scholarly journals Spatiotemporal Change Detection Analysis of Turkish Lake Water Surface Area in Response to Anthropogenic Ecosystem Disturbances Using Long-Term Landsat TM/ETM+ Data

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Durmaz F ◽  
Karakaya N
Keyword(s):  
Change Detection ◽  
Surface Area ◽  
Lake Water ◽  
Water Surface ◽  
Landsat Tm ◽  
Spatiotemporal Change ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Water Surface Area

scholarly journals SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 31-35 ◽  
Author(s):  
A. E. Akay ◽  
B. Gencal ◽  
İ. Taş
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Short Paper ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Spatiotemporal Change ◽  
Change Detection Analysis ◽  
Flooded Forest ◽  
Detection Analysis

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

scholarly journals MONITORING OF LAKE ECOSYSTEM PARAMETER USING LANDSAT DATA (A CASE STUDY: LAKE RAWA PENING)

2017 ◽  
Vol 12 (1) ◽  
pp. 71 ◽  
Author(s):  
Bambang Trisakti ◽  
Nana Suwargana ◽  
Joko Santo Cahyono
Keyword(s):  
Surface Area ◽  
Lake Water ◽  
Water Hyacinth ◽  
Water Surface ◽  
Atmospheric Correction ◽  
Water Clarity ◽  
Likelihood Method ◽  
Lake Ecosystem ◽  
Water Surface Area ◽  
Water Classification

Most lakes in Indonesia have suffered (decrease in quality) caused by land conversion in the catchment area, soil erosion, and water pollution from agriculture and households. This study utilizes remote sensing data to monitor several parameters used as ecosystem status assessors in accordance with the guidelines of Lake Ecosystem Management provided by the Ministry of Environment. The monitoring was done at Lake Rawa Pening using Landsat TM/ETM+ satellite data over the period of 2000-2013. The data standardization was done for sun angle correction and also atmospheric correction by removing dark pixels using histogram adjustment method. RGB color composites (R: NIR + SWIR, G: NIR, B: NIR-RED) were used for water hyacinth identification; thus, the lake water surface area can be delineated. Further samples were collected for water hyacinth and water classification with Maximum Likelihood method. Total Suspended Matter (TSM) by Doxaran model and the water clarity from field measurement was correlated to build water clarity algorithm. The results show that Lake Rawa Pening was deterioting in term of quality during the period of 2000-2013; it can be seen from the dynamic rate of the shrinkage and the expansion of the lake water surface area, the uncontrolled distribution of water hyacinth which it covered 45% of the lake water surface area in 2013, the increased of TSM concentration, and the decreased of water clarity. Most parts of Rawa Pening’s water have clarity less than 2.5 m which indicated that the thropic status is hypertrophic class.

scholarly journals Changes in Water Surface Area of the Lake in the Steppe Region of Mongolia: A Case Study of Ugii Nuur Lake, Central Mongolia

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1470
Author(s):  
Erdenesukh Sumiya ◽  
Batsuren Dorjsuren ◽  
Denghua Yan ◽  
Sandelger Dorligjav ◽  
Hao Wang ◽  
...  
Keyword(s):  
Surface Area ◽  
Global Climate Change ◽  
Lake Water ◽  
Water Surface ◽  
Global Climate ◽  
Lake Surface ◽  
Central Mongolia ◽  
Water Surface Area ◽  
Annual Evaporation ◽  
Lake Surface Area

The Ugii Nuur Lake is not only one of the small hydrologically closed lakes located in the Orkhon River Basin in Central Mongolia but also the most vulnerable area for global climate change. Therefore, this study aims to investigate the impacts of recent global climate change on the water surface area. The data we analyzed were various measured hydro-meteorological variables of the lake basin and the lake surface area, which was estimated from Landsat series satellite data from 1986 to 2018. The methods we used were Mann-Kendall (MK), Innovative trend analysis method (ITAM), Sen’s slope estimator test, correlation, and regression analysis. The variation of lake water surface area has a strong positive correlation with the change of the lake water level (r = 0.95). The Mann-Kendall trend analysis has indicated that under a significant decrease in total annual precipitation ( Z   = −0.902) and inflow river discharge ( Z   = −5.392) and a considerable increase in total annual evaporation ( Z = 4.385) and annual average air temperature ( Z   = 4.595), the surface area of the Ugii Nuur Lake has decreased sharply ( Z = −6.021). The total annual evaporation (r = −0.64) and inflow river discharge (r = 0.67) were the essential hydro-meteorological factors affecting the surface area of the Ugii Nuur Lake. The lake surface area decreased by 13.5% in 2018 compared with 1986. In the near future, it is vital to conduct scientific studies considering the volume of lake water, groundwater, and the anthropogenic impact.

scholarly journals Patterns and Drivers of Deforestation and Forest Degradation in Myanmar

2021 ◽  
Vol 13 (14) ◽  
pp. 7539
Author(s):  
Zaw Naing Tun ◽  
Paul Dargusch ◽  
DJ McMoran ◽  
Clive McAlpine ◽  
Genia Hill
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Local Governments ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Map Algebra ◽  
Change Detection Analysis ◽  
Detection Analysis

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

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