scholarly journals DEVELOPMENT OF SCIENTIFICALLY-METHODOLOGICAL FRAMEWORKS OF THE RECLAIMED LANDS AND WATER BODIES’ ECOLOGICAL STATE’S ASSESSMENT

2019 ◽  
pp. 78-86
Author(s):  
A. M. Shevchenko ◽  
O. V. Vlasova ◽  
V. V. Udovenko ◽  
R. P. Bozhenko
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Anthropogenic Pressure ◽  
Ecological State ◽  
Reclaimed Land ◽  
Spatial Assessment ◽  
Sensing Data ◽  
Satellite Information ◽  
Irrigated Lands

The aim of the research is to present the development of scientifically-methodological foundations of the irrigated lands and water bodies’ ecological state’s assessment with the prospects for their improvement on the base of remote sensing data usage.  The development of evaluation of ecological state in the article highlights the stages of formation and improvement. The unified integrated approach to the assessment of the hydrogeological, engineering-geological and soil-reclamation conditions and prediction of their changes under the influence of irrigation is the result of the formation stage.  At the stage of theoretical justification and development of the scientific foundations of ecological reclamation monitoring, a methodology for the spatial assessment of the ecological reclamation state of irrigated lands was developed for the assessment of their resistance to the harmful effects of water and soils degradation. Taking into account ecological aspects the definition of ecologically-ameliorative state was applied. An ecologically-ameliorative stability of lands - complex indicator of the geosystem’ state’s dynamic is proposed . The ecologically-ameliorative stability is proposed to consider as potential (genetic) and as actual (technogenic). A comparison of the potential and actual ecologically-ameliorative stability of lands for different periods of time, taking into account the level of anthropogenic pressure on the territory, makes possible to predict the ecological state of the lands under irrigation. The current general tendency to reduce the volume of monitoring work, long-term stationary research, the size of the observation network, and the actual areas of controlled lands leads to the decreasing of the results’ information fulfillment. At the modern stage, the theoretically-methodological foundations for the determination and practical application of estimated indicators of the reclaimed land and water bodies’ state based on remote sensing data have been developed.Were organized the polygons for the results aprobation. The software “Database of spectral signatures” was developed to collect and store the results processing of the satellite information. The software AnalistNOAA was developed to analyze the hydrothermal conditions of Ukraine. The program “Multi-criteria assessment of irrigation territories” makes it possible to coordinate individual chemical and physical indicators. To achieve the optimal level of ordering of terrestrial and satellite information, a theory of their interchangeability has been developed on the whole. Conclusions. The assessment methodology is based on the concept of ecologically-ameliorative stability of lands. It allows a comprehensive spatial assessment and forecasting of the ecologically-ameliorative state of irrigated farmlands. The use of remote sensing data is an effective mean to increase the level of information and responsiveness of ground-based research in the spatial assessment of the ecological state, water-ecological and ecologically-ameliorative situations. Improvement of the scientifically-methodological foundations for assessing the ecological state of reclaimed land and water bodies is based on the obtaining of the temporal (year, month, week) and spatial (region, district, economy, field) values based on a variety of satellite information and partial or complete replacement of the estimated indicators determined by the ground surveys, by the remote sensing data determined indicators.

The use of remote sensing data for estimation of ecological state of water bodies by the example of the Svityaz lake

2010 ◽  
Vol 16 (4) ◽  
pp. 51-56
Author(s):  
L.V. Podgorodetskaia ◽  
◽  
L.N. Zub ◽  
O.D. Fedorovskyi ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Ecological State ◽  
Sensing Data ◽  
State Of Water

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Remote sensing data in land-reclamation systems and reclaimed land management assessment

2019 ◽  
Vol 16 (3) ◽  
pp. 96-104
Author(s):  
N.N. Dubenok ◽  
◽  
Yu.G. Yanko ◽  
A.F. Petrushin ◽  
R.V. Kalinichenko ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Management ◽  
Land Reclamation ◽  
Remote Sensing Data ◽  
Reclaimed Land ◽  
Sensing Data

scholarly journals Dynamic Threshold Selection for the Classification of Large Water Bodies within Landsat-8 OLI Water Index Images

2016 ◽  
Author(s):  
Fangfang Zhang ◽  
Junsheng Li ◽  
Qian Shen ◽  
Bing Zhang ◽  
Huping Ye ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Water Bodies ◽  
Landsat 8 ◽  
Threshold Selection ◽  
Water Index ◽  
Sensing Data ◽  
Twin Peaks ◽  
Large Water ◽  
Threshold Range

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

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