CHANGE OF MANGROVE DENSITY BASED ON LANDSAT IMAGE CHARACTERISTIC IN COASTAL OF TANAH LAUT REGENCY

2017 ◽  
Vol 7 (2) ◽  
pp. 141
Author(s):  
Nur Salam ◽  
Muhammad Syahdan ◽  
Algui Sumas Ponaru
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Coastal Area ◽  
Landsat Image ◽  
Medium Density ◽  
Mangrove Vegetation ◽  
Long Time ◽  
Image Characteristic

Monitoring of mangrove vegetation in the long-time vulnerable is needed to determine its conversion as the first step of mangrove management. The purpose of this study mapped the spatial distribution of mangrove vegetation and discribed the change of mangrove vegetation area in the coastal area of Tanah Laut Regency for 12 years. The method was used remote sensing and method field obeservation. The mangrove density of 2003 is dominated by medium density and by 2015 is dominated by dense density. The area of mangrove density rarely increased by 375.87 ha. The area of density is decreasing the area of 1.454,16 ha. Density of density increased by 1,639.77 ha.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

Evaluating the spatial distribution and the intensity of urban heat island using remote sensing, case study of Isfahan city in Iran

2019 ◽  
Vol 45 ◽  
pp. 686-692 ◽  
Author(s):  
Niloufar Shirani-bidabadi ◽  
Touraj Nasrabadi ◽  
Shahrzad Faryadi ◽  
Adnan Larijani ◽  
Majid Shadman Roodposhti
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Urban Heat Island ◽  
Heat Island ◽  
Urban Heat

scholarly journals CULTURAL AND CHRONOLOGICAL ATTRIBUTION OF MIDDLE NEOLITHIC MATERIALS FROM THE SETTLEMENTS USHAKOVO 3 AND PRIBREZHNOYE

2020 ◽  
Author(s):  
Э.Б. Зальцман
Keyword(s):  
Coastal Area ◽  
Morphological Characteristics ◽  
Wall Structure ◽  
Vistula Lagoon ◽  
New Materials ◽  
Double Row ◽  
Long Time ◽  
Funnel Beaker Culture ◽  
The Baltic ◽  
The Vistula Lagoon

В работе характеризуются новые материалы, полученные в ходе исследований неолитических (по прибалтийской периодизации) поселений побережья Вислинского залива. Данные древности, по всем признакам, относятся к культуре воронковидных кубков, памятники которой ранее в регионе были неизвестны. Все материалы КВК выявлены на поселениях, основные культурные комплексы с которых относятся к приморской культуре шнуровой керамики (рис. 1). Незначительные по размерам стоянки КВК существовали здесь до прихода населения приморской культуры. В Ушаково 3 керамика КВК найдена в культурном слое в восточной части раскопа, где она залегала в основном отдельно от керамики приморской культуры (рис. 2–4). В Прибрежном кроме керамических материалов зафиксированы следы 2 построек с двухрядной столбовой конструкцией стен (рис. 5). Постройки наземного типа, удлиненной формы, шириной не более 3,2 м. Технологические и морфологические характеристики фрагментов керамики, обнаруженной в пределах построек, не оставляют сомнений в том, что эти комплексы принадлежат КВК (рис. 6: 1, 13). Кроме того, здесь же выявлены две амфоры с типичными чертами баденизации в КВК (рис. 6: 14, 15). Керамика КВК встречалась также и в культурном слое поселения (рис. 7: 1, 3, 4, 6, 7, 10, 12). Все материалы КВК с поселений Ушаково 3 и Прибрежное датируются в диапазоне 3500–3100 гг. CalBC (Приложение 1). Наиболее вероятно, что небольшие по численности группы населения КВК проникли в прибрежную зону около середины IV тыс. до н. э., когда на этой территории уже продолжительное время существовали местные сообщества цедмарской неолитической культуры. The paper characterizes new materials obtained during the excavations of Neolithic sites (according to the Baltic periodization) in the Vistula Lagoon coast. These antiquities are attributed to the Funnel Beaker culture the sites of which have not been discovered in this region before. All FBC materials were identified at settlements where the main cultural assemblages are attributed to the Primorskaya Corded Ware Culture (Fig. 1). Small FBC sites had existed in this area before the arrival of the Primorskaya culture population. Ushakovo 3 revealed FBC ceramics in the occupation layer located in the eastern part of the excavation trench where, in most cases, these artifacts were lying separately from the Primorskaya culture ceramics (Fig. 2–4). Traces of two constructions with a double-row pillar wall structure (Fig. 5) were recorded at Pribrezhnoye. Technological and morphological characteristics of the ceramics discovered in the constructions leave no doubt that these assemblages belong to the FBC (Fig. 6: 1, 13). Two amphorae with typical features of «badenization» in the FBC were discovered at this site as well (Fig. 6: 14, 15). The FBC ceramics also occurred in the occupation layer of the site (Fig. 7: 1, 3, 4, 6, 7, 10, 12). All FBC materials from Ushakovo 3 and Pribrezhnoye fall within 3500–3100 CalBC (Appendix 1). Most likely, around mid 4th millennium BC small FBC population groups reached the coastal area which had been already inhabited by local Zedmar Neolithic communities for a long time.

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