Deteksi Tinggi Vegetasi di Delta Mahakam dengan Penginderaan Jauh

Anggraini, Nanin; Julzarika, Atriyon

doi:10.14203/oldi.2019.v4i3.212

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…This interference can be referred to as "noise" and cannot be used for analyzing the spectral characteristics of vegetation reflectance; therefore, the data from the noise are ignored. 80 The accuracy test results showed an OA of 84.51% with 71 field-observation sample points. Mangrove management, conservation, and restoration depend on accurate mapping of the quality, distribution, and number of species.…”

Section: Best Model For Mangrove Species Classificationmentioning

confidence: 93%

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Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Simarmata,

Wikantika,

Darmawan

et al. 2024

J. Appl. Rem. Sens.

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Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas are located in East and South Lampung, Indonesia. The field samples used represented 144 points of mangrove species. The classification method used an RF algorithm and four models with varying parameters: model 1 with Sentinel-2; model 2 with both Sentinel-2 and field spectral data; model 3 with Sentinel-2, field spectral data, and spectrally transformed data; and model 4 only with spectrally transformed data. The results showed that Rhizophora mucronata, Sonneratia alba, Avicennia lanata, and Avicennia marina were the most common mangrove species in these areas, with reflectance values in the range of 0.002 to 0.493, 0.006 to 0.833, 0.014 to 0.768, and 0.002 to 0.758. Permutation importance (PI) that affects the classification model is the red band, near-infrared, and green normalized difference vegetation index, where the most PI in model 3 is 0.283. The highest level of agreement for mangrove species is found in model 3. Model 3 is the best parameter for RF classification that showed the best mapping accuracy, with the overall accuracy, user accuracy, producer accuracy, and kappa value being 81.25%, 81.68%, 81.25%, and 0.80, respectively.

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Section: Best Model For Mangrove Species Classificationmentioning

confidence: 93%

“…A factor influencing the intensity of sunlight that the sensor can receive is the method of measuring samples in the field, such as the location of the measured leaf parts, field conditions, and density of each mangrove tree. 80 Figure 5 illustrates the box plots of the reflectance values of mangrove species.…”

Section: Spectral Library Of Mangrove Speciesmentioning

confidence: 99%

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Simarmata,

Wikantika,

Darmawan

et al. 2024

J. Appl. Rem. Sens.

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“…The Fill Sink method has an advantage in addressing high-value anomalies in valley areas. These gaps can be eliminated using GIS features [12]. Flow direction determines the flow direction from each raster cell.…”

Section: Geomorphic Flood Indexmentioning

confidence: 99%

Flood hazard comparison based on geomorphic flood index and hydraulic HEC-RAS (Case study in Ciliwung Watershed, Jakarta)

Pratiwi,

Rohmat,

Nugroho

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The Jakarta Special Capital Region is a highly vulnerable area to floods due to its location on wetlands laced by 13 major rivers and borders the Java Sea, with more than 40% of its land below sea level and groundwater extraction leading to the ground sinking. The city has experienced several major flood disasters, and climate change has increased the risk, frequency, and severity of flooding in Jakarta. The present study aims to identify areas with flood potential in the Jakarta Special Capital Region (Provinsi DKI Jakarta) using the Geospatial Flood Index (GFI) method recommended by the National Disaster Management Agency (BNPB). The GFI method is an alternative rapid assessment that utilizes the Digital Elevation Model for National Spatial (DEMNAS) data with an 8-meter spatial resolution and employs ARCGIS and QGIS software to identify areas with high potential for flooding and the extent of inundation. To assess the effectiveness of this method, a comparison is made with the Hydraulic HECRAS model for the section from the Automatic Water Level Recorder (AWLR) MT. Hartono to the Manggarai Flood Gate, considering flood return periods Q50 and Q100. The modeling results indicate that the inundation area estimated by the conventional GFI modeling is 150% larger than that the HECRAS Hydraulic model predicted.

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“…The Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is a shortwave Lband sensor for cloudless land observation. Data recording was carried out during the day and at night (Anggraini et al, 2019). The data were processed into slope data using ArcToolbox 3D Analyst Tools on the software ArcGIS (Islam et al, 2014).…”

Section: Land Slopementioning

confidence: 99%

A Modelling and Mitigation Strategy for the Impact of the Tsunami Disaster at Tamban Beach, Malang Regency, using a Geographic Information System

Rudianto¹,

Widodo²,

Guntur³

et al. 2022

EEC

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Tsunami events are caused by a series of waves resulted from sudden changes in the water column, seismicactivities such as the eruption of underwater volcanoes,landslides on cliffs above or below the sea, andfalling asteroids. The Meteorology, Climatology, and eophysics Agency released a warning of a potentialearthquake in the southern part of Java Island, including the Malang Regency, with a magnitude of 8.7 onthe Richterâs scale. It means that a tsunami with amaximum inundation height of up to 22 meters coulddestroy Tamban Beach. The objectives of this study were1)to examine the spatial aspect of Tamban Beachand its relevance to the ecological aspect as well as thetime aspect related to the estimation,2) to formulatea strategy to save the community, and 3) to estimate the impact of the damage to cover and land use. Themethods used were a scoring technique and an overlay analysis using a Geographic Information System(GIS), with parameters including the distance to the coast and river from the sea, slope, elevation, land use,tsunami run-up prediction, and inundation model. The Partial Least Squares (PLS) method was used todetermine the relationship between the level of vulnerability of an area to the tsunami disaster with variousassessments and efforts to mitigate the disaster. The results showed that most of the coastal areas of TambanBeach had a moderate level of vulnerability, except for the residential areas that had a high level ofvulnerability. A tsunami mitigation strategy was significantly influenced by the areaâs level of vulnerabilityto a disaster and the assessment of disaster management efforts.

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Deteksi Tinggi Vegetasi di Delta Mahakam dengan Penginderaan Jauh

Cited by 4 publications

References 3 publications

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

Flood hazard comparison based on geomorphic flood index and hydraulic HEC-RAS (Case study in Ciliwung Watershed, Jakarta)

A Modelling and Mitigation Strategy for the Impact of the Tsunami Disaster at Tamban Beach, Malang Regency, using a Geographic Information System

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