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Academic literature on the topic 'Vegetated water bodies'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Vegetated water bodies"

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Hardy, Andy, Georgina Ettritch, Dónall Cross, Pete Bunting, Francis Liywalii, Jacob Sakala, Andrew Silumesii, et al. "Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats." Remote Sensing 11, no. 5 (March 12, 2019): 593. http://dx.doi.org/10.3390/rs11050593.

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Providing timely and accurate maps of surface water is valuable for mapping malaria risk and targeting disease control interventions. Radar satellite remote sensing has the potential to provide this information but current approaches are not suitable for mapping African malarial mosquito aquatic habitats that tend to be highly dynamic, often with emergent vegetation. We present a novel approach for mapping both open and vegetated water bodies using serial Sentinel-1 imagery for Western Zambia. This region is dominated by the seasonally inundated Upper Zambezi floodplain that suffers from a number of public health challenges. The approach uses open source segmentation and machine learning (extra trees classifier), applied to training data that are automatically derived using freely available ancillary data. Refinement is implemented through a consensus approach and Otsu thresholding to eliminate false positives due to dry flat sandy areas. The results indicate a high degree of accuracy (mean overall accuracy 92% st dev 3.6) providing a tractable solution for operationally mapping water bodies in similar large river floodplain unforested environments. For the period studied, 70% of the total water extent mapped was attributed to vegetated water, highlighting the importance of mapping both open and vegetated water bodies for surface water mapping.
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Omar, D., M. Idrees, H. Ahmadu, A. Yusuf, O. Ipadeola, A. Babalola, and A. Abdulyekeen. "Assessment of vegetation dynamics and forest loss using google earth engine and multi-temporal sentinel-2 imagery." Agro-Science 21, no. 2 (June 22, 2022): 85–94. http://dx.doi.org/10.4314/as.v21i2.10.

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This study evaluated regional vegetation dynamics and changes between 2015 and 2020 using Google earth engine (GEE) platform and normalized difference vegetation index (NDVI) derived from the multi-petabyte catalogue of sentinel-2 imageries. Using the computational capability of GEE, yearly mean NDVI from 2015 to 2020 were computed using level C-1 product. Subsequently, each of the NDVI images was classified into four land cover classes; water bodies, non-vegetated, grassland /cropland /shrubs, and forest using NDVI threshold values of < 0.01, 0.01-0.20, 0.20-0.30 and > 0.30, respectively. The classified maps allowed for the assessment of yearly variation in vegetation and changes between 2015 and 2020. Result showed that non-vegetated area increased from 18.53% in 2015 to 42.56% in 2020 (~ 25.00% gain), the forest area reduced to 6.78% in 2020 compared to 23.76% measured in 2015 (~ 17.00% loss in forest); whereas water bodies and grassland/cropland/shrubs remained relatively constant (0.21 and ~ 50.00%, respectively) across the years studied. Presently, the forest land was estimated to be about 2, 371.131 km2 (~ 6.70%) of the total land mass, grassland/cropland/shrubs occupied 17, 770.79 km2 (~ 50.07%), non-vegetated area was slightly less than half with 15, 274.85 km2 (~ 43.04%) and water bodies occupied 75.68 km2 (~ 0.21%).
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Deng, Xiangzheng, Jikun Huang, Yingzhi Lin, and Qingling Shi. "Interactions between Climate, Socioeconomics, and Land Dynamics in Qinghai Province, China: A LUCD Model-Based Numerical Experiment." Advances in Meteorology 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/297926.

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This simulation-based research produces a set of forecast land use data of Qinghai Province, China, applying the land use change dynamics (LUCD) model. The simulation results show that the land use pattern will almost keep being consistent in the period from 2010 to 2050 with that in 2000 in Qinghai Province. Grassland and barren or sparsely vegetated land will cover more than 80% of the province’s total area. The land use change will be inconspicuous in the period from 2010 to 2050 involving only 0.49% of the province’s land. The expansion of urban and built-up land, grassland, and barren or sparsely vegetated land and the area reduction of mixed dryland/irrigated cropland and pasture, water bodies, and snow or ice will dominate land use changes of the case study area. The changes of urban and built-up land and mixed dryland/irrigated cropland and pasture will slow down over time. Meanwhile, the change rates of water bodies, snow and ice, barren or sparsely vegetated land, and grassland will show an inverted U-shaped trajectory. Except for providing underlying surfaces for RCMs for future climate change assessment, this empirical research of regional land use change may enhance the understanding of land surface system dynamics.
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Mail, Abd Al Salam Mohammed. "Desertification Detected in the Udhaim River Basin, Iraq Based on Spectral Indices Derived from Remote Sensing Images." Miscellanea Geographica 21, no. 3 (September 1, 2017): 124–31. http://dx.doi.org/10.1515/mgrsd-2017-0007.

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AbstractIn this study, changes in Land Use Land Cover (LULC) have been investigated over the Udhaim River Basin in Iraq by using spectral indices. NDVI, NDBI, NDWI, NDBaI, and CI represent respectively the vegetation, built-up, water bodies, bare-land, and soil crust of LULC. Two different images were acquired for the analysis, namely a Landsat 5 TM image from 1 July 2007 and a Landsat 8 OLI from 5 June 2015, both representing summer conditions. Results show that the percentages of vegetated land and water body areas have decreased. On the contrary, the percentages of built-up, bare land and soil crust areas have increased. The loss of vegetated areas and water body areas is a signal of land degradation leading to desertification, due to the combined effects of climate conditions, water deficit and human activities. Field observation shows that human activities have a significant impact on land degradation.
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Fonsêca, Nathan C., Giselle L. Moreira, José Nailson B. Santos, Marília Isabelle O. da Silva, Máida Cynthia D. de Lima, Ana Jéssica S. Barbosa, Jéssica Stéfane A. Cunha, Diogo José O. Pimentel, Flávio Cipriano de A. do Carmo, and Felipe S. Amorim. "Spatial-Temporal Dynamics of Vegetation Cover in a Diversity Hotspot for the Conservation of Brazilian Cerrado." Journal of Agricultural Science 11, no. 15 (September 15, 2019): 200. http://dx.doi.org/10.5539/jas.v11n15p200.

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This work investigates the spatial-temporal dynamics of land use and vegetation covers in a conservation area of Cerrado, in the county of Currais, Piau&iacute;, in which the economy depends on large agricultural projects. We used maps of a 32-year time series (1985 to 2017) of land use and cover provided by the Brazilian Annual Land Use and Land Cover Mapping Project (MapBiomas). We assessed six classes of land uses and vegetation covers: forest, savanna, grassland, agriculture/pasture, non-vegetated area, and water bodies. There was a fast increase in pressure on natural ecosystems from 1985 and 2017, primarily from 2000. The land use for agriculture and pasture increased from 0.26% (726.93 ha) in 2000 to 16% (50,772.63 ha) in 2017. During this period, the native vegetation decreased 15.90%, with savannas suffering the largest loss in hectares of vegetation (41,663.73 ha), followed by the forests (9,837.35 ha). The grassland cover, non-vegetated area, and water bodies remained unchanged. These results provide essential information for decision making and can be used to guide public policies for the conservation, monitoring, and sustainable management of remnant vegetation areas.
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Fadhli, Muhammad, Rifardi Rifardi, and Suardi Tarumun. "PEMODELAN PERUBAHAN PENGGUNAAN LAHAN DI KABUPATEN KAMPAR." Jurnal Ilmu Lingkungan 14, no. 1 (March 20, 2020): 52. http://dx.doi.org/10.31258/jil.14.1.p.52-65.

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This study aims to identify the types of land use and patterns of land use change in Kampar District. This study uses a survey method with visual interpretation techniques Landsat imagery in 1998,2008,2018 using geographic information systems (GIS). The results showed that 12 types of land use in Kampar district in 1998-2008-2018 were: 1) forest, 2) plantation forest, 3) plantation, 4) mixed plantation, 5) dry land agriculture, 6) rice field, 7 ) shrubs, 8) built up land, 9) mines, 10) open, 11) ponds, and 12) bodies of water. The patterns of land use change in Kampar Regency in the period 1998-2008-2018 were 187 patterns. There are 2 types of patterns of change, namely 1) the pattern of change from vegetated land use to vegetated land use and 2) the pattern of changes in the use of vegetated to non-vegetated land. The first type with the most dominant pattern based on the area of change include: 1) forests - plantations - plantations, 2) mixed plantations - plantations - plantations 3) Forests – plantation forests - plantation forests. The second type with a pattern of change based on area includes: 1) forest – forest - open, 2) mixed plantation - built up land – built up land, 3) mixed plantation - mixed plantation - mine.
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Singh, Ashangbam Inaoba, and Kanwarpreet Singh. "Remote Sensing and GIS based Land Use Land Cover Analysis in Chandel District, Manipur, India." IOP Conference Series: Earth and Environmental Science 889, no. 1 (November 1, 2021): 012046. http://dx.doi.org/10.1088/1755-1315/889/1/012046.

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Abstract Rapid urbanization has dramatically altered land use and land cover (LULC). The focus of this research is on the examination of the last two decades. The research was conducted in the Chandel district of Manipur, India. The LULC of Chandel (encompassing a 3313 km2 geographical area) was mapped using remotely sensed images from LANDSAT4-5, LANDSAT 7 ETM+, and LANDSAT 8 (OLI) to focus on spatial and temporal trends between years 2000 and 2021. The LULC maps with six major classifications viz., Thickly Vegetated Area (TVA), Sparsely Vegetated Area (SVA), Agriculture Area (AA), Population Area (PA), Water Bodies (WB), and Barren Area (BA) of the were generated using supervised classification approach. For the image classification procedure, interactive supervised classification is adopted to calculate the area percentage. The results interpreted that the TVA covers approximately 65% of the total mapped area in year 2002, which has been decreased up to 60% in 2007, 56% in 2011, 55 % in 2017, and 52% in 2021. The populated area also increases significantly in these two decades. The change and increase in the PA has been observed from year 2000 (8%) to 2021 (11%). Water Bodies remain same throughout the study period. Deforestation occurs as a result of the rapid rise of the population and the extension of the territory.
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Sahour, Hossein, Kaylan M. Kemink, and Jessica O’Connell. "Integrating SAR and Optical Remote Sensing for Conservation-Targeted Wetlands Mapping." Remote Sensing 14, no. 1 (December 30, 2021): 159. http://dx.doi.org/10.3390/rs14010159.

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The Prairie Pothole Region (PPR) contains numerous depressional wetlands known as potholes that provide habitats for waterfowl and other wetland-dependent species. Mapping these wetlands is essential for identifying viable waterfowl habitat and conservation planning scenarios, yet it is a challenging task due to the small size of the potholes, and the presence of emergent vegetation. This study develops an open-source process within the Google Earth Engine platform for mapping the spatial distribution of wetlands through the integration of Sentinel-1 C-band SAR (synthetic aperture radar) data with high-resolution (10-m) Sentinel-2 bands. We used two machine-learning algorithms (random forest (RF) and support vector machine (SVM)) to identify wetlands across the study area through supervised classification of the multisensor composite. We trained the algorithms with ground truth data provided through field studies and aerial photography. The accuracy was assessed by comparing the predicted and actual wetland and non-wetland classes using statistical coefficients (overall accuracy, Kappa, sensitivity, and specificity). For this purpose, we used four different out-of-sample test subsets, including the same year, next year, small vegetated, and small non-vegetated test sets to evaluate the methods on different spatial and temporal scales. The results were also compared to Landsat-derived JRC surface water products, and the Sentinel-2-derived normalized difference water index (NDWI). The wetlands derived from the RF model (overall accuracy 0.76 to 0.95) yielded favorable results, and outperformed the SVM, NDWI, and JRC products in all four testing subsets. To provide a further characterization of the potholes, the water bodies were stratified based on the presence of emergent vegetation using Sentinel-2-derived NDVI, and, after excluding permanent water bodies, using the JRC surface water product. The algorithm presented in the study is scalable and can be adopted for identifying wetlands in other regions of the world.
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Mia, Md Bodruddoza, Tanzeer Hasan, and Syed Humayun Akhter. "Change Detection of Landuse-landcover in and around Cox’s Bazar-Teknaf Coastal Area of Bangladesh Using Satellite Images." Dhaka University Journal of Earth and Environmental Sciences 8, no. 1 (December 14, 2020): 1–9. http://dx.doi.org/10.3329/dujees.v8i1.50754.

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The prime objective of this study is to detect changes of the biophysical resources (or landuse-landocver) of the Cox’s Bazar-Teknaf area from 1999 to 2015 using Landsat TM/ETM+/OLI sensors images after applying classifications and indices approaches. The normalized differential vegetation index (NDVI) result showed that water bodies reduced by about 20% of the study area from 1999 to 2015. Bared land or beach decreased by 6% from 1999 to 2005 and then increasing trend is observed in this study from 2005 to 2015. Mixed land was more or less an increasing trend in this study area. Vegetation cover increased from 1999 to 2005 and then suddenly decreased a lot from 2009 to 2015. The declining trend of water bodies is mostly in the northern part of the study area, which is mostly shallow area where shrimp or salt farms exist. The result of normalized differential water index (NDWI) showed that the water bodies decreased from 1999 to 2015 about 10% of the study area. Land area was increased from 1999 to 2005 and then increased a little from 2005 to 2009 and afterward it decreased. The normalized differential salinity index (NDSI) result shows that the area of non-saline zone increased from 1999 to 2015. Low saline zones reduced from 1999 to 2005 but it increased after 2005 due to absence of high and medium salinity signature from NDSI value. The low saline zone is mostly in the northern side of Cox’s Bazar where shrimp farms or salt bed exist. In unsupervised thematic maps, the water bodies increased in this region from 1999 to 2009 and then declined again. The declining trend of water bodies indicates the erosion activities from 1999 to 2009. The fallow lands including beach also decreased from 2005 to 2015, indicates more agricultural activities including fisheries, salt production in this study area. On the other hand, the vegetated region decreased but settlements area including vegetation increased in this area. In supervised thematic map, the result showed that the shrimp cultivation and salt bed increased in this region from 1999 to 2015 and agricultural land has decreasing trend. On the other hand, the vegetated region was ups and down trend from 1999 to 2009. The study indicates that the Landsat images are quite efficient to map biophysical resources of the study area with various techniques. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(1), 2019, P 1-9
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Han, P., K. Vijayaraghavan, S. Reuben, E. S. Estrada, and U. M. Joshi. "Reduction of nutrient contaminants into shallow eutrophic waters through vegetated treatment beds." Water Science and Technology 68, no. 6 (September 1, 2013): 1280–87. http://dx.doi.org/10.2166/wst.2013.361.

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One of the most effective mitigative approaches to eutrophication is the reduction of nutrient loading into water bodies. Bioremediation presents an economically viable and ecologically sustainable technology to nutrient pollution control taking advantage of the remarkable ability of plants and their associated microbial community to assimilate and remove nutrients from the environment. In this study, four emergent macrophytes (Cyperus haspan, Pandanus amaryllifolius, Pontederia cordata and Thalia geniculata) and two floating plants (Hygroryza aristata and Pistia stratiotes) were deployed in bank-side treatment beds and comparatively assessed for their remediative capabilities for nutrient control. P. stratiotes exhibited the highest removal efficiency for both nitrate and phosphate among the six plant species studied. Emergent macrophytes, P. amaryllifolius, C. haspan and P. cordata, were also found to be highly effective in nutrient uptake exhibiting removal efficiencies up to 100%. With the exception of T. geniculata, depletion of nutrients as a result of plant uptake significantly impeded the natural colonization of algae invariably leading to improvements in water quality in terms of turbidity and pH. Suppression of algae proliferation by T. geniculata was not preceded by a reduction in nutrient concentrations suggesting that T. geniculata may be directly inhibiting algal growth through allelopathy.
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Book chapters on the topic "Vegetated water bodies"

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Belete, Mulugeta Dadi. "Characterization of vegetated riparian buffer zones as the last line of defense for protecting water bodies from degradation." In Ecohydrology-Based Landscape Restoration, 126–39. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003309130-7.

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Conference papers on the topic "Vegetated water bodies"

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Lodhi, Mahtab A. "Analysis of spectral signals emanating from vegetated and nonvegetated substrates in shallow water bodies." In Photonics East '99, edited by Khalid J. Siddiqui and DeLyle Eastwood. SPIE, 1999. http://dx.doi.org/10.1117/12.372884.

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