High-resolution Satellite Imagery Data Research Articles

Analisis Spasial Terhadap Tingkat Kerawanan Banjir DAS Kenali Kecil Menggunakan Metode SNI 8197:2015

Floods are the most common disasters in almost all places, including Indonesia and Jambi City in particular. Areas that are directly exposed to the threat of flooding are in the Watershed. Land conversion is one of the causes of flooding in the Kenali Kecil watershed. This study uses the standard SNI 8197:2015 regarding the Flood Hazard Mapping Method 1:50,000 and 1:25,000 scale. The approach used is landscape analysis, which emphasizes the process of forming natural systems on the earth's surface. This study aims to analyze and produce a map of the level of flood vulnerability in the Kenali Kecil watershed area. The data needed in this study are ten days of rainfall data, slope data and land cover data. Slope data was obtained from DEMNas data processing and land cover data was obtained from manual digitizing of Google Earth's High-Resolution Satellite Imagery data. Data processing uses ArcMap software with scoring and overlay techniques. The results showed that the Kenali Kecil watershed had a dendritic pattern with an area of 15.68 km2 and located in 5 villages, namely: Bagan Pete, Kenali Besar, Penyengat Rendah, Mendalo Darat and Pematang Gajah. The basic rainfall of the Kenali Kecil watershed is 204.21 mm, the slope is mostly >4% and the land cover is mostly residential. Based on the SNI 8197:2015 procedure and the results of the scoring and overlay process with ArcMap, the flood susceptibility level of the Kenali Kecil watershed is at a medium hazard level of 8.25 km2 (52.6%) and a height of 7.43 km2 (47.4%).

ANALISIS KUALITAS HASIL PREDIKSI KLASIFIKASI PENGGUNAAN LAHAN MENGGUNAKAN CA MARKOV MODEL BERDASARKAN PETA RENCANA TATA RUANG

Land use changes due to community activities and mobility occur because of the increasingly complex need for land. Spatial analysis is needed to identify land use changes which are subsequently reviewed by the Regional Spatial Plan in accordance with Government Regulation Number 8 of 2013 concerning the accuracy of the RTRW map. In this study, the study area taken was Pati Regency around the South Ring Road which includes four districts. From the acquisition of high resolution satellite imagery data in 2009, 2015 and 2019, predictions were made for the years 2023 and 2030 to determine the development of the area around the South Ring Road. The results of the prediction of land use using CA Markov in 2023 will be compared with the prediction in 2030 to determine the quality of the prediction results of the classification of land use in the prediction year with the same input data interval and exceeding the input data interval by conducting a suitability analysis with the RTRW. In 2023, the category of conformity is 95.41341%, and in 2030 amounting to 95.41340%. This shows that the prediction results of land use change with CA Markov for the same year with the time interval of the input data have insignificant differences with the predicted results with longer intervals when compared to the current RTRW.

Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance systems focus on a single-sensor based solutions and lagging the integration of multiple information sources. Moreover, monitoring larger landscapes using unmanned aerial vehicles (UAV) are challenging, and, therefore combining high resolution satellite imagery data with advanced machine learning (ML) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status. In this study, we classified banana under mixed-complex African landscapes through pixel-based classifications and ML models derived from multi-level satellite images (Sentinel 2, PlanetScope and WorldView-2) and UAV (MicaSense RedEdge) platforms. Our pixel-based classification from random forest (RF) model using combined features of vegetation indices (VIs) and principal component analysis (PCA) showed up to 97% overall accuracy (OA) with less than 10% omission and commission errors (OE and CE) and Kappa coefficient of 0.96 in high resolution multispectral images. We used UAV-RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed-model system combining object detection model (RetinaNet) and a custom classifier for simultaneous banana localization and disease classification. Their accuracies were tested using different performance metrics. Our UAV-RGB mixed-model revealed that the developed object detection and classification model successfully classified healthy and diseased plants with 99.4%, 92.8%, 93.3% and 90.8% accuracy for the four classes: banana bunchy top disease (BBTD), Xanthomonas Wilt of Banana (BXW), healthy banana cluster and individual banana plants, respectively. These approaches of aerial image-based ML models have high potential to provide a decision support system for major banana diseases in Africa.

Object based convolutional neural network for cloud classification in very high-resolution hyperspectral imagery

Remote sensing has a critical role for spatial data-based information systems and monitoring of the earth’s surface. The presence of clouds in optical sensing remote sensing satellite images is often a problem for many remote sensing applications. Therefore, the proper detection and classification of clouds in optical sensor remote sensing applications is quite a challenging task. This study aims to classify cloud objects in remote sensing satellite image data. The data used in this study is Pleiades very high-resolution satellite imagery data. The number of datasets used amounted to 1299 data. Cloud objects in this study are categorized into three classes, namely thick cloud, thin cloud, and clear. This study uses a deep learning algorithm, Convolutional Neural Network (CNN) for the classification of cloud objects. The CNN model used is LeNet with architectural modifications and parameters adjusted to the research needs. Classification of cloud objects with the LeNet model results in increased accuracy in each epoch during the training process and takes 1150.355 seconds for 200 epochs with the best accuracy value of 97.50%. The performance of LeNet is better than the VGG16 model as a comparison with the best accuracy of 96.50% with 600 data inputs.

地震災害の被害想定のための高解像度衛星画像・GPSビデオカメラを用いた 建物インベントリー推定手法の確立

地震災害の被害想定のための高解像度衛星画像・GPSビデオカメラを用いた 建物インベントリー推定手法の確立

Sensitivity Microscale of Urban Heat Island Reduction by Green Space

Green space is one of the main measures to alleviate urban heat islands (UHI). The transformation mechanism of daytime and nighttime scale sensitivity of vegetation coverage to reduce the UHI effect in a daily cycle has been unclear. As a result, we propose a scale sensitivity measurement algorithm to study the spatial and temporal response relationship between UHI and green coverage. Based on the scale theory of landscape ecology and the method of geostatistical analysis, we adopted ArcGIS, MATLAB, SPSS, and other data processing software as well as a large amount of measured and high-resolution satellite imagery data of Beijing and Tianjin to quantitatively study their spatial scale sensitivity and daily variation features of urban green spaces to reduce summer UHI. The results show that first, the green coverage rate and the UHI intensity experience positive and negative correlations during the daytime, and negative correlations at night. When the correlation coefficient is significant, there is a linear relationship between the UHI intensity and the core green rate. Second, the reduction of the UHI by green spaces displays spatial and temporal change scale sensitivity characteristics. The radius scale of daytime sensitivity is 15m, and the radius scale of nighttime sensitivity is 60m. The study's conclusion enriches the theoretical parameters of landscape ecological scales and patterns, and provides spatial and temporal scales for systematic planning of green space to reduce UHI.

Mapping Irrigation Networks with Geographical Information Systems Using Satelite Imagery Data: A Case of Brebes Regency, Indonesia

Water resources are important factors in food production. Those are very vital and strategic to meet food needs and food security. As water is scarce both in terms of volume and distribution throughout the year, reliable water management is needed. To support this water management, the accurate data is needed. However, the complete tabular data is not enough. It is because the existing tabular data does not provide the various activities and events based on time and place spatially and detail enough for planning purposes at the sub-district level. The researchers use high-resolution satellite imagery data that have been pre-processed with the geometric and radiometric corrections. They are used as one of the layers in the working map, so it is easier to provide the depiction of irrigation network objects, to find out the location of rice fields that have not been irrigated and the location of damaged irrigation networks. The depiction of the working map can also be used to map irrigation networks and their network conditions. Through this work, it has been shown that the researchers can map irrigation networks in detail for operational planning at a sub-district level with the help of technology, in particular for developing countries that is difficult or even impossible to do in the past.

The Effect of the ENSO on the Variability of SST and Chlorophyll-a in the South China Sea

The South China Sea (SCS) is one of the western marginal oceans of the Pacific Ocean, surrounded by South China, the Indo Peninsula of China, Peninsular Malaysia, the Philippines, and Borneo Island. The effect of the ENSO on the Variability of SST and chlorophyll-a has been investigated by many researchers. However, in the study, there were not still a small number of researchers who studied in this SCS area using high-resolution satellite imagery data. Chlorophyll-a and SST data taken from MODIS data, and wind analysis data from CCMP (Cross Calibrate Multi-Platform). The results obtained, wind speed affects over changes SST and Chlorophyll-a at SCS. The influence of wind speed against the variability of SST is very strong, but otherwise, over variability of Chlorophyll-a is less impact. There is a high correlation between ENSO (El Nino) with the SST which tends to be dominant throughout the west season due to the influence of wind speed in the South China Sea from the Pacific towards Australia throughout the west season. However, this is in contrast to the correlation between ENSO and Chlorophyll-a which appears less dominant, which is suspected of other factors that may affect Chlorophyll-a anomalies in addition to wind and SST factors.

Informative accuracy investigation and updating map using remote sensing technique and GIS

In this work, using GPS which has best accuracy that can be established set of GCPs, also two satellite images can be used, first with high resolution QuickBird, and second has low resolution Landsat image and topographic maps with 1:100,000 and 1:250,000 scales. The implementing of these factors (GPS, two satellite images, different scales for topographic maps, and set of GCPs) can be applying. In this study, must be divided this work into two parts geometric accuracy and informative accuracy investigation. The first part is showing geometric correction for two satellite images and maps.The second part of the results is to demonstrate the features (how the features appearance) of topographic map or pictorial map (image map), Where it is highlight the different features with different scales to know the accuracy of information. Where can be noticed through graphics that features appear very close to each other at a certain scale and become detached at another scale and this problem can be solved by generalization method. Geodatabase in GIS program also have been used as a modern style store all data related to the project in one folder divisions with that data. In addition to storage in a shape file. Geographic Information Systems (GIS) uses Remote Sensing (RS) data for a lot of applications. One of the application areas is the updating of the GIS database using high resolution imagery. Finally, high resolution satellite imagery data is very important to obtain updating map for Kut city by implementation two methods geodatabase and shapefile.

Application of domestic high-resolution satellite imagery data to the investigation of surface collapse in the Tiechanggou coal mine of Xinjiang

Application of domestic high-resolution satellite imagery data to the investigation of surface collapse in the Tiechanggou coal mine of Xinjiang

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.

Evaluation of high-resolution images application for wild medicinal plants macro monitoring：a case of Apocynum

To investigate the resources of medicinal plant, such as wild Apocynum, supervised classification based on Principal Component Analysis (PCA) and texture feature were used to monitor wild medicinal plants from image captured by ZY-3 and World-view-2 and compare which satellite Image are more appropriate to monitor the wild medicinal plants. The research results shows that: for more complex growth conditions wild medicinal plants Apocynum, high-resolution images Worldview-2 is more suitable for its remote identification, the low-resolution satellite ZY-3 can only recognizes the wild medicinal plants which distributed intensively. If the study target distribution is more intensive and larger scale, and cultivated type medicinal plants, the use of satellite ZY-3 in low resolution remote sensing data to identify the target can be a good choice, it is not necessary to buy high-resolution data, in order to avoid waste of expenditure, for the scattered distribution, the high-resolution satellite imagery data may be indispensable to identify targets.

UPDATING OBJECT FOR GIS DATABASE INFORMATION USING HIGH RESOLUTION SATELLITE IMAGES: A CASE STUDY ZONGULDAK

Abstract. Nowadays Geographic Information Systems (GIS) uses Remote Sensing (RS) data for a lot of applications. One of the application areas is the updating of the GIS database using high resolution imagery. In this context high resolution satellite imagery data is very important for many applications areas today's and future. And also, high resolution satellite imagery data will be used in many applications for different purposes. Information systems needs to high resolution imagery data for updating. Updating is very important component for the any of the GIS systems. One of this area will be updated and kept alive GIS database information. High resolution satellite imagery is used with different data base which serve map information via internet and different aims of information systems applications in future topographic and cartographic information systems will very important in our country in this sense use of the satellite images will be unavoidable. In this study explain to how is acquired to satellite images and how is use this images in information systems for object and roads. Firstly, pan-sharpened two of the IKONOS's images have been produced by fusion of high resolution PAN and MS images using PCI Geomatica v9.1 software package. Automatic object extraction has been made using eCognition v4.0.6. On the other hand, these objects have been manually digitized from high resolution images using ArcGIS v9.3. software package. Application section of in this study, satellite images data will be compared each other and GIS objects and road database. It is also determined which data is useful in Geographic Information Systems. Finally, this article explains that integration of remote sensing technology and GIS applications.

Multi-resolution Satellite Imagery Rectification using Bi-linear Interpolation Method for Quantitative Analysis

Rectification process is necessary so as to orient a satellite imagery to a planar surface and make its geometry planimetric. It establishes the image in the correct spatial location and orientation for subsequent quantitative analysis. This paper introduces the rectification of a true color Satellite imagery that was synthesized out of a Quick Bird high resolution Satellite imagery data for quantitative analysis. However an overview of the geometric correction process was also highlighted. Reference system is an important factor in order to verify and identify all measurements and data collection processes from Satellite imageries. The quality of the outputs depends on how good and refined the reference system is defined. The technique uses Global Positioning System (GPS) positioning information, geo-reference and other parameters, such as interpolation method to automatically register and ortho rectify the raw imagery data. Through the process, a seamless imagery is produced. Through semi-automatic and manual editing, a standard satellite-map imagery that satisfies geometric extraction needs was produced which would subsequently be used to determine quantitatively, geometric cadastral boundaries, hence, analyzing the imagery data. The result of the satisfactory imagery rectification shows that the total root mean square (RMS) error is 0.6152 meter, for X is 0.4119 meter and for Y is 0.4570 meter.

Growth, population and industrialization, and urban land expansion of China

China is experiencing urbanization at an unprecedented rate over the last two decades. The overall goal of this paper is to understand the extent of and the factors driving urban expansion in China from the late 1980s to 2000. We use a unique three-period panel data set of high-resolution satellite imagery data and socioeconomic data for entire area of coterminous China. Consistent with a number of the key hypotheses generated by the monocentric model, our results demonstrate the powerful role that the growth of income has played in China's urban expansion. In some empirical models, the other key variables in the monocentric model—population, the value of agricultural land and transportation costs—also matter. Adapting the basic empirical model to account for the environment in developing countries, we also find that industrialization and the rise of the service sector appear to have affected the growth of the urban core, but their role was relatively small when compared to the direct effects of economic growth. We also make a methodological contribution, demonstrating the potential importance of accounting for unobserved fixed effects.

Commercial observation satellites: broadening the sources of geospatial data

Commercial observation satellites promise to broaden substantially the sources of imagery data available to potential users of geospatial data and related information products. We examine the new trend toward private firms acquiring and operating high-resolution imagery satellites. These commercial observation satellites build on the substantial experience in Earth observation operations provided by government-owned imaging satellites for civilian and military purposes. However, commercial satellites will require governments and companies to reconcile public and private interests in allowing broad public access to high-resolution satellite imagery data without creating national security risks or placing the private firms at a disadvantage compared with other providers of geospatial data.