Study on ecological services evaluation of water conservation using multi-source remote sensing products

2021 ◽  
Author(s):  
Yinglin Wang ◽  
Lin Wu ◽  
Zhengwei Guo ◽  
Yabo Huang
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Ecological Services ◽  
Services Evaluation

Acquisitions of Vegetation Coverage and Cultivated Land Occupation Ratio of Taiyuan Valley Plain for Example Using CBERS-02B CCD Image

2012 ◽  
Vol 518-523 ◽  
pp. 5663-5667
Author(s):  
Shi Wei Li ◽  
Ji Long Zhang ◽  
Jian Sheng Yang
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Vegetation Coverage ◽  
Cultivated Land ◽  
Land Occupation ◽  
Coverage Ratio

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.

scholarly journals The Influence of Eco-water Retrieved by Quantitative Remote Sensing on Runoff in Upper Minjiang River Basin

2016 ◽  
Vol 20 (3) ◽  
pp. 1 ◽  
Author(s):  
Jin Huang ◽  
Wunian Yang ◽  
Wunian Yang ◽  
Xin Yang ◽  
Xin Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Water Conservation ◽  
Quantitative Research ◽  
Flood Control ◽  
Water Layer ◽  
Climate Data ◽  
Minjiang River ◽  
Quantitative Remote Sensing ◽  
Del Rio

Remote sensing quantitative retrieval of ecological water (eco-water) has been foundational in systemic and quantitative research for water resources. Eco-water resource levels indicate conservation ability for the eco-water layer and influence of this on precipitation transformation and runoff regulation. The remote sensing quantitative inversion retrieved the MEC (Modulus of eco-water Conservation) of the Upper Minjiang River Basin study area in 1994 and 2001, and combined with climate data between 1990 and 2005, the influence of conservation water on the eco-water layer on runoff was then analyzed. Results revealed significant efficacy for flood control and water supply during the drought from the hydrologic cycle of ecowater. Thus protection and restoration of the eco-water layer for flood and drought prevention are crucial.  Influencia del agua ecológica en la escorrentía de la cuenca alta del río Minjiang medida a través de teledetección cuantitativa ResumenEl sondeo remoto del agua ecológica (del inglés Eco-water, agua conservada en la superficie terrestre) es indispensable en la investigación sistemática y cuantitativa de las fuentes de agua. Los niveles de suministros de agua ecológica indican la capacidad de conservación de la capa de agua ecológica y la influencia de esta en la transformación de precipitación y la regulación de escorrentía. La inversión cuantitativa por sondeo remoto estableció el Módulo de Conservación de Agua Ecológica (MEC, del inglés Modulus of Eco-Water Conservation) para el área de estudio en la cuenca alta del río Minjiang entre 1994 y 2001, y combinada con la información climática de entre 1990 y 2005, se analizó la influencia de conservacion de agua en la capa ecoacuática. Los resultados mostraron una gran eficacia en el control de inundaciones y en el suministro de agua durante la sequía a lo largo del ciclo hidrológico. Por esta razón, la protección y restauración de la capa de agua ecológica para la prevención de inundaciones y sequía es necesaria.

Assessment on 2000-2010 Ecosystem Function Changes of Yiwulvshan National Nature Scenic Area

2014 ◽  
Vol 1051 ◽  
pp. 489-494
Author(s):  
Xiao Chen Wang ◽  
Jing Hai Zhu ◽  
Yuan Man Hu ◽  
Wei Ling Liu
Keyword(s):  
Remote Sensing ◽  
Ecosystem Function ◽  
Primary Productivity ◽  
Water Conservation ◽  
Net Primary Productivity ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
The Past ◽  
Remote Sensing Technology ◽  
Sensing Technology

Based on the remote-sensing data and ground data, this study is conducted on the ecosystem function of Yiwulvshan National Nature Scenic Area (hereinafter as “Yiwulvshan Scenic Area”) from 2000 to 2010 with the GIS (geographic information system) and RS (remote sensing) technology, so as to provide reference for better environmental protection of the scenic area. It is shown from the results that there is no obvious change of land use in Yiwulvshan Scenic Area; while the capacity for soil and water conservation is slightly improved mainly due to increase of vegetation coverage; the vegetation net primary productivity declines somewhat about 5.27% in past 10 years; and biodiversity is slightly increased. As a whole, the ecosystem function of Yiwulvshan Scenic Area basically kept stable in the past 10 years, which indicated that the existing regulations can effectively protect the ecological function of the Scenic Area.

scholarly journals Remote-Sensing Monitoring of Grassland Degradation Based on the GDI in Shangri-La, China

2019 ◽  
Vol 11 (24) ◽  
pp. 3030 ◽  
Author(s):  
Yanlin Yang ◽  
Jinliang Wang ◽  
Yun Chen ◽  
Feng Cheng ◽  
Guangjie Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Satellite Image ◽  
Animal Husbandry ◽  
Transformation Rate ◽  
Grassland Degradation ◽  
Degradation Index ◽  
Monitoring Period ◽  
Degraded Grassland

Grassland resources are important land resources. However, grassland degradation has become evident in recent years, which has reduced the function of soil and water conservation and restricted the development of animal husbandry. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are the foundation for the scientific use of grasslands. The grassland degradation index of ground comprehensive evaluation (grassland degradation index, GDIg) is a digital expression of grassland growth that can accurately indicate the degradation of grasslands. In this research, the accuracy of GDIg in evaluating grassland degradation is discussed; the typical areas of grassland degradation in Shangri-La City, i.e., the towns of Jiantang and Xiaozhongdian, are selected as the research area. Through a field survey and spectroscopy combined with Huanjing-1 (HJ-1) satellite image data, grassland degradation was monitored in the study area from 2008 to 2017. The results show that: (1) GDIg based on six indicators, namely, above-ground biomass, cover level, height, biomass of edible herbage, biomass of toxic weeds, and species richness, can effectively indicate grassland degradation, with the accuracy of the degradation grade assessment reaching 98.6%. (2) The correlation between the GDIg and the grey values of 4 wavebands and 7 types of vegetation indexes derived from the HJ-1 is analysed, and the degraded grassland inversion model was built and revised based on HJ-1 data. The grassland degradation evaluation index of remote sensing (GDIrs) model indicates that grassland degradation is proportional to the ratio vegetation index (RVI). (3) The grassland area was 405.40 km2 in the initial monitoring period, accounting for 17.26% of the study area, while at the end of the monitoring period, the area was 338.87 km2, with a loss of 66.53 km2. From 2008 to 2017, the area of non-degraded and slightly degraded grassland in the study area presented a downward trend, with decreases of 59.87 km2 and 49.93 km2, respectively. In contrast, the area of moderately degraded grassland increased by 41.17 km2 from 91.58 km2 in 2008 to 132.74 km2 in 2017, accounting for 39.17% of the grassland. The area of severely degraded grassland was 78.32 km2, accounting for 23.11% of the grassland in 2017. (4) The degraded grasslands in the study area mainly transformed into the degradation-enhanced (deterioration) type. As the transformation rate gradually slows down, the current situation of grassland degradation is not hopeful.

Hypsometric curve and integral estimation of four hilly watersheds of Ramganga river basin using RS and GIS

2018 ◽  
Vol 3 (02) ◽  
pp. 145-149
Author(s):  
Anurag Malik ◽  
Anil Kumar ◽  
Priya Rai ◽  
Sachin Kumar Singh
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
River Basin ◽  
Water Conservation ◽  
Geographical Information ◽  
Rs And Gis ◽  
Hypsometric Integral ◽  
Integral Estimation ◽  
Ramganga River ◽  
Soil And Water

In this study, hypsometric curve (HC) and hypsometric integral (HI) of Chaukhutia, Bino, Naula and Gagas watersheds located in upper Ramganga River basin, Uttarakhand State, India, was done using Remote Sensing (RS) and Geographical Information System (GIS). The results of analysis revealed that the HI = 0.398, 0.345, 0.372 and 0.319 for Chaukhutia, Bino, Naula and Gagas watersheds, respectively. Based on HC two geological stages of erosion cycle i.e. monadnock and mature were identified in the study area. Therefore, the findings of this research could be useful for planning and constructing soil and water conservation structures at appropriate locations in these watersheds.

scholarly journals Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images

2020 ◽  
Vol 12 (12) ◽  
pp. 1942
Author(s):  
Robert F. Paul ◽  
Yaping Cai ◽  
Bin Peng ◽  
Wendy H. Yang ◽  
Kaiyu Guan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Satellite Remote Sensing ◽  
Near Infrared ◽  
Heavy Precipitation ◽  
Landscape Scale ◽  
Direct Observations ◽  
The Us ◽  
The Mean ◽  
Us Midwest

Climate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding of its impacts on crop growth and biogeochemical cycling. Satellite remote sensing offers a unique opportunity to observe and analyze this dynamic phenomenon at the landscape scale. Here we analyzed a series of red–green–blue (RGB) and near infrared (NIR) remote sensing images from the Planet Labs CubeSat constellation following a period of heavy precipitation in May 2017 to determine the spatiotemporal characteristics of ponding events in the maize–soybean cropland of Champaign County, Illinois USA. We trained Random Forest algorithms for near-daily images to create binary classifications of surface water versus none, which achieved kappa values around 0.9. We then analyzed the morphology of classification results for connected pixels across space and time and found that 2.5% (5180 ha) of this cropland was classified as water surface at some point during this period. The frequency distribution of areal ponding extent exhibited a log–log relationship; the mean and median areas of ponds were 1231 m2 and 126 m2, respectively, with 26.1% of identified ponds being at the minimum threshold area of 45 m2, and 2.5% of the ponds having an area greater than 104 m2 (1 ha). Ponds lasted for a mean duration of 2.4 ± 1.7 days, and 2.3% of ponds lasted for more than a week. Our results suggest that transient ponding may be significant at the landscape scale and ought to be considered in assessments of crop risk, soil and water conservation, biogeochemistry, and sustainability.

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