scholarly journals The methodology of studying changes in the Gobi region`s lake area

2018 ◽  
pp. 28-39
Author(s):  
Tuvshin G ◽  
Khosbayar Ts ◽  
Davaadorj D
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Seasonal Changes ◽  
Vegetation Index ◽  
Lake Area ◽  
Factors Affecting ◽  
The Past ◽  
Number Of Factors ◽  
Remote Sensing Methods

This study based on remote sensing methods of changes in the multi-year and seasonal changes in areas of the Buuntsagaan, Orog, Olgoy Lakes in the depression of lakes. Using Landsat 5 and 8 satellite 2000-2017, We used 14 pictures of water and vegetation index those were taken from the lake. Over the past decades, the Gobi region’s lakes have been decreased significantly. There are number of factors affecting changes in areas of the lakes. Finally, we concluded that the water level in the Baidrag and Tuin rivers are decreasing in Buuntsagaan and Orog Lakes.

scholarly journals Change Detection and Prediction of Urmia Lake and its Surrounding Environment During the Past 60 Years Applying Geobased Remote Sensing Analysis

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 519-525 ◽  
Author(s):  
K. Valizadeh Kamran ◽  
B. Khorrami
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Industrial Development ◽  
Urmia Lake ◽  
Lake Area ◽  
Lake Urmia ◽  
The Past ◽  
Aerial Photogrammetry ◽  
Surrounding Environment ◽  
Elevation Data

<p><strong>Abstract.</strong> The gradual depletion of Urmia lake has been a challenge in both national and international scale during recent years. In recent decades the imprudent industrial development accompanied by continuous use of groundwater aquifers has been one of the most pivotal reasons for this crisis. Water level monitoring and detection of its changes within Urmia lake as well as the surrounding environment in the past 60 years integrating GIS, Remote sensing and photogrammetric methods is the main goal for this study. In order to accomplish this, Aerial Photogrammetry images and derived topographic maps from them for the year 1955, Digital Elevation data, quantitative and qualitative information regarding water wells and lake Urmia respectively and finally remotely sensed images of Landsat TM, ETM+ and OLI sensors were used. The temporal range for the study was set to 60 from the year 1955 to 2014. Scrutinizing 12 images relating to different periods, vast changes in both area and perimeter of the lake were detected. Based on the results, the lake area has decreased from 451800 hectares in 1955 to 89730 in recent years due to various causative factors. It is also found that the highest water recession, which caused increasing coastal salty areas, has been occurred in the southern parts of the lake. While the receding water level of the lake has a deep correlation with the increasing agricultural activities around the lake, it has a reverse relation with the lake water EC. These fluctuations can be detrimental to the environment, economy and society. Looking at the changes in lake Urmia, if the current situation keeps on and no drastic measures are taken, turning into a salt land, the Lake would be completely disappeared till 2033.</p>

scholarly journals Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013–2020

2021 ◽  
Vol 13 (13) ◽  
pp. 2570
Author(s):  
Teng Li ◽  
Bozhong Zhu ◽  
Fei Cao ◽  
Hao Sun ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Eastern China ◽  
Water Environment ◽  
Relative Difference ◽  
Boreal Summer ◽  
Support Vector ◽  
Lake Area ◽  
The Past ◽  
Remote Sensing Reflectance

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

scholarly journals Remote sensing analysis on lake area variations of Yamzho Yumco in Tibetan Plateau over the past 40 a

2012 ◽  
Vol 24 (3) ◽  
pp. 494-502 ◽  
Author(s):  
CHU Duo ◽  
◽  
PU Qiong ◽  
LABA Zhuoma ◽  
ZHU Liping ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tibetan Plateau ◽  
Lake Area ◽  
The Past

scholarly journals Changes in Vegetation on Mount Agung Volcano Bali Indonesia

2019 ◽  
Vol 4 (2) ◽  
pp. 54
Author(s):  
Sutomo Sutomo ◽  
Luthfi Wahab
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Present Condition ◽  
Short Time Scale ◽  
Landsat 8 ◽  
Web Based ◽  
Volcanic Event ◽  
The Past

Volcanic activity is a major natural disturbance that can catastrophically change an ecosystem over a short time scale. The eruption of Mt. Agung strato-volcano in 1963-1964 was considered among the most important volcanic event of the 20th century due to its effect on global climate. Studies on vegetation and landscape of Mt. Agung post-1970-1980 has been scarce. The current eruption of Mount Agung in June-July 2018, brought awareness of the importance urge to document the past and current landscape along with vegetation on Mt. Agung. Our study aimed to utilize remote sensing technique to explore the pattern of current (2017) land cover and vegetation density on Mt. Agung and estimate of vegetated areas and whether it has changed from the past. LANDSAT 8 images (www.earthexplorer.usgs.gov/) were used in this study. Supervised classification in ENVI was employed to obtain land use or land cover of the Mt. Agung area. Normalized Difference Vegetation Index (NDVI) was also calculated using the feature in the ARC GIS. Online web-based application, REMAP was used to obtain information on past and present condition of the crater of Mt. Agung to see whether there have been changes in vegetated areas around the crater using REMAP (www.remap-app.org). Results showed there are basically five main landcover that can be recognized namely forest (20758.23 ha), settlement (4058.37 ha), water area (41606.64 ha), open area (15335.64 ha) and farming (34554.78 ha). Our NDVI analysis also resulted in areas with have high density (78836.04 ha), medium density (15490.26 ha) and also no vegetation (31008.24 ha). Using web-based GIS application REMAP, we found that there has been an increase (approximately 1 km2) in vegetation cover from the 1980s to 2016.  The changes in vegetation near the crater of Mt. Agung is relatively slow when compared to another volcano such as Mt. Merapi. Remote sensing application has enabled us to obtain information on vegetation change relatively easily compared to conduct an extensive on-ground survey where more time and funding is needed.

scholarly journals Long-term analysis of irrational water use processes based on cartographic materials and remote sensing data

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Andrey Medvedev ◽  
Natalia Alekseenko ◽  
Natalia Telnova ◽  
Alexander Koshkarev
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Level ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Topographic Maps ◽  
High Resolution Data ◽  
Sensing Data ◽  
The Past ◽  
Resolution Data

<p><strong>Abstract.</strong> Assessment and monitoring of environmental features based on large-scale and ultra-high resolution data, including remote sensing data, which have advantages in the repeatability of information and the speed of processing of incoming data, often face issues of completeness and duration of time series in retrospective analysis. Cartographic materials and remote sensing data allow monitoring for rapidly changing natural and anthropogenic features in the study areas, but very often face a problem when an event or phenomenon occurred many years ago and it is necessary to make a complete chronology.</p><p>Ultra-high-resolution data, remote sensing data and the results of the subsequent geoinformation analysis are widely used to solve problems in a number of socio-economic areas of territorial development, in particular:</p><ul><li>in environmental studies &amp;ndash; identification of local sources of water pollution, the consequences of their impact onecosystems, synthetic assessment of the ecological state of the territories and their comfort;</li><li>in the management of various resources, including water &amp;ndash; determination of biological productivity of water bodies, identification of water bioresources, detection of anthropogenically provoked and natural changes in water mass,implementation for glaciological studies, etc.</li></ul><p>Within the framework of the current study, a multi-time analysis of the water area and the coastal strip of Lake Sevan (the Republic of Armenia) at an altitude of about 1900 m above sea level, was carried out. The lake has repeatedly beensubjected to changes in the water level of the reservoir in the past. The 1930s and in the period between 1949 to 1962 were noted by the most intense drop in water level (more than 10 meters). In the 1990s, there was a slight increase inthe level, and then until 2001, the level of the lake continued to decrease.</p><p>The main factors affecting aquatic ecosystems and the overall ecological status of the lake are:</p><ol><li>Repeated changes in the water level of the reservoir in the past and its expected fluctuations in the future.</li><li>The uncontrolled discharge of harmful substances caused great damage to the lake, which affected the water qualityand biodiversity of this unique natural site.</li><li>Untimely cleaning of flooded forests, which increases the risk of eutrophication of the lake.</li><li>The poorly organized system of waste disposal and unauthorized landfills of municipal solid waste, as well as animalwaste.</li><li>Unauthorized construction of recreational facilities and capital structures in the coastal and water protection zonewhich may be flooded.</li></ol><p> The information support of the study is based on the materials of satellite imagery from the worldview2, SPOT 5/6,Resurs-P, Canopus-B, materials from the international space station (ISS), materials of archival aerial photography anddata obtained from the UAVs, in combination with other map data sources in the range of scales 1&amp;thinsp;:&amp;thinsp;5&amp;thinsp;000 &amp;ndash; 1&amp;thinsp;:&amp;thinsp;100&amp;thinsp;000,including digital topographic maps, land use maps, statistical and literary data. In fact, cartographic materials andremote sensing data provide a time history of 75 years, from large-scale topographic maps of 1942&amp;ndash;1943 to highlydetailed images of 2017&amp;ndash;2018.</p><p>According to the results of the study, it was possible to establish the position of the coastline for different time periods.The period between 1949 and 1962, when there was the most critical drop in the water level, was especially interestingand had not been studied before. Archival aerial photographs for 1943 and 1963 allowed to reconstruct the position ofthe coastline for almost every year of irrational water use.</p>

scholarly journals Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa

Drones ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 28 ◽  
Author(s):  
Ibrahim Wahab ◽  
Ola Hall ◽  
Magnus Jirström
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Farming Systems ◽  
Sub Saharan Africa ◽  
Field Methods ◽  
Smallholder Farms ◽  
Sub Saharan ◽  
Remote Sensing Methods ◽  
Spad Readings

The application of remote sensing methods to assess crop vigor and yields has had limited applications in Sub-Saharan Africa (SSA) due largely to limitations associated with satellite images. The increasing use of unmanned aerial vehicles in recent times opens up new possibilities for remotely sensing crop status and yields even on complex smallholder farms. This study demonstrates the applicability of a vegetation index derived from UAV imagery to assess maize (Zea mays L.) crop vigor and yields at various stages of crop growth. The study employs a quadcopter flown at 100 m over farm plots and equipped with two consumer-grade cameras, one of which is modified to capture images in the near infrared. We find that UAV-derived GNDVI is a better indicator of crop vigor and a better estimator of yields—r = 0.372 and r = 0.393 for mean and maximum GNDVI respectively at about five weeks after planting compared to in-field methods like SPAD readings at the same stage (r = 0.259). Our study therefore demonstrates that GNDVI derived from UAV imagery is a reliable and timeous predictor of crop vigor and yields and that this is applicable even in complex smallholder farms in SSA.

scholarly journals Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas

2019 ◽  
Vol 6 (4) ◽  
pp. 775
Author(s):  
Eveline Pereira ◽  
Eduarda Silveira ◽  
Inácio Thomaz Bueno ◽  
Fausto Weimar Acerbi Júnior
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Seasonal Changes ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Changes ◽  
Spectral Features ◽  
Object Based ◽  
Global Accuracy ◽  
Or Fires

The Brazilian Savannas have been under increasing anthropic pressure for many years, and land-use/land-cover changes (LULCC) have been largely neglected. Remote sensing provides useful tools to detect changes, but previous studies have not attempted to separate the effects of phenology from deforestation, clearing or fires to improve the accuracy of change detection without a dense time series. The scientific questions addressed in this study were: how well can we differentiate seasonal changes from deforestation processes combining the spatial and spectral information of bi-temporal (normalized difference vegetation index) NDVI images? Which feature best contribute to increase the separability on classification assessment? We applied an object-based remote sensing method that is able to separate seasonal changes due to phenology effects from LULCC by combining spectral and the spatial context using traditional spectral features and semivariogram indices, exploring the full capability of NDVI image difference to train random forest (RF) algorithm. We found that the spatial variability of NDVI values is not affect by vegetation seasonality and, therefore, the combination of spectral features and semivariogram indices provided high global accuracy (97.73%) to separate seasonal changes and deforestation or fires. From the total of 13 features, 6 provided the best combination to increase the separability on classification assessment (4 spatial and 2 spectral features). How to accurately extract LULCC while disregarding the ones caused by phenological differences in Brazilian seasonal biomes undergoing rapid land-cover changes can be achieved by adding semivariogram indices in combination with spectral features as input data to train RF algorithm.

scholarly journals Remote Sensing Vegetation Indices in Viticulture: A Critical Review

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 457
Author(s):  
Rigas Giovos ◽  
Dimitrios Tassopoulos ◽  
Dionissios Kalivas ◽  
Nestor Lougkos ◽  
Anastasia Priovolou
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Proximal Sensing ◽  
Spectral Bands ◽  
Sensing Platforms ◽  
Precision Viticulture ◽  
Remote Sensing Methods

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

scholarly journals A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume

2019 ◽  
Vol 11 (2) ◽  
pp. 158 ◽  
Author(s):  
Jonathan Chipman
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Thermal Emission ◽  
Water Levels ◽  
Satellite Monitoring ◽  
Lake Area ◽  
Laser Altimeter ◽  
Economic Resource ◽  
Lake Formation

Lakes in arid regions play an important role in regional water cycles and are a vital economic resource, but can fluctuate widely in area and volume. This study demonstrates the use of a multisensor satellite remote sensing method for the comprehensive monitoring of lake surface areas, water levels, and volume for the Toshka Lakes in southern Egypt, from lake formation in 1998 to mid-2017. Two spectral water indices were used to construct a daily time-series of surface area from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), validated by higher-resolution Landsat images. Water levels were obtained from analysis of digital elevation models from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), validated with ICESat Geoscience Laser Altimeter System (GLAS) laser altimetry. Total lake volume peaked at 26.54 × 109 m3 in December 2001, and declined to 0.76 × 109 m3 by August 2017. Evaporation accounted for approximately 86% of the loss, and groundwater recharge accounted for 14%. Without additional inflows, the last remaining lake will likely disappear between 2020 and 2022. The Enhanced Lake Index, a water index equivalent to the Enhanced Vegetation Index, was found to have lower noise levels than the Normalized Difference Lake Index. The results show that multi-platform satellite remote sensing provides an efficient method for monitoring the hydrology of lakes.

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