scholarly journals Mapping the Condition of Seagrasses Beds in Ternate -Tidore Waters, and Surrounding Areas

2016 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Simon I. Patty
Keyword(s):  
Image Analysis ◽  
Spatial Distribution ◽  
Field Test ◽  
Health Conditions ◽  
Landsat 8 ◽  
Seagrass Beds ◽  
Coastal Systems ◽  
Seagrass Cover ◽  
Water Ecosystems ◽  
Surrounding Areas

Seagrass beds is one of the most prolific shallow water ecosystems, having ecological function in the life of the various marine organisms and other coastal systems. Data and information of seagrass condition in the waters of Ternate, Tidore and surrounding areas are still hardly unexplored. This study aimed to describe the spatial distribution information of seagrass cover percentage, seagrass conditions and environmental characteristics. The basic data used for mapping of seagrass is Landsat 8 on a path 110 row 59 recordings in July 2015. Analysis of overlaying and the interpretation of the seagrass distribution software using "ERMapper, Image Analysis 1.1 on ArcGIS ArcView 3.2 and 10.1". Field test was conducted on frame 50 x 50 cm squares, each square of the recorded species of seagrasses and cover percentage value. Condition assessment based on seagrass cover by (Rahmawati et al., 2014) and (KMLH, 2004). The results show that there are eight species of seagrass found in the waters of the island of Ternate, Tidore and Hiri Maitara island. The highest percentage in the seagrass cover was found in Maitara islands and Hiri Island, i.e ≥ 50%. Seagrass cover conditions in general are relatively "moderate", but the health conditions are less healthy / less wealthy (30 to 59.9%). Keywords: Seagrass beds, seagrass conditions, mapping, satelite image ABSTRAK Padang lamun merupakan salah satu ekosistem perairan dangkal yang paling produktif, mempunyai  fungsi ekologis dalam kehidupan berbagai organisme laut dan sistem pesisir lainnya.  Informasi  data  padang  lamun  di  perairan Ternate, Tidore dan sekitarnya masih belum tereksplorasi dengan baik. Penelitian ini bertujuan  mendeskripsikan  informasi  secara  spasial  sebaran  lamun,  persentase  tutupan, kondisi lamun dan karakteristik lingkungannya. Data dasar yang digunakan untuk pemetaan padang lamun adalah citra Landsat 8 pada path 110 row 59 rekaman Juli  2015. Analisis tumpang susun dan interpretasi sebaran lamun dengan menngunakan perangkat lunak “Ermapper, Image Analysis 1.1 pada ArcView 3.2 dan “ArcGIS 10.1”. Uji lapangan dilakukan pada frame kuadrat 50 x 50 cm, disetiap kuadrat dicatat jenis lamun dan nilai persentase tutupan.  Penilaian kondisi lamun berdasarkan tutupan menurut (Rahmawati dkk., 2014) dan (KMLH, 2004). Hasilnya menunjukkan bahwa  terdapat 8 jenis lamun yang ditemukan di perairan pulau Ternate, pulau Tidore, pulau Hiri dan pulau Maitara. Presentase tutupan lamun tertinggi terdapat di pulau Maitara dan pulau Hiri yaitu ≥ 50 %. Kondisi lamun pada umumnya memiliki tutupan tergolong “sedang”, namun kondisinya kurang sehat/kurang kaya (30-59,9%). Kata kunci: Padang lamun, kondisi lamun, pemetaan, citra satelit   1 Proyek Penelitian RHM-COREMAP, 2015 2 UPT. Loka Konservasi Biota Laut Bitung-LIPI

scholarly journals STRUKTUR KOMUNITAS LAMUN DI MALANG RAPAT, BINTAN

2019 ◽  
Vol 4 (2) ◽  
pp. 148-159
Author(s):  
Wahyu Adi ◽  
Aditya Hikmat Nugraha ◽  
Yehiel Hendry Dasmasela ◽  
Agus Ramli ◽  
Calvyn Fredrik Aldus Sondak ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Line Transect ◽  
Percent Cover ◽  
Seagrass Beds ◽  
Seagrass Species ◽  
Substrate Type ◽  
Thalassia Hemprichii ◽  
Enhalus Acoroides ◽  
Present Information ◽  
Seagrass Cover

Padang lamun menyediakan banyak manfaat. Diperlukan penilaian tutupan lamun dan distribusi spasial spesies lamun, untuk memastikan manfaat dari padang lamun tetap ada. Penelitian ini dilakukan dengan metode kuadran disepanjang line transect, dan analisis data dilakukan untuk dapat menyajikan informasi tentang titik koordinat transek garis, persen tutupan lamun, persen tutupan spesies lamun, jenis substrat (pasir, lumpur dan pecahan karang), jumlah spesies Enhallus acoroides (tegakan/m2) dan distribusi spasial spesies lamun. Hasil penelitian menunjukkan kondisi padang lamun di wilayah studi sebagai kategori tutupan padat. Spesies lamun Thalassia hemprichii (Th) dan Enhalus acoroides (Ea) adalah spesies dominan dan menyebar ke seluruh wilayah penelitian.COMMUNITY STRUCTURE OF SEAGRASS IN MALANG RAPAT, BINTAN. Seagrass beds provide many benefits. Assessment of seagrass cover and spatial distribution of seagrass species is needed, to ensure the benefits of seagrass beds remain. The study was conducted using the quadratic method on the line transect, and the analysis was carried out to be able to present information about line transect coordinate points, percent cover of seagrass, percent cover of seagrass species, substrate type (sand, mud and rubble), number of species of Enhallus acoroides (shoot/m2) and spatial distribution of seagrass species. The results showed the condition of seagrass beds in the study area as a category of solid cover. Seagrass species Thalassia hemprichii (Th) and Enhalus acoroides (Ea) are dominant species and spread throughout the study area.

Field Test Research on Environmental Noise Characteristics in the Throat Area of Metro Depot

2021 ◽  
Vol 263 (5) ◽  
pp. 1818-1822
Author(s):  
Lei He ◽  
Ruixiang Song ◽  
Jie Yang ◽  
Yubin Wu ◽  
Yanan Wu
Keyword(s):  
Field Test ◽  
Open Space ◽  
Noise Pollution ◽  
Environmental Noise ◽  
Statistical Characteristics ◽  
Small Radius ◽  
Time Frequency ◽  
Noise Characteristics ◽  
Train Operation ◽  
Surrounding Areas

Environmental noise pollution is the primary environmental problem faced by the superstructure of metro depots. The throat area of depot is prone to high wheel-rail impact noise due to the use of seam lines, multiple joints, turnouts and small radius curves. The noise exerts through fire and ventilation openings on the side walls, which may cause high annoyance to the residents in the superstructures both on the upper cover and surrounding areas. In this paper, a field test was condected on the environmental noise in the throat area of metro depot. The noise of the trackside and adjacent open space were recorded, and the time-frequency domain characteristics and statistical characteristics and attenuation law of the noise generated by train operation in the throat area were analyzed. The research results have certain guiding significance for the prediction and control of noise in the throat area of the depot.

scholarly journals Lineament automatic extraction analysis for Galal Badra river basin using Landsat 8 satellite image

2019 ◽  
Vol 12 (25) ◽  
pp. 44-55 ◽  
Author(s):  
Safaa Sabah Adhab
Keyword(s):  
Program Analysis ◽  
Satellite Image ◽  
Landsat 8 ◽  
Automatic Extraction ◽  
Lineament Density ◽  
Density Analysis ◽  
Lineament Analysis ◽  
Intersection Density ◽  
Surrounding Areas ◽  
The Relationship

This research including lineament automated extraction by using PCI Geomatica program, depending on satellite image and lineament analysis by using GIS program. Analysis included density analysis, length density analysis and intersection density analysis. When calculate the slope map for the study area, found the relationship between the slope and lineament density.The lineament density increases in the regions that have high values for the slope, show that lineament play an important role in the classification process as it isolates the class for the other were observed in Iranian territory, clearly, also show that one of the lineament hit shoulders of Galal Badra dam and the surrounding areas dam. So should take into consideration the lineaments because its plays an important role in the study area.

scholarly journals MAPPING OF THE SEAGRASS COVER ALONG THE MEDITERRANEAN COAST OF TURKEY USING LANDSAT 8 OLI IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1103-1105 ◽  
Author(s):  
T. Bakirman ◽  
M. U. Gumusay ◽  
I. Tuney
Keyword(s):  
Accuracy Assessment ◽  
Mediterranean Coast ◽  
Environmental Data ◽  
Benthic Habitat ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Carbon Dioxide Absorption ◽  
Landsat Images ◽  
Marine Animals ◽  
Seagrass Cover

Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5–10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.

scholarly journals Temporal evolution and spatial distribution of dust creation events in Tore Supra and in ASDEX Upgrade studied by CCD image analysis

2010 ◽  
Vol 50 (3) ◽  
pp. 035002 ◽  
Author(s):  
Suk-Ho Hong ◽  
Christian Grisolia ◽  
Volker Rohde ◽  
Pascale Monier-Garbet ◽  
◽  
...  
Keyword(s):  
Image Analysis ◽  
Spatial Distribution ◽  
Temporal Evolution ◽  
Ccd Image

Object-based image analysis of suburban landscapes using Landsat-8 imagery

2018 ◽  
Vol 12 (6) ◽  
pp. 720-736 ◽  
Author(s):  
Ming Shang ◽  
Shixin Wang ◽  
Yi Zhou ◽  
Cong Du ◽  
Wenliang Liu
Keyword(s):  
Image Analysis ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

2019 ◽  
Vol 11 (21) ◽  
pp. 2583 ◽  
Author(s):  
Payam Najafi ◽  
Hossein Navid ◽  
Bakhtiar Feizizadeh ◽  
Iraj Eskandari ◽  
Thomas Blaschke
Keyword(s):  
Image Analysis ◽  
Agricultural Research ◽  
Soil Surface ◽  
Control Measures ◽  
Landsat 8 ◽  
Surface Residue ◽  
Ground Truth Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2A

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

scholarly journals Modelling and quantifying the spatial distribution of post-wildfire ash loads

2016 ◽  
Vol 25 (2) ◽  
pp. 249 ◽  
Author(s):  
Chris J. Chafer ◽  
Cristina Santín ◽  
Stefan H. Doerr
Keyword(s):  
Spatial Distribution ◽  
Polynomial Regression ◽  
Spatial Scales ◽  
Fire Severity ◽  
Spectral Ratio ◽  
Landsat 8 ◽  
Applied Model ◽  
Sampling Locations ◽  
Before And After

Ash is generated in every wildfire, but its eco-hydro-geomorphic effects remain poorly understood and quantified, especially at large spatial scales. Here we present a new method that allows modelling the spatial distribution of ash loads in the post-fire landscape, based on a severe wildfire that burnt ~13 600 ha of a forested water supply catchment in October 2013 (2013 Hall Road Fire, 100 km south-west of Sydney, Australia). Employing an existing spectral ratio-based index, we developed a new spectral index using Landsat 8 satellite imagery: the normalised wildfire ash index (NWAI). Before- and after-fire images were normalised and a differenced wildfire ash image (dNWAI) computed. The relationship between dNWAI and ash loads (t ha−1) quantified in situ at nine sampling locations burnt under a range of fire severities was determined using a polynomial regression (R2 = 0.98). A spatially applied model was computed within a geographic information system (GIS) to illustrate the spatial distribution of ash across the area burnt and to estimate ash loads in the five subcatchments affected by the wildfire. Approximately 181 000 tonnes of ash was produced by the wildfire, with specific loads increasing with fire severity. This new tool to model wildfire ash distribution can inform decisions about post-fire land management in future wildfires in the region. It can also be adapted for its application in other fire-prone environments.

Sign in / Sign up

Export Citation Format

Share Document