scholarly journals The Determination of Rainfall Threshold Triggering Landslides Using Remote Sensing

2021 ◽  
Vol 893 (1) ◽  
pp. 012011
Author(s):  
L Agustina ◽  
A Safril
Keyword(s):  
Remote Sensing ◽  
Rainfall Intensity ◽  
Nearest Neighbor ◽  
Remote Sensing Data ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Bilinear Method ◽  
Sensing Data ◽  
Central Java ◽  
Hourly Rainfall

Abstract Landslide is one of the natural disasters that can cause a lot of loss, both material and fatalities. Banjarnegara Regency is one of Central Java Province regencies where landslides often occur due to the region's topography and high intensity rainfall.. Therefore, it is necessary to determine the threshold of rainfall that can trigger landslides to be used as an early warning for landslides. The rainfall data used for the threshold is daily and hourly rainfall intensity from remote sensing data that provides complete data but relatively rough resolution. So that remote sensing data need to be re-sampled. The remote sensing data used is CMORPH satellite data that has been re-sampled for detailing existing information of rainfall data. The resampling method used is the bilinear method and nearest neighbor by choosing between the two based on the highest correlation. Threshold calculation using Cumulative Threshold (CT) method resulted equation P3 = 7.0354 - 1.0195P15 and Intensity Duration (ID) method resulted equation I = 1.785D-0305. The peak rainfall intensity occurs at the threshold of 97-120 hours before a landslide occur.

A feasibility of six-hourly rainfall forecast over central India using model output and remote sensing data

2012 ◽  
Vol 2 (2) ◽  
pp. 138 ◽  
Author(s):  
Satya Prakash ◽  
C. Mahesh ◽  
Rakesh Mohan Gairola ◽  
Batjargal Buyantogtokh
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Central India ◽  
Model Output ◽  
Sensing Data ◽  
Rainfall Forecast ◽  
Hourly Rainfall

K Nearest Neighbor Method for Forest Inventory Using Remote Sensing Data

2007 ◽  
Vol 44 (2) ◽  
pp. 149-165 ◽  
Author(s):  
Qingmin Meng ◽  
Chris J. Cieszewski ◽  
Marguerite Madden ◽  
Bruce E. Borders
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Nearest Neighbor ◽  
Remote Sensing Data ◽  
K Nearest Neighbor ◽  
Sensing Data

A Hybrid Classification Approach Based on Support Vector Machine and K-Nearest Neighbor for Remote Sensing Data

2017 ◽  
Vol 31 (10) ◽  
pp. 1750034 ◽  
Author(s):  
Gulnaz Alimjan ◽  
Tieli Sun ◽  
Hurxida Jumahun ◽  
Yu Guan ◽  
Wanting Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Remote Sensing Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Sensing Data ◽  
Knn Classifier ◽  
Training Samples ◽  
Hybrid Classification

Analysis and classification for remote sensing landscape based on remote sensing imagery is a popular research topic. In this paper, we propose a new remote sensing data classifier by incorporating the support vector machine (SVM) learning information into the K-nearest neighbor (KNN) classifier. The SVM is well known for its extraordinary generalization capability even with limited learning samples, and it is very useful for remote sensing applications as data samples are usually limited. The KNN has been widely used in data classification due to its simplicity and effectiveness. However, the KNN is instance-based and needs to keep all the training samples for classification, which could cause not only high computation complexity but also overfitting problems. Meanwhile, the performance of the KNN classifier is sensitive to the neighborhood size [Formula: see text] and how to select the value of the parameter [Formula: see text] relies heavily on practice and experience. Based on the observations that the SVM can contribute to the KNN on the problems of smaller training samples size as well as the selection of the parameter [Formula: see text], we propose a support vector nearest neighbor (abbreviated as SV-NN) hybrid classification approach which can simplify the parameter selection while maintaining classification accuracy. The proposed approach is consist of two stages. In the first stage, the SVM is performed on the training samples to obtain the reduced support vectors (SVs) for each of the sample categories. In the second stage, a nearest neighbor classifier (NNC) is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the NNC identifies the category with minimum distance. To evaluate the effectiveness of the proposed approach, firstly experiments of classification for samples from remote sensing data are evaluated, and then experiments of identifying different land covers regions in the remote sensing images are evaluated. Experimental results show that the SV-NN approach maintains good classification accuracy while reduces the training samples compared with the conventional SVM and KNN classification model.

scholarly journals KARAKTERISASI RESPON SPEKTRAL SPESIES LAMUN Enhalus acoroides DAN Cymodocea rotundata DI PULAU KARIMUNJAWA

2017 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Pramaditya Wicaksono
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Spectral Response ◽  
Remote Sensing Data ◽  
Seagrass Species ◽  
Sensing Data ◽  
Enhalus Acoroides ◽  
Central Java ◽  
Seagrass Mapping ◽  
Cymodocea Rotundata

<p class="JudulABSInd"><strong>                                                                                      ABSTRAK</strong></p><p class="abstrak">Pemahaman mengenai variasi respon spektral spesies lamun sangat berguna dalam menunjang keberhasilan aktivitas pemetaan sumberdaya alam padang lamun menggunakan penginderaan jauh. Penelitian ini bertujuan untuk melakukan inventarisasi pantulan spektral spesies lamun <em>Enhalus acoroides</em> (Ea) dan <em>Cymodocea rotundata</em> (Cr) pada berbagai kondisi, yaitu sehat, tertutup epifit dan rusak. Pengukuran pantulan spektral lamun dilakukan di Kepulauan Karimunjawa, Kabupaten Jepara, Jawa Tengah pada tanggal 4-6 April 2016. Pengukuran respon spektral spesies lamun dilakukan pada panjang gelombang 350-1100 nm menggunakan Jaz <em>Spectrometer</em> buatan OceanOptics. Hasil dari penelitian ini adalah berupa koleksi respon spektral kedua spesies lamun tersebut pada berbagai kondisi, yang dapat digunakan sebagai dasar untuk menentukan: 1) panjang gelombang yang sesuai untuk memisahkan spesies tersebut secara spektral; 2) panjang gelombang yang sesuai untuk melakukan pemetaan variasi kondisi spesies tersebut; dan 3) langkah awal dalam pembuatan pustaka spektral padang lamun dan habitat bentik di Indonesia. Dari hasil penelitian ini dapat disimpulkan bahwa pada julat panjang gelombang 650–690 nm dapat digunakan untuk membedakan lamun menjadi tiga kelas yaitu 1) <em>Ea</em> rusak, 2) <em>Ea</em> tertutup epifit dan 3) <em>Ea</em> sehat, <em>Cr</em> sehat, dan <em>Cr</em> tertutup epifit. Pada saluran NIR antara 733–888 nm, kelima kelas tersebut dapat dibedakan meskipun akan sulit untuk membedakan Kelas <em>Ea</em> rusak dan <em>Cr</em> ber-epifit. Untuk <em>Ea</em> dan <em>Cr</em> sehat, respon spektralnya berbeda hampir pada semua panjang gelombang kecuali pada 650–730 nm dan kurang dari 480 nm. <strong></strong></p><p><strong>Kata kunci</strong>: respon spektral, <em>Enhalus acoroides, Cymodocea rotundata</em></p><p class="judulABS"><em><strong>                                                                                      ABSTRACT</strong></em></p><p class="Abstrakeng"><em>Understanding spectral variations of seagrass species is important for the success of seagrass mapping activities using remote sensing data. The aim of this research is to collect Enhalus acoroides and Cymodocea rotundata spectral response using field spectrometer at different conditions, i.e. healthy, covered by epiphyte, and dead. The field measurement of seagrass spectral response was conducted in Karimunjawa Islands, Central Java, Indonesia on 4 – 6 April 2016. Jaz spectrometer from OceanOptics was used to collect seagrass spectral response at 350-1100 nm. The results of this research is the collection of seagrass spectral response at different conditions, which is highly important and beneficial for determining: 1) the most suitable spectral wavelength to differentiate these seagrass species; 2) the most effective spectral wavelength to map seagrass condition variations using remote sensing data; and 3) the initial spectral library of seagrass and benthic habitats in Indonesia. From this research, it can be concluded that wavelengths between 650-690 nm can be used to distinguish the seagrass into three classes: 1) dead Ea, 2) Ea covered by epiphytes, and 3) healthy Ea, healthy Cr, and Cr covered by epiphytes. In the near infrared wavelenghts between 733-888 nm, these five classes can be distinguished although it would be difficult to distinguish dead Ea and Cr covered by epiphytes. For healthy Ea and Cr, their spectral response is different in nearly all wavelengths except at 650-730 nm and less than 480 nm.</em></p><p><strong><em>Keywords</em></strong><em>: spectral response, Enhalus acoroides, Cymodocea rotundata</em><em> </em><em></em></p>

scholarly journals Application of Remote Sensing and Geoinformatics Techniques in Erosion Mapping and Groundwater Management in the River Amba Watershed, Central Nigeria

2021 ◽  
pp. 59-65
Author(s):  
Aliyu Itari Abdullahi ◽  
Nuhu Degree Umar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Groundwater Management ◽  
Rainfall Intensity ◽  
Flow Direction ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Aerial Photographs ◽  
Sensing Data ◽  
Geoinformatics Techniques

This research integrated easy-to-handle remote sensing data and geoinformatics techniques for erosion mapping and groundwater management in the River Amba watershed, central Nigeria. It is aimed at: (a) the determination of the erosion-prone areas and (b) the estimation of the groundwater potential contamination risk under current and future anthropogenic activities. Rainfall intensity was evaluated from monthly rainfall data (2001 - 2011) from the station located within the River Amba Watershed. Digital Elevation Model (DEM) for the terrain was created using the 3D Analyst tool (Surfer 14) and was used to determine the flow direction and lineament features in each raster cells. Remote sensing data (aerial photographs and LANDSAT imagery) were used to develop a land-use map, while geological mapping was used to determine the local geology of the watershed area. The contributions of the various factors to the erosion hazardous areas are: elevation 31.49 %, land use 21 %, slope 14 %, geology 12.52 %, rainfall intensity 10.5 % and flow accumulation 10.5 %. The combined influences of these factors to erosion susceptibility as either: very high, high, moderate, low, and very low with the south-western part characterized as high while other parts of the study area moderate to very low erosion vulnerability. The groundwater level is shallow (4.0 –28.5 m) and discharges through the Amba river and many springs. These springs along with boreholes and wells supply drinking water to Lafia and the environs.

Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies

Agronomie ◽  
2002 ◽  
Vol 22 (2) ◽  
pp. 205-215 ◽  
Author(s):  
Bruno Combal ◽  
Fr�d�ric Baret ◽  
Marie Weiss
Keyword(s):  
Remote Sensing ◽  
Sugar Beet ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Ancillary Information

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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