Your search found 4 records
1 Khachatryan, H.; Suh, D. H.; Xu, W.; Useche, P.; Dukes, M. D. 2019. Towards sustainable water management: preferences and willingness to pay for smart landscape irrigation technologies. Land Use Policy, 85:33-41. [doi: https://doi.org/10.1016/j.landusepol.2019.03.014]
Water management ; Sustainability ; Irrigation systems ; Irrigation practices ; Technology ; Willingness to pay ; Water conservation ; Soil moisture ; Evapotranspiration ; Sensors ; Models / USA / California / Florida / Texas
(Location: IWMI HQ Call no: e-copy only Record No: H049282)
(1.10 MB)
Urbanization trends, leading to growing irrigated residential landscapes continue to escalate concerns on surface, ground, and drinking water quantity and quality among environmental groups and regulatory agencies. While automated lawn irrigation systems established in urban areas are critical factors affecting water quantity and quality, homeowners’ water use may vary with their preferences for lawn irrigation systems. The choice of an irrigation system is not determined only by local restrictions or policies but also by homeowners’ preferences. Further, individuals’ preferences can be influenced by the availability of product-specific attributes such as evapotranspiration or soil-moisture based controllers (known as smart irrigation controllers). With a focus on single-family home residents in California, Florida, and Texas, the present study uses the discrete choice analysis framework to link smart irrigation attributes (e.g., sensor types, wireless operation, remote control, alert notification) and monetary incentives (e.g., annual water bill savings, rebates) to preferences and willingness-to-pay. Results indicate that homeowners prefer smart irrigation controllers to conventional automated systems, and that savings on annual water bills could be one of the most important features determining adoption of smart irrigation controllers. Controller features such as the type of operation (i.e., wireless/on-site weather station) and system malfunction alert/notification also impacted homeowners’ preferences. The findings provide practical insights into the promotion of smart irrigation controllers that can be integrated with educational campaigns, or advertisements highlighting benefits of smart irrigation technologies. Clearer understanding about homeowners’ preferences could serve as a feedback loop for policy makers and improve water policies at state and local levels.
2 Chen, F.; Chen, X.; Van de Voorde, T.; Roberts, D.; Jiang, H.; Xu, W.. 2020. Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sensing of Environment, 242:111706. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111706]
Surface water ; Observation ; Mapping ; Remote sensing ; Urban environment ; Satellite imagery ; Multispectral imagery ; Land cover / Switzerland / Belgium / USA / Baden / Brussels / Santa Barbara
(Location: IWMI HQ Call no: e-copy only Record No: H049685)
(5.25 MB)
Commonly applied water indices such as the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) were originally conceived for medium spatial resolution remote sensing images. In recent decades, high spatial resolution imagery has shown considerable potential for deriving accurate land cover maps of urban environments. Applying traditional water indices directly on this type of data, however, leads to severe misclassifications as there are many materials in urban areas that are confused with water. Furthermore, threshold parameters must generally be fine-tuned to obtain optimal results. In this paper, we propose a new open surface water detection method for urbanized areas. We suggest using inequality constraints as well as physical magnitude constraints to identify water from urban scenes. Our experimental results on spectral libraries and real high spatial resolution remote sensing images demonstrate that by using a set of suggested fixed threshold values, the proposed method outperforms or obtains comparable results with algorithms based on traditional water indices that need to be fine-tuned to obtain optimal results. When applied to the ASTER and ECOSTRESS spectral libraries, our method identified 3677 out of 3695 non-water spectra. By contrast, NDWI and MNDWI only identified 2934 and 2918 spectra. Results on three real hyperspectral images demonstrated that the proposed method successfully identified normal water bodies, meso-eutrophic water bodies, and most of the muddy water bodies in the scenes with F-measure values of 0.91, 0.94 and 0.82 for the three scenes. For surface glint and hyper-eutrophic water, our method was not as effective as could be expected. We observed that the commonly used threshold value of 0 for NDWI and MNDWI results in greater levels of confusion, with F-measures of 0.83, 0.64 and 0.64 (NDWI) and 0.77, 0.63 and 0.59 (MNDWI). The proposed method also achieves higher precision than the untuned NDWI and MNDWI with the same recall values. Next to numerical performance, the proposed method is also physically justified, easy-to implement, and computationally efficient, which suggests that it has potential to be applied in large scale water detection problem.
3 Wu, J.; Wang, X.; Zhong, B.; Yang, A.; Jue, K.; Wu, J.; Zhang, L.; Xu, W.; Wu, S.; Zhang, N.; Liu, Q. 2020. Ecological environment assessment for greater Mekong Subregion based on pressure-state-response framework by remote sensing. Ecological Indicators, 117:106521. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2020.106521]
Environmental Impact Assessment ; Ecological indicators ; Remote sensing ; Landsat ; Biodiversity ; Vegetation ; Land use ; Land cover ; Spatial distribution ; Farmland ; Ecosystems ; Anthropogenic factors ; Evapotranspiration ; Sustainable development / China / Myanmar / Lao People's Democratic Republic / Greater Mekong Subregion / Yunnan / Sipsongpanna
(Location: IWMI HQ Call no: e-copy only Record No: H049753)
(6.71 MB)
The environment project in the greater Mekong sub-region was the largest multi-field environmental cooperation launched by six countries (China, Vietnam, Laos, Myanmar, Thailand and Cambodia) in 2006, since the cooperation mechanism was established by Asian Development Bank (ADB) in 1992. How to establish the indicators to assess the achievements of the biological corridor construction and the status of ecological environment quantitatively is one of the prerequisites for the future project ongoing phase. The popular Pressure-State-Response (PSR) framework was employed in this study to assess the natural and human pressure, the healthy state of regional natural environment, and the subsequent response of ecosystem dynamic change in the Greater Mekong Subregion. Instead of using surveying based data as driving parameters, large amount of driving factors were retrieved from multi-source remote sensing data from 2000 to 2017, which provides access to larger updated and real-time databases, more tangible data allowing more objective goal management, and better spatially covered. The driving factors for pressure analysis included digital elevation, land surface temperature, evapotranspiration, light index, road network map, land cover dynamic change and land use degree, which were derived directly and indirectly from remote sensing. The indicators for state evaluation were composed of vegetation index, leaf area index, and fractional vegetation cover from remote sensing directly. The comprehensive response index was mainly determined by the pressure and state indicators. Through the analysis based on an overlay technique, it showed that the ecological environment deteriorated firstly from 2000 to 2010 and then started to improve from 2010 to 2017. The proofs indicated that the natural forest and wetland ecosystems were improved and the farmland area was decreased between 2000 and 2017. This study explored effective indicators from remote sensing for the ecological and environmental assessment, which can provide a strong decision-making basis for promoting the sustainable development of the ecological environment in the greater Mekong subregion, as well as the technological support for the construction of the biodiversity corridor.
4 Xu, W.; Tang, M.; Li, Y. 2022. A new method for assessment of regional drought risk: information diffusion and interval mapping adjustment based on k-means cluster points. Journal of Water and Climate Change, 13(12):4302-4316. [doi: https://doi.org/10.2166/wcc.2022.345]
Drought ; Risk assessment ; Risk management ; Mapping ; Indicators ; Vulnerability ; Water resources ; Cluster analysis / China / Anhui
(Location: IWMI HQ Call no: e-copy only Record No: H051590)
https://iwaponline.com/jwcc/article-pdf/13/12/4302/1154476/jwc0134302.pdf
https://vlibrary.iwmi.org/pdf/H051590.pdf
https://vlibrary.iwmi.org/pdf/H051590.pdf
(0.77 MB) (784 KB)
Aiming at the defect that there is no ability for the conventional weighted comprehensive assessment method (WCA) to grade drought risk directly, a method based on k-means cluster points to realize the classification of drought risk is proposed in this paper. On the basis of calculating the drought risk value of cluster points, the inverse distance weight interpolation method (IDWI) and multidimensional normal diffusion method (MND) were used to quantify the drought risk value, and the discrimination between the risk value and grade was improved by interval mapping adjustment (IMA). In this paper, the drought risk of Anhui Province from 2000 to 2020 was calculated to verify the above method. The results show that: (1) The drought risk quantification method based on information redistribution of k-means cluster point can not only realize automatic risk classification, but also re-quantify the risk value of the assessment object in the same risk grade, which makes up for the defects that the conventional WCA cannot carry out grade division and the conventional clustering method cannot assign the risk value of the assessment object. (2) The result of information redistribution based on MND is closer to the actual drought situation and more reasonable than IDWI. (3) The dispersion effect of risk value obtained by information redistribution based on k-means cluster point can be improved by the IMA of drought risk. It improves the discrimination degree of risk value, so that the grades can be displayed more intuitively. The defect of the WCA is overcome by the new method proposed in this paper, the follow-up utilization space is widened, and the thinking of risk quantification in drought risk assessment is broadened.
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