共查询到10条相似文献,搜索用时 78 毫秒
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Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing 总被引:5,自引:0,他引:5
William DJ Rybicki NB Lombana AV O'Brien TM Gomez RB 《Environmental monitoring and assessment》2003,81(1-3):383-392
The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems. 相似文献
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开展快速可靠的水生态监测并预测其变化趋势,对保护水生态环境具有重要的价值。近年来,环境DNA宏条形码技术(简称环境DNA技术)的快速发展弥补了传统形态学生物监测的缺陷,显著提升了水生生物群落的监测能力。与机器学习、遥感和云服务等技术结合,环境DNA技术不仅能大尺度、高频率、高灵敏度、自动化地获取生态监测信息,而且能准确地识别水生态系统的变化趋势,进而改变对水生态系统的认识与管理方式。因此,研究着重总结了环境DNA技术在水生态监测中的应用,分析了环境DNA技术与机器学习、卫星遥感等跨学科合作的潜在机遇,基于环境DNA技术简单、便捷的优势,提出了社会公民参与水环境保护的生态监测新思路。 相似文献
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Yuemin Yue Kelin Wang Bing Zhang Zhengchao Chen Quanjun Jiao Bo Liu Hongsong Chen 《Environmental monitoring and assessment》2010,160(1-4):157-168
Remote sensing of local environmental conditions is not accessible if substrates are covered with vegetation. This study explored the relationship between vegetation spectra and karst eco-geo-environmental conditions. Hyperspectral remote sensing techniques showed that there were significant differences between spectral features of vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative spectra could delineate karst and non-karst vegetation groups. Canonical correspondence analysis (CCA) successfully assessed to what extent the variation of vegetation spectral features can be explained by associated eco-geo-environmental variables, and it was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst region. Our study indicates that vegetation spectra is tightly linked to eco-geo-environmental conditions and CCA is an effective means of studying the relationship between vegetation spectral features and eco-geo-environmental variables. Employing a combination of spectral and spatial analysis, it is anticipated that hyperspectral imagery can be used in interpreting or mapping eco-geo-environmental conditions covered with vegetation in karst region. 相似文献
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Wei Chen Tetsuro Sakai Kazuyuki Moriya Lina Koyama Chunxiang Cao 《Environmental Modeling and Assessment》2013,18(5):547-558
The estimation of vegetation coverage is essential in the monitoring and management of arid and semi-arid sandy lands. But how to estimate vegetation coverage and monitor the environmental change at global and regional scales still remains to be further studied. Here, combined with field vegetation survey, multispectral remote sensing data were used to estimate coverage based on theoretical statistical modeling. First, the remote sensing data were processed and several groups of spectral variables were selected/proposed and calculated, and then statistically correlated to measured vegetation coverage. Both the single- and multiple-variable-based models were established and further analyzed. Among all single-variable-based models, that is based on Normalized Difference Vegetation Index showed the highest R (0.900) and R 2 (0.810) as well as lowest standard estimate error (0.128024). Since the multiple-variable-based model using multiple stepwise regression analysis behaved much better, it was determined as the optimal model for local coverage estimation. Finally, the estimation was conducted based on the optimal model and the result was cross-validated. The coefficient of determination used for validation was 0.867 with a root-mean-squared error (RMSE) of 0.101. The large-scale estimation of vegetation coverage using statistical modeling based on remote sensing data can be helpful for the monitoring and controlling of desertification in arid and semi-arid regions. It could serve for regional ecological management which is of great significance. 相似文献
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遥感监测土壤湿度综述 总被引:1,自引:0,他引:1
遥感技术具有大面积同步观测,时效性、经济性强等特点,为大面积动态监测土壤湿度提供了可能。本文对近年来国内外遥感监测土壤湿度的理论、方法的发展和应用进行了回顾,重点介绍了目前已经比较成熟和广泛应用的基于可见光与热红外波段的植被指数方法以及在干旱、半干旱地区的应用,通过对比分析了各种遥感监测方法的优缺点,指出了土壤湿度遥感监测方法存在的不足,展望了土壤湿度遥感监测方法的发展趋势。 相似文献
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Li Guoqing Li Xiaobing Zhou Tao Wang Hong Li Ruihua Wang Han Wei Dandan 《Environmental Modeling and Assessment》2016,21(3):339-355
In this research, the improved Terrestrial Ecosystem Regional (TECO-R) model was adapted to steppe ecosystems and then utilized to simulate the soil organic carbon pool in the period from 1989 to 2011 (excluding 1994, 2002, 2009, and 2010) for a typical steppe in Xilingol League of Inner Mongolia in China. The improved TECO-R model is an ecological model in combination of remote sensing data, which allows the spatial scale for the analysis of soil organic carbon which is not limited to vegetation or soil type. The spatial and temporal resolution advantages of remote sensing image can be well utilized in this model. The results indicate that in addition to an accurate simulation of the soil carbon pool of a steppe ecosystem, the vegetation aboveground carbon pool, grazing intensity of herbivores, mowing coefficient, litter carbon pool, root carbon pools of different vegetation layers, root-shoot ratio, actual residence time of different carbon pools, and allocation coefficients of different carbon pools in corresponding years are also available from the TECO-R model. Some of the above data are difficult to obtain through macro-observation but can be simulated with the TECO-R model by combining the model with input data; this is very important for a correct understanding of the feedback relationships between the steppe ecosystem’s carbon cycle and climate change (e.g., global warming) and human activities such as grazing. 相似文献