scholarly journals DETEKSI KEBAKARAN HUTAN DAN LAHAN MENGGUNAKAN CITRA SATELIT HIMAWARI-8 DI KALIMANTAN TENGAH

2020 ◽  
Vol 20 (2) ◽  
pp. 79-89
Author(s):  
Alpon Sepriando ◽  
Hartono Hartono ◽  
Retnadi Heru Jatmiko
Keyword(s):  
Temporal Resolution ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Image Data ◽  
Observation Time ◽  
Omission Error ◽  
Commission Error ◽  
Fire Monitoring ◽  
Active Fire ◽  
Imagery Data

IntisariKebakaran hutan dan lahan terjadi hampir setiap tahun di Indonesia, terutama di wilayah Sumatera dan Kalimantan saat musim kemarau. Deteksi kebakaran hutan dan lahan dengan citra satelit menggunakan indikator yang disebut titik panas. Titik panas yang digunakan saat ini di Indonesia diperoleh dari pengolahan data citra satelit berorbit polar (MODIS dan VIIRS) dengan resolusi temporal yang rendah, yaitu hanya 6 kali dalam sehari. Tujuan dari penelitian ini adalah memanfaatkan data citra satelit Himawari-8 untuk deteksi kebakaran hutan dan lahan yang menghasilkan titik panas dengan resolusi temporal 10 menit, dimana hasilnya di validasi dengan citra polar dan data kebakaran lapangan. Lokasi penelitian berada di Provinsi Kalimantan Tengah dan waktu penelitian adalah bulan September 2019. Data yang digunakan untuk pengolahan adalah 5 saluran Advanced Himawari Imager, peta batas administrasi dan tutupan lahan. Pemrosesan data citra satelit mencakup pemilihan piksel penutup lahan dan batas administrasi, penentuan waktu pengamatan, eliminasi piksel awan, Algoritma Pemantau Kebakaran Aktif, dan validasi hasil. Data citra Himawari-8 dapat diolah menjadi titik panas dengan temporal 10 menit. Validasi terhadap citra polar memiliki tingkat akurasi 66,2%-75,4%, comission error 28,2-46,9% dan omission error 24,6-33,8%. Tingginya comision error terhadap citra VIIRS dikarenakan citra VIIRS memiliki resolusi spasial yang jauh lebih tinggi dibandingkan dengan citra Himawari-8.  AbstractForest and land fires occur almost every year in Indonesia, especially in Sumatra and Kalimantan during the dry season. Detection of forest and land fires with satellite imagery uses an indicator called a hotspot. The hotspots used today in Indonesia are obtained from the processing of polar orbital satellite image data (MODIS and VIIRS) with a low temporal resolution, which is only six times a day. The purpose of this study is to utilize Himawari-8 satellite imagery data for the detection of forest and land fires that produce hotspots with a temporal resolution of 10 minutes, where the results are validated with polar imagery and field fire data. The research location is in Central Kalimantan Province, and the time of the study is September 2019. Data used for processing are 5 Advanced Himawari Imager channels, administrative boundary maps, and land cover. Processing of satellite imagery data includes the selection of cover pixels and administrative boundaries, determination of observation time, elimination of cloud pixels, Active Fire Monitoring Algorithm, and validation of results. Himawari-8 image data can be processed into hotspots with a temporal 10 minutes. Validation of polar images has an accuracy rate of 66.2% -75.4%, commission error 28.2-46.9% and omission error 24.6-33.8%. The high commission error on the VIIRS image is because the VIIRS image has a much higher spatial resolution compared to the Himawari-8 image. 

Assessment of Multiplatform Satellite Image Frequency for Crop Health Monitoring

2020 ◽  
Author(s):  
Valeriy Kovalskyy ◽  
Xiaoyuan Yang
Keyword(s):  
Satellite Imagery ◽  
Health Monitoring ◽  
Critical Role ◽  
Satellite Image ◽  
Data Sources ◽  
Percent Cover ◽  
Digital Agriculture ◽  
Crop Health ◽  
Image Frequency ◽  
Imagery Data

<p>Imagery products are critical for digital agriculture as they help delivering value and insights to growers. Use of publicly available satellite data feeds by digital agriculture companies helps keeping imagery services affordable for broader base of farmers. Optimal use of public and private imagery data sources plays a critical role in the success of image based services for agriculture. </p><p>At the Climate Corporation we have established a program focused on intelligence about satellite image coverage and frequency expected in different geographies and times of the year which is becoming critical for global expansion of the company. In this talk we report the results of our analysis on publicly available imagery data sources for key agricultural regions of the globe. Also, we demonstrate how these results can guide commercial imagery acquisition decisions on the case study in Brazil, where some growers run the risk of going through the growing season without receiving imagery from one satellite if relying on a single source of satellite imagery. The study clearly shows the validity of approaches taken as the results matched with factual image deliveries to single digits of percent cover on regional level. Also, our analysis clearly captured realistic temporal and spatial details of chances in image frequency from addition of alternative satellite imagery sources to the production stream. The optimization in imagery acquisitions enables filling data gaps for research and development. In the meantime, it contributes to delivering greater value for growers in Crop Health Monitoring and other image based service. </p>

scholarly journals Review of topographic analysis methods for the western Himalaya using AWiFS and MODIS satellite imagery

2010 ◽  
Vol 51 (54) ◽  
pp. 153-160 ◽  
Author(s):  
V.D. Mishra ◽  
J.K. Sharma ◽  
R. Khanna
Keyword(s):  
Satellite Imagery ◽  
Rayleigh Scattering ◽  
Visual Analysis ◽  
Satellite Image ◽  
Image Data ◽  
Topographic Effects ◽  
Topographic Analysis ◽  
Mountainous Regions ◽  
In Situ Observations

AbstractThe topographic effects of differential terrain illumination in optical satellite imagery of rugged mountainous regions have serious consequences for qualitative and quantitative analysis for various snow applications. Therefore, effective removal or minimization of topographic effects is necessary in satellite image data of mountainous regions. Different methods for topographic corrections, including C-correction, Minnaert corrections (including slope) and slope-matching method, are analysed in the context of snow reflectance. Combination of dark-object subtraction models DOS1 and DOS3 is used for image-based atmospheric corrections while considering the effect of Rayleigh scattering on the transmissivity in different spectral bands of AWiFS and MODIS image data. The performance of different models is evaluated using (1) visual analysis, (2) change in snow reflectance on sunny and shady slopes after the corrections, (3) validation with in situ observations and (4) graphical analysis. The results show that the slope-matching technique could eliminate most of the shadowing effects in Himalayan rugged terrain and correctly estimate snow reflectance from AWiFS and MODIS imagery. The validation of results with in situ observations for both types of imagery suggests that all other methods significantly underestimate reflectance values after the corrections.

scholarly journals Clustering of Trees from Panchromatic Images

2019 ◽  
Vol 8 (6) ◽  
pp. 75-77
Keyword(s):  
Satellite Imagery ◽  
Satellite Image ◽  
Image Data ◽  
Tree Crown ◽  
Crown Height ◽  
Tree Crowns ◽  
Clustering Model ◽  
Automatic Clustering ◽  
Proposed Model ◽  
Segmentation Models

Tree Clustering from satellite images assists in ecological environmental protection. It also helps in managing green resources to provide sustainable development guidance. The automatic clustering of trees is a challenging task. Many models tend to give poor results when there is noise in the image. The aim is to propose a model for clustering of tree crown from panchromatic satellite image using image processing algorithms. In the proposed model we use Cartosat-2 satellite data and the image data is pre-processed to enhance the resulted image analyzed using segmentation models. The resulted image is trained using the clustering model which classifies the tree crowns from the panchromatic images. The proposed model can be able to classify tree crowns effectively from satellite imagery. The proposed model also calculates the tree crown height and width from satellite imagery.

scholarly journals ANALYSIS OF CHANGES IN MANGROVE FOREST FERTILITY USING SATELLITE IMAGE DATA AND WILCOXON TEST

CI-TECH ◽  
2021 ◽  
Vol 2 (01) ◽  
pp. 25-29
Author(s):  
Siti Zainab ◽  
Hendrata Wibisana
Keyword(s):  
Regression Model ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Image Data ◽  
Storm Surges ◽  
Wilcoxon Test ◽  
Landsat 8 ◽  
Significant Difference ◽  
Exponential Regression Model ◽  
The City

Gunung Anyar is one of the districts in the city of Surabaya. This district has a height of approximately 3 meters above sea level. Based on data from the Central Statistics Agency (BPS) for the City of Surabaya 2019, Gunung Anyar District has an area of ​​9.2 square kilometers and is divided into four sub-districts. These include the Kelurahan Rungkut Menanggal, Rungkut Tengah, Mount Anyar and Mount Anyar Tambak (AyoSurabaya.com by Rizma Riyandi). The mangrove's robust root system helps form a natural barrier against storm surges and flooding. River and land sediments are trapped by roots, which protect shorelines and slow erosion. This filtering process also prevents harmful sediments from reaching coral reefs and seagrass beds (Anugerah Ayu Sundari 2019). The method used by remote sensing with Landsat 8 satellite imagery was analyzed using SeaDAS software, it was obtained that the comparison value in each band 2,3,4 and band 5 had differences in each reflectance value. The 2015 satellite image map has the largest value in band_4 with the exponential regression model y = 125.06e-22.13x with R2 = 0.0732, while the 2019 satellite image map which has the largest value is band_4 with the logarithmic regression model y = 141.72ln (x) + 326.3 where R2 = 0.0281. Using the Wilcoxon H1 Test Statistics it is accepted that there is a significant difference between the diameter of mangroves from satellite imagery in 2015 and the diameter of mangroves from satellite images in 2019. Because the number of positive rankings from the diameter of mangrove satellite imagery in 2015 is greater than the diameter of mangroves from satellite imagery in 2019. , it can be concluded that the mangrove area of ​​Wonorejo Surabaya is experiencing fertility.

scholarly journals Symmetry perception with spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan K. George ◽  
Cesare Soci ◽  
Mario Miscuglio ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Mirror Symmetry ◽  
Satellite Image ◽  
Image Data ◽  
Coincidence Detection ◽  
Spiking Neural Networks ◽  
Feed Forward Neural Network ◽  
Neuromorphic Computing ◽  
Symmetry Perception ◽  
High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

scholarly journals The Utilization of Satellite Imagery Data to Predict Hydrology Characteristics in Dodokan Watershed

2016 ◽  
Vol 33 ◽  
pp. 36-43 ◽  
Author(s):  
Sri Malahayati Yusuf ◽  
Kukuh Murtilaksono ◽  
Mahendra Harjianto ◽  
Endah Herlina
Keyword(s):  
Satellite Imagery ◽  
Imagery Data

Building Detection in Earthquake Prone Urban Area with Satellite Imagery Data Using Deep Learning

2021 ◽  
Author(s):  
Edy Irwansyah ◽  
Alexander A. Santoso. Gunawan ◽  
Calvin Surya ◽  
Dewa Ayu Defina Audrey Nathania
Keyword(s):  
Deep Learning ◽  
Urban Area ◽  
Satellite Imagery ◽  
Building Detection ◽  
Imagery Data

Resolving pre-collapse slope motion at the February 2021 Chamoli rock-ice avalanche via feature tracking of optical satellite imagery

2021 ◽  
Author(s):  
Maximillian Van Wyk de Vries ◽  
Shashank Bhushan ◽  
David Shean ◽  
Etienne Berthier ◽  
César Deschamps-Berger ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Feature Tracking ◽  
Satellite Image ◽  
Image Feature ◽  
Sampling Distance ◽  
Velocity Changes ◽  
The Stability ◽  
Slide Area ◽  
Ice Avalanche

<p>On the 7<sup>th</sup> of February 2021, a large rock-ice avalanche triggered a debris flow in Chamoli district, Uttarakhand, India, resulting in over 200 dead or missing and widespread infrastructure damage. The rock-ice avalanche originated from a steep, glacierized north-facing slope with a history of instability, most recently a 2016 ice avalanche. In this work, we assess whether the slope exhibited any precursory displacement prior to collapse. We evaluate monthly slope motion over the 2015 and 2021 period through feature tracking of high-resolution optical satellite imagery from Sentinel-2 (10 m Ground Sampling Distance) and PlanetScope (3-4 m Ground Sampling Distance). Assessing slope displacement of the underlying rock is complicated by the presence of glaciers over a portion of the collapse area, which display surface displacements due to internal ice deformation. We overcome this through tracking the motion over ice-free portions of the slide area, and evaluating the spatial pattern of velocity changes in glaciated areas. Preliminary results show that the rock-ice avalanche bloc slipped over 10 m in the 5 years prior to collapse, with particularly rapid slip occurring in the summer of 2017 and 2018. These results provide insight into the precursory conditions of the deadly rock-ice avalanche, and highlight the potential of high-resolution optical satellite image feature tracking for monitoring the stability of high-risk slopes.</p>

Sign in / Sign up

Export Citation Format

Share Document