scholarly journals Comparative results of the average daily net radiation (Rnd) from meteorological observation data and Landsat–8 remote sensing imagery areas of Hoa Binh province

2022 ◽  
Vol 9 (12) ◽  
pp. 9-22
Author(s):  
Le Hung Chien ◽  
Doan Ha Phong ◽  
Tran Xuan Truong
Keyword(s):  
Remote Sensing ◽  
Landsat 8 ◽  
Observation Data ◽  
Net Radiation ◽  
Meteorological Observation ◽  
Remote Sensing Imagery ◽  
Comparative Results ◽  
Hoa Binh

scholarly journals Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 456
Author(s):  
Weiying Xie ◽  
Jian Yang ◽  
Yunsong Li ◽  
Jie Lei ◽  
Jiaping Zhong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Learning ◽  
Residual Error ◽  
Observation System ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Adversarial Learning ◽  
Remote Sensing Imagery ◽  
Discriminative Feature ◽  
Latent Space

Cloud detection is a significant preprocessing step for increasing the exploitability of remote sensing imagery that faces various levels of difficulty due to the complexity of underlying surfaces, insufficient training data, and redundant information in high-dimensional data. To solve these problems, we propose an unsupervised network for cloud detection (UNCD) on multispectral (MS) and hyperspectral (HS) remote sensing images. The UNCD method enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. The UNCD enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. First, a compact representation of the original imagery is obtained by a latent adversarial learning constrained encoder. Meanwhile, the majority class with sufficient samples (i.e., background pixels) is more accurately reconstructed than the clouds with limited samples by the decoder. An image discriminator is used to prevent the generalization of out-of-class features caused by latent adversarial learning. To further highlight the background information in the deep latent space, a multivariate Gaussian distribution is introduced. In particular, the residual error with clouds highlighted and background samples suppressed is applied in the cloud detection in deep latent space. To evaluate the performance of the proposed UNCD method, experiments were conducted on both MS and HS datasets that were captured by various sensors over various scenes, and the results demonstrate its state-of-the-art performance. The sensors that captured the datasets include Landsat 8, GaoFen-1 (GF-1), and GaoFen-5 (GF-5). Landsat 8 was launched at Vandenberg Air Force Base in California on 11 February 2013, in a mission that was initially known as the Landsat Data Continuity Mission (LDCM). China launched the GF-1 satellite. The GF-5 satellite captures hyperspectral observations in the Chinese Key Projects of High-Resolution Earth Observation System. The overall accuracy (OA) values for Images I and II from the Landsat 8 dataset were 0.9526 and 0.9536, respectively, and the OA values for Images III and IV from the GF-1 wide field of view (WFV) dataset were 0.9957 and 0.9934, respectively. Hence, the proposed method outperformed the other considered methods.

scholarly journals Identification and Classification of Complex Agricultural Croplands Using Multi-Temporal ALOS-2/PALSAR-2 Data: A Case Study in Central Java, Indonesia

2018 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Prima Rizky Mirelva ◽  
Ryota Nagasawa
Keyword(s):  
Remote Sensing ◽  
Paddy Field ◽  
Agricultural Land ◽  
National Income ◽  
Landsat 8 ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Cropping Pattern ◽  
Central Java ◽  
Multi Temporal

The agriculture sector makes a significant contribution to the Indonesian economy and has become one of the sources of national income. Therefore, precise agricultural mapping is very important to national and regional administrations. Satellite remote sensing provides the most effective tool for identifying a wide expanse of agriculture croplands. However, cloud coverage in tropical regions limits the use of optical remote sensing. SAR is an active remote sensing technique, which offers completely cloud-free observation data. The multi-temporal ALOS-2/PALSAR-2 data were used in this study, complemented by optical multi-temporal remote sensing data, that is, Landsat 8 OLI for classifying complex agricultural croplands. The study area, located in the Klaten Regency, Central Java Province, with 112 km2 coverage, was selected because of its dynamic cropping pattern and complex agricultural land use types. In this study, the RGB composite of HH, HV and HV-HH, derived from ALOS-2/PALSAR-2 polarizations, was found to be effective at separating two types of paddy field cropping pattern: all-year paddy (paddy-I) and paddy upland fields (paddy-II). The multi-temporal Landsat 8 data were also found to be useful for observing the cropping pattern. Moreover, the classification accuracy, which was as high as 85.02% in terms of overall accuracy, with a kappa coefficient of 0.824, from multi-temporal ALOS-2/PALSAR-2 data, was obtained. These results show that multi-temporal ALOS-2/PALSAR-2 data are capable of discriminating between two different paddy field cropping types, as well as beneficial for discriminating between the cropping stage and cropping pattern information for several other land uses.

scholarly journals A Novel Remote Sensing Index for Extracting Impervious Surface Distribution from Landsat 8 OLI Imagery

2019 ◽  
Vol 9 (13) ◽  
pp. 2631 ◽  
Author(s):  
Hong Fang ◽  
Yuchun Wei ◽  
Qiuping Dai
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Impervious Surface ◽  
Landsat 8 ◽  
The Novel ◽  
Impervious Surfaces ◽  
Surface Distribution ◽  
Remote Sensing Imagery ◽  
Normalised Difference Vegetation Index ◽  
Multispectral Remote Sensing

The area of urban impervious surfaces is one of the most important indicators for determining the level of urbanisation and the quality of the environment and is rapidly increasing with the acceleration of urbanisation in developing countries. This paper proposes a novel remote sensing index based on the coastal band and normalised difference vegetation index for extracting impervious surface distribution from Landsat 8 multispectral remote sensing imagery. The index was validated using three images covering urban areas of China and was compared with five other typical index methods for the extraction of impervious surface distribution, namely, the normalised difference built-up index, index-based built-up index, normalised difference impervious surface index, normalised difference impervious index, and combinational built-up index. The results showed that the novel index provided higher accuracy and effectively distinguished impervious surfaces from bare soil, and the average values of the recall, precision, and F1 score for the three images were 95%, 91%, and 93%, respectively. The novel index provides better applicability in the extraction of urban impervious surface distribution from Landsat 8 multispectral remote sensing imagery.

scholarly journals The Development of Interpretataion Method For Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island

2017 ◽  
Vol 15 (1) ◽  
pp. 20 ◽  
Author(s):  
Ferad Puturuhu ◽  
Projo Danoedoro ◽  
Junun Sartohadi ◽  
Danang Srihadmoko
Keyword(s):  
Remote Sensing ◽  
Debris Flow ◽  
Spatial Data ◽  
Landsat 8 ◽  
Land Resource ◽  
Digital Imagery ◽  
Visual Interpretation ◽  
Remote Sensing Imagery ◽  
Soil Creep ◽  
Landslide Event

ABSTRAKPenginderaa jauh merupakan salah satu metode yang digunakan untuk menjawab permasalahan penelitian tentang teknologi perolehan data spasial dan sekaligus permasalahan kewilayahan serta manajemen sumber daya laha. Pemanfaatan metode penginderaan jauh untuk penelitian landslide dianataranya metode interpretasi citra secara visual dan digital.  Tujuan penelitian ini adalah membandingkan akurasi metode interpretasi dan menentukan lokasi kejadian landslide. Citra yang digunakan dalam penelitian ini adalah citra Landsat 8, Quickbird dan SRTM. Metode yang digunakan untuk menentukan kandidat landslide adalah interpretasi visual berlapis, Interpretasi citra digital dengan NDVI, OBIA, Toposhape, dan kombinasi NDVI-OBIA, dan NDVI-OBIA-Toposhape. Penggunaan metode interpretasi kejadian landslide yang terbaik adalah interpretasi visual berlapis dengan presentase 90 %. Interpretasi digital dengan NDVI mempunyai ketelitian 47 %, OBIA ketelitiannya  45 %, Toposhape 47 %, kombinasi NDVI-OBIA 47 %, dan Kombinasi NDVI-OBIA-Toposhape 53 %. Dari interpretasi visual berlapis dan pengamatan lapangan diperoleh tipe landslide yang ditemukan yaitu nendatan/slump (soil rotational slide) dalam jumlah yang banyak 7 titik (38.9%), rayapan tanah (soil creep),  aliran bahan rombakan (debris flow), longsor translasi dengan material tanah (earths Slide), dan  nendatan majemuk (multiple rotational slide).Kata kunci: Pengembanga, Metode, Interpretasi Citra, Penginderaan Jauh, Kandidat,    Landslide, Paninsula LeitimurABSTRACTRemote sensing is one of the methods used to address the problem of research on spatial data acquisition technologies and is also acquiring the problems of territorial and land resource management. The utilization of remote sensing method for the landslide research is visual and digital imagery interpretation. The purpose of this study was to compare the accuracy of the method of interpretation and determine the location of the landslide event. The imagery that used in this study was Landsat 8, Quickbird and SRTM. The method that used to determine the candidate of landslide was the layered visual interpretation, digital imagery interpretation with NDVI, OBIA, Toposhape, and combination-OBIA NDVI and NDVI-OBIA-Toposhape. The use of the interpretation method for the landslide event is the best of layered-visual interpretation with a percentage of 90%. Digital interpretation with NDVI has a 47% of its accuracy, thoroughness OBIA 45%, Toposhape 47%, the combination of NDVI-OBIA 47%, and the combination of NDVI-OBIA-Toposhape 53%. From  the layered-visual interpretation and field observations were obtained type of landslide found that soil rotational slide in large quantities 7 points (38.9%), creep soil (soil creep), the flow of material destruction (debris flow), landslides translation with soil materials (earths slide) and multiple rotational slide.Keywords: Development, Method, Imagery Interpretation, Remote Sensing, Candidate of Landslide, Landslide and Leitimur JaizirahCitation: Puturuhu, F., Danoedoro, P., Sartohadi, J. and Srihadmoko, D. (2017). The Development of Interpretataion Method for Remote Sensing Imagery In Determining The Candidate of Landslide In Leitimur Paninsula, Ambon Island. Jurnal Ilmu Lingkungan, 15(1), 20-34, doi:10.14710/jil.15.1.20-34

scholarly journals REQUIREMENTS TO THE MATERIALS OF REMOTE SENSING FOR MONITORING THE STATE OF PASTURES

2019 ◽  
Vol 6 (1) ◽  
pp. 16-23
Author(s):  
Aizhanat Svankulova ◽  
Ekaterina Kulik
Keyword(s):  
Remote Sensing ◽  
Satellite Observation ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Observation Data ◽  
Satellite Systems ◽  
Area Of Interest ◽  
Pasture Degradation ◽  
Wide Range

The article is devoted to the role of remote sensing and satellite observation data in the optical and infrared ranges of the electromagnetic spectrum for solving a wide range of pasture monitoring tasks. The article outlines the requirements for images from the Landsat 8 and Sentinel-2 spacecraft for pastures monitoring in the Kosh-Agach district of the Altai Republic. The main characteristics of the imaging equipment of satellite systems data are considered. Data acquisition is recommended from the Earth Explorer geoportal, where the choice of materials includes the following: area of interest, cloudiness percentage, shooting period, shooting system, shooting time. Survey data are involved in the formation of an information model for determining pasture degradation, tracking pasture status and yield dynamics, and planning for their rational use.

scholarly journals Estimation and Assessment of Total Phosphorus and Trophic State Index Estimated from Landsat 8 Dataset on 2018 at Daecheong Reservoir

2020 ◽  
Vol 42 (9) ◽  
pp. 415-423
Author(s):  
Jongjin Baik ◽  
Jongmin Park ◽  
Minha Choi
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Spatial Distribution ◽  
Spatial Pattern ◽  
Total Phosphorus ◽  
Trophic State ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Remote Sensing Imagery ◽  
State Index

Objectives : Remote sensing of water quality parameters (WQPs) has been widely applied along with the emerging importance of obtaining the spatial pattern of water quality at inland reservoir. This study retrieved the Total Phosphorus (TP) via remote sensing imagery at Daecheong reservoir and used it for acquiring the spatial distribution of Trophic State Index (TSI).Methods : First, stepwise regression for TP was established using the ground-based TP measurement and reflectance of Landsat 8 collocated in space and time. With the developed regression model, we estimated the spatial distribution of TSI and evaluated the severity of eutrophication at Daecheong reservoir.Results and Discussion : The result confirmed that predicted TP yielded reasonable statistics compared against measured TP with R<sup>2</sup> of 0.956 and p-value of 0.022. Seasonal variation of TP was highly influenced by the precipitation. Similarly, precipitation also influenced to the spatial pattern of TSI. Before the rainfall event (e.g., August 22 2018) at Daecheong reservoir, TSI metric indicated the oligotrographic stage. However, TSI of Daecheong reservoir after the rainfall (e.g., October 25 2018) indicated the mesotrophic and eutrophic stage.Conclusions : These results confirmed that the retrieval of WQP from remote sensing imagery can serve as a robustness tool to monitor the water quality over the large scale area.

Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

2018 ◽  
Vol 18 (1) ◽  
pp. 41-52
Author(s):  
Tayeb Sitayeb ◽  
Ishak Belabbes
Keyword(s):  
Remote Sensing ◽  
Social Systems ◽  
Bare Soil ◽  
Climatic Conditions ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Natural Ecosystems ◽  
Stipa Tenacissima ◽  
Steppe Species ◽  
Sparse Vegetation

Abstract Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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