scholarly journals Satellite Imageries and Field Data of Macrophytes Reveal a Regime Shift of a Tropical Lake (Lake Ziway, Ethiopia)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 396
Author(s):  
Yohannes Tefera Damtew ◽  
Boud Verbeiren ◽  
Aymere Awoke ◽  
Ludwig Triest
Keyword(s):  
Surface Water ◽  
Water Level ◽  
Regime Shift ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Aquatic Vegetation ◽  
Meteorological Data ◽  
Water Area ◽  
Ethiopian Rift

Lake Ziway is one of the largest freshwater lakes located in the central Ethiopian rift valley. The lake shoreline is dominated by macrophytes which play an important role in immobilizing run-off pollution, stabilize sediments and support biodiversity. Monitoring the spatio-temporal changes of great lakes requires standardized methods. The aim of this study was to assess the current and long-term trends of macrophyte distribution, surface water area and the water level of Lake Ziway using remote sensing images from 1986 to 2016 with additional hydro-meteorological data. A supervised image classification with classification enhancement using Normalized Difference Aquatic Vegetation Index (NDAVI) and Normalized Difference Vegetation Index (NDVI) was applied. The classification based on NDAVI revealed eight target classes which were identified with an overall producer’s accuracy of 79.6%. Contemporary open water and macrophyte fringes occupied most of the study area with a total area of 407.4 km2 and 60.1 km2, respectively. The findings also revealed a regime shift in the mean water level of the lake and a decline in macrophyte distribution. The long-term water surface area of Lake Ziway also decreased between 1986 and 2016. The changes in water level could be explained by climate variability in the region and strong anthropogenic disturbance. A decline in water level was also associated with lowered surface water area, lakeward retreated macrophyte fringes and enhanced landward encroachment of mudflats, and resulted in a succession of macrophytes with semi-terrestrial vegetations.

scholarly journals Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3010 ◽  
Author(s):  
Ruimeng Wang ◽  
Haoming Xia ◽  
Yaochen Qin ◽  
Wenhui Niu ◽  
Li Pan ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Water Body ◽  
Vegetation Index ◽  
Water Area ◽  
Google Earth ◽  
Body Area ◽  
Hetao Plain ◽  
Google Earth Engine

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

scholarly journals Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2178
Author(s):  
Wenxia Tan ◽  
Jindi Xing ◽  
Shao Yang ◽  
Gongliang Yu ◽  
Panpan Sun ◽  
...  
Keyword(s):  
Water Level ◽  
Water Depth ◽  
Vegetation Index ◽  
Aquatic Vegetation ◽  
Yangtze River Basin ◽  
Google Earth ◽  
Water Level Fluctuations ◽  
Quantitative Evidence ◽  
Longgan Lake

Aquatic vegetation in shallow freshwater lakes are severely degraded worldwide, even though they are essential for inland ecosystem services. Detailed information about the long term variability of aquatic plants can help investigate the potential driving mechanisms and help mitigate the degradation. In this paper, based on Google Earth Engine cloud-computing platform, we made use of a 33-year (1987–2019) retrospective archive of moderate resolution Landsat TM, ETM + and OLI satellite images to estimate the extent changes in aquatic vegetation in Longgan Lake from Middle Yangtze River Basin in China using the modified enhanced vegetation index, including emerged, floating-leaved and floating macrophytes. The analysis of the long term dynamics of aquatic vegetation showed that aquatic vegetation were mainly distributed in the western part of the lake, where lake bottom elevation ranged from 11 to 12 m, with average water depth of less than 1 m in spring. The vegetation area variation for the 33-year period were divided into six stages. In years with heavy precipitation, the vegetation area decreased sharply. In the following years, the area normally restored. Aquatic vegetation area had a significant negative correlation with the spring water level and summer water level. The results showed that aquatic vegetation was negatively affected when water depth exceeded 2.5 m in May and 5 m in summer. It is recommended that water depth remain close to 1 m in spring and close to 3 m in summer for aquatic vegetation growth. Our study provide quantitative evidence that water-level fluctuations drive vegetation changes in Longgan Lake, and present a basis for sustainable lake restoration and management.

scholarly journals Development of forest cover mask to monitor the health condition of forests in Poland using long-term satellite observations

2016 ◽  
Vol 77 (2) ◽  
pp. 141-150
Author(s):  
Maciej Bartold
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Forest Health ◽  
Remote Sensing Data ◽  
Forest Regions ◽  
The Impact

Abstract The work presented here aims at developing cover mask for monitoring forest health in Poland using remote sensing data. The main objective was to assess the impact of using the mask on forest condition monitoring combined with vegetation indices obtained from long-term satellite data. In this study, a new mask developed from the CORINE Land Cover 2012 (CLC2012) database is presented and its one-kilometer pixel size matched to low-resolution data derived from SPOT VEGETATION satellite registrations. For vegetation mapping, only pixels with a cover ≥ 50% of broad-leaved and mixed forests defined by CLC2012 were taken into account. The masked pixels were used to evaluate spatial variability in eight Natural-Forest Regions (NFRs). The largest coverages by masked forests were obtained in Sudetian (65.7%), Carpathian (65.9%) and Baltic (51.3%) regions. For other forest regions the coverage was observed to be around 30-50%. Time-series of the Normalized Difference Vegetation Index (NDVI) comprising SPOT VEGETATION images from 1998 until 2014 were computed and cross-comparison analyses on ≥ 50% and < 50% forest cover masks brought up frequent differences at a level higher than 0.05 NDVI in seven out of eight NFRs. An exception is the Sudetian region, where the data was highly consistent. Furthermore, the Mann-Whitney U non-parametric test revealed statistically significant differences in two regions: Baltic and Masurian-Podlasie NFR. The comparative analysis of NDVI confirmed that there is a need for additional investigation of the quality of newly developed forest mask combined with vegetation and meteorological data.

scholarly journals Forage Production of the Argentine Pampa Region Based on Land Use and Long-Term Normalized Difference Vegetation Index Data

2009 ◽  
Vol 62 (2) ◽  
pp. 163-170 ◽  
Author(s):  
Carlos M. Di Bella ◽  
Ignacio J. Negri ◽  
Gabriela Posse ◽  
Florencia R. Jaimes ◽  
Esteban G. Jobbágy ◽  
...  
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forage Production ◽  
Index Data ◽  
Pampa Region

Air quality at open-cast coal mining regions at Hunter Valley, Australia and Ramagundam, India

2021 ◽  
Author(s):  
Harsh Kamath ◽  
Chanchal Chauhan ◽  
Sameer Mishra ◽  
Aariz Ahmed ◽  
Raman Srikanth
Keyword(s):  
Power Plants ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Thermal Power ◽  
Coal Mines ◽  
Export Market ◽  
P Value ◽  
Open Cast ◽  
Monitoring Stations

&lt;p&gt;The upper Hunter Valley region in New South Wales (NSW), Australia has several open-cast coal mines, which supply coal to two large thermal power plants (TPPs) in the area, beside the export market. Long-term Particulate Matter (PM) pollutants and meteorological measurements are recorded by a network of 13 NSW government-owned continuous monitoring stations in the upper Hunter Valley region. The Ramagundam area in the state of Telangana, India has similar pollution source characteristics (coal mines and TPPs), but PM pollutant measurements are largely carried out with manual monitoring stations at 24-hour intervals, not more than twice a week. As the coal and overburden excavation from open-cast coal mines and stack emissions from TPPs lead to local PM pollution, we have used MODIS-MAIAC Aerosol Optical Depth (AOD) at 550 nm and Normalized Difference Vegetation Index (NDVI) along with the local meteorological data such as ambient temperature, relative humidity, wind speed and direction to model PM10 and PM2.5 at the upper Hunter Valley and Ramagundam regions. Our model can explain about 60% of variation in PM10 (p-value &lt; 0.0001), while a similar model is able to explain about 75% of the variation in the PM2.5 (p-value &lt; 0.0001). We will extend our model results from Hunter Valley to Ramagundam area and comment on the potential of using geospatial products such as AOD as a proxy to ground-based pollution measurements in developing countries such as India, where pollution data is scarce.&lt;/p&gt;

scholarly journals Identification of Surface Water Storing Sites Using Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI)

2018 ◽  
Vol 8 ◽  
pp. 91-100
Author(s):  
Belete Berhanu ◽  
Ethiopia Bisrat
Keyword(s):  
Surface Water ◽  
Water Resource ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cost Effective ◽  
Water Shortage ◽  
Sample Volume ◽  
Runoff Generation ◽  
Topographic Wetness Index ◽  
Wetness Index

Ethiopia is endowed with water and has a high runoff generation area compared to many countries, but the total stored water only goes up to approximately 36BCM. The problem of water shortage in Ethiopia emanates from the seasonality of rainfall and the lack of infrastructure for storage to capture excess runoff during flood seasons. Based on this premise, a method for a syndicate use of topography, land use and vegetation was applied to locate potential surface water storing sites. The steady-state Topographic Wetness Index (TWI) was used to represent the spatial distribution of water flow and water stagnating across the study area and the Normalized Difference Vegetation Index (NDVI) was used to detect surface water through multispectral analysis. With this approach, a number of water storing sites were identified in three categories: primary sources (water bodies based), secondary sources (Swampy/wetland based) and tertiary sources (the land based). A sample volume analysis for the 120354 water storing sites in category two, gives a 44.92BCM potential storing capacity with average depth of 4 m that improves the annual storage capacity of the country to 81BCM (8.6 % of annual renewable water sources). Finally, the research confirmed the TWI and NDVI based approach for water storing sites works without huge and complicated earth work; it is cost effective and has the potential of solving complex water resource challenges through spatial representation of water resource systems. Furthermore, the application of remote sensing captures temporal diversity and includes repetitive archives of data, enabling the monitoring of areas, even those that are inaccessible, at regular intervals.

scholarly journals Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2580 ◽  
Author(s):  
Tri Acharya ◽  
Anoj Subedi ◽  
Dong Lee
Keyword(s):  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pure Water ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Environmental Noise ◽  
Water Extraction ◽  
Landsat 8 ◽  
Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

Inter-comparison of remotely-sensed actual evapotranspiration products in the Zayandehrud river basin, Iran

2021 ◽  
Author(s):  
Neda Abbasi ◽  
Hamideh Nouri ◽  
Sattar Chavoshi Borujeni ◽  
Pamela Nagler ◽  
Christian Opp ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Water Shortage ◽  
Remotely Sensed ◽  
Irrigation Scheduling ◽  
Accurate Estimation ◽  
Agricultural Areas ◽  
Eta Products

&lt;p&gt;Accurate estimation of evapotranspiration (ET) helps to create a better understanding of water allocation, irrigation scheduling, and crop management especially in arid and semiarid regions where agricultural areas are far more affected by water shortage and drought events. Remote sensing (RS) facilitates estimating the ET in regions where long-term field measurements are missed.&amp;#160; In this study, we compare the performance of free open-access remotely sensed actual ET products at eleven counties of the Zayandehrud basin. The Zayandehrud basin, one of the major watersheds of Iran, suffers from recurrent droughts and long-term impacts of aridity. The RS products used in this study are namely WaPOR (2009-2019), MOD16A2 (2003-2019), SSEBOp (2003-2019). We also merged the two products of SSEBOp and WaPOR and assessed its performance. To prepare the Merged ETa Product (MEP), WaPOR was resampled to the spatial resolution of SSEBOp. Then, the average pixel values of the resampled ETa product and SSEBOp were calculated. To compare ETa estimations over croplands in each county, maximum Normalized Difference Vegetation Index (NDVI) maps at annual scale (2003-2019) were prepared using LANDSAT 5, 7, and 8 images. Annual mean ETa estimations were then extracted over croplands by using annual maximum NDVI layers. We compared the RS-based ETa with reported long-term ETa values extracted from the local available literature. Our results showed a consistent underestimation of MOD16A2 in all counties. The MEP and WaPOR outperformed other products in the estimation of ETa in seven. Estimations of WaPOR and SSEBOp agreed in most of the counties. Our analysis displayed that, although MOD16A2 underestimated ETa values, it could together with SSEBOp capture the drought better than that of WaPOR and MEP in the lower reaches of the basin. Further study is needed to evaluate the monthly and seasonal performance of RS-based ETa products.&lt;/p&gt;

scholarly journals Monitoring of Inland Surface Water Quality Using Remote Sensing on the Example of Wigry Lake

2018 ◽  
Vol 63 ◽  
pp. 00017
Author(s):  
Michał Lupa ◽  
Katarzyna Adamek ◽  
Renata Stypień ◽  
Wojciech Sarlej
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Water Quality ◽  
Lake Area ◽  
Landsat Images ◽  
Secchi Disc ◽  
Water Index ◽  
Normalized Difference Water Index

The study examines how LANDSAT images can be used to monitor inland surface water quality effectively by using correlations between various indicators. Wigry lake (area 21.7 km2) was selected for the study as an example. The study uses images acquired in the years 1990–2016. Analysis was performed on data from 35 months and seven water condition indicators were analyzed: turbidity, Secchi disc depth, Dissolved Organic Material (DOM), chlorophyll-a, Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The analysis of results also took into consideration the main relationships described by the water circulation cycle. Based on the analysis of all indicators, clear trends describing a systematic improvement of water quality in Lake Wigry were observed.

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