of men earning more. Identities were defined by traditional ideas like “women are housewives”, while men identities were strongly associated with fisheries with reinforced masculinity. Livelihood diversity was higher for women also showing a tendency of slow change into other roles. Management was found to be strongly androcentric, revealing a deep gender inequality. The research exemplifies how a gender analysis can be conducted for management enhancement. It also invites replication around the world. If management is found to be androcentric in coastal locations elsewhere, a serious gender inequality can be at hand at global level.     

Growing up and cleaning up: The environmental Kuznets curve redux

Borrowing from the Kuznets curve literature, researchers have coined the term “environmental Kuznets curve” or EKC to characterize the relationship between pollution levels and income: pollution levels will increase with income but some threshold of income will eventually be reached, beyond which pollution levels will decrease. The link between the original Kuznets curve, which posited a similar relationship between income and inequality, and its pollution-concerned offspring lies primarily with the shape of both curves (an upside-down U) and the central role played by income change. Although the EKC literature has burgeoned over the past several years, few concrete conclusions have been drawn, the main themes of the literature have remained constant, and no consensus has been reached regarding the existence of an environmental Kuznets curve. EKC research has used a variety of types of data and a range of geographical units to examine the effects of income levels on pollution. Changes in pollution levels might also be at least partly explained by countries’ position in the demographic transition and their general population structure, however little research has included this important aspect in the analysis. In addition, few analyses confine themselves to an evaluation for one country of the long-term relationship between income and pollution. Using United States CO₂ emissions as well as demographic, employment, trade and energy price data, this paper seeks to highlight the potential impact of population and economic structure in explaining the relationship between income and pollution levels.     

Effects of detailed soil spatial information on watershed modeling across different model scales

Hydro-ecological modelers often use spatial variation of soil information derived from conventional soil surveys in simulation of hydro-ecological processes over watersheds at mesoscale (10–100 km²). Conventional soil surveys are not designed to provide the same level of spatial detail as terrain and vegetation inputs derived from digital terrain analysis and remote sensing techniques. Soil property layers derived from conventional soil surveys are often incompatible with detailed terrain and remotely sensed data due to their difference in scales. The objective of this research is to examine the effect of scale incompatibility between soil information and the detailed digital terrain data and remotely sensed information by comparing simulations of watershed processes based on the conventional soil map and those simulations based on detailed soil information across different simulation scales. The detailed soil spatial information was derived using a GIS (geographical information system), expert knowledge, and fuzzy logic based predictive mapping approach (Soil Land Inference Model, SoLIM). The Regional Hydro-Ecological Simulation System (RHESSys) is used to simulate two watershed processes: net photosynthesis and stream flow. The difference between simulation based on the conventional soil map and that based on the detailed predictive soil map at a given simulation scale is perceived to be the effect of scale incompatibility between conventional soil data and the rest of the (more detailed) data layers at that scale. Two modeling approaches were taken in this study: the lumped parameter approach and the distributed parameter approach. The results over two small watersheds indicate that the effect does not necessarily always increase or decrease as the simulation scale becomes finer or coarser. For a given watershed there seems to be a fixed scale at which the effect is consistently low for the simulated processes with both the lumped parameter approach and the distributed parameter approach.     

A multidisciplinary multi-scale framework for assessing vulnerabilities to global change

Terrestrial ecosystems provide a number of vital services for people and society, such as food, fibre, water resources, carbon sequestration, and recreation. The future capability of ecosystems to provide these services is determined by changes in socio-economic factors, land use, atmospheric composition, and climate. Most impact assessments do not quantify the vulnerability of ecosystems and ecosystem services under such environmental change. They cannot answer important policy-relevant questions such as 'Which are the main regions or sectors that are most vulnerable to global change?’ 'How do the vulnerabilities of two regions compare?’ 'Which scenario is the least harmful for a sector?’This paper describes a new approach to vulnerability assessment developed by the Advanced Terrestrial Ecosystem Analysis and Modelling (ATEAM) project. Different ecosystem models, covering biodiversity, agriculture, forestry, hydrology, and carbon sequestration are fed with the same Intergovernmental Panel on Climate Change (IPCC) scenarios based on the Special Report on Emissions Scenarios (SRES). Each model gives insights into specific ecosystems, as in traditional impact assessments. Moreover, by integrating the results in a vulnerability assessment, the policy-relevant questions listed above can also be addressed. A statistically derived European environmental stratification forms a key element in the vulnerability assessment. By linking it to other quantitative environmental stratifications, comparisons can be made using data from different assessments and spatial scales.     

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACT

We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.