scholarly journals Sea Surface-Visible Aquaculture Spatial-Temporal Distribution Remote Sensing: A Case Study in Liaoning Province, China from 2000 to 2018

2019 ◽  
Vol 11 (24) ◽  
pp. 7186
Author(s):  
Junmei Kang ◽  
Lichun Sui ◽  
Xiaomei Yang ◽  
Yueming Liu ◽  
Zhihua Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Distribution ◽  
Liaoning Province ◽  
Dynamic Monitoring ◽  
Small Scale ◽  
Accuracy Evaluation ◽  
Migration Distance ◽  
Marine Aquaculture ◽  
Coastal Landform ◽  
Sea Area

Aquaculture plays an important role in providing food and reducing poverty but it affects environmental change and coastal ecosystems. Remote sensing is a technology that is helpful in the spatial-temporal dynamic monitoring of aquaculture, coastal management, and environmental monitoring. Most research focuses on inland and coastal areas, and little attention is paid to the extensive distribution of marine aquaculture. As an example, we use the freely available Landsat data of the developed marine aquaculture Liaoning Province of China and use the object-oriented automatic extraction method to analyze the spatial and temporal distribution information of marine aquaculture from 2000 to 2018. The accuracy evaluation from the randomly distributed sample points in high-resolution remote sensing images shows that the extraction accuracy for all of the five individual years of aquaculture area was higher than 82%. The results showed that (1) in the past 19 years, the area of marine aquaculture in Liaoning Province showed an increasing trend, which increased from 35.41 km2 in 2000 to 201.83 km2 in 2018, approximately 5.7 times increase in total area, but the growth rate decreased slightly due to government policy and the environmental quality of the sea area. (2) The centroid of offshore aquaculture in Liaoning Province shows a migration pattern to the northeast, in general, extending from the Dalian Bay sea area to the eastern sea area of the Dalian Chengshantou National Nature Reserve of Coastal Landform in the northeastern direction, and the migration distance reached 48.78 km. Moreover, the migration distance between 2005 and 2010 was the largest of all of the periods, reaching 35.43 km. The new marine aquaculture areas are mainly concentrated in the eastern direction of Xiaoyao Bay, the Changshan Islands, and Guanglu Island in Changhai County. (3) The landscape pattern of marine aquaculture in Liaoning Province is split, large-scale aquaculture and small-scale aquaculture are symbiotic, and landscape ecological activities are active. For local managers, this study can provide valuable supporting data for the assessment of marine aquaculture yield in this region, comprehensive control and management of the marine environment, and stability of the marine ecosystem. For other countries or regions, this work provides a great reference value for monitoring the dynamic spatial distribution of marine aquaculture.

scholarly journals Information Extraction and Dynamic Monitoring of Autumn Grain Crops in Heishan Area Based on Landsat8 Remote Sensing Image

2018 ◽  
Vol 53 ◽  
pp. 03063
Author(s):  
Ding Hua ◽  
Li Ruren ◽  
Liu Yumei
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Object Oriented ◽  
Liaoning Province ◽  
Dynamic Monitoring ◽  
Remote Sensing Images ◽  
Grain Crops ◽  
The Past ◽  
Elevation Data ◽  
Northeast Of China

Based on not only the basic data of total 9-phase Landsat8 OLI remote sensing images from 2013 to 2016 in Heishan District, Jinzhou City, Liaoning Province, also the supplementary data of highresolution remote sensing images and elevation data in Heishan District, as well as phenology data in Northeast of China, the remote sensing images of the studied areas were classified by various methods and the classification results were evaluated accurately. The results show that the method of object-oriented classification obtains the best effect and highest precision on the information extraction of autumn grain crops. Its overall spatial distribution accuracy is about 95.2091%, and the Kappa coefficient is 0.9360. The method of object-oriented classification was applied to the dynamic monitoring of autumn crops in the researched areas in recent years, and then the analysis of the main autumn crops in there from 2013 to 2016 showed that the rice areas increased significantly in the past four years, while the corn areas have shrunk by nearly one-fifth.

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

scholarly journals Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3000
Author(s):  
Georg Zitzlsberger ◽  
Michal Podhorányi ◽  
Václav Svatoň ◽  
Milan Lazecký ◽  
Jan Martinovič
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Urban Change ◽  
Small Scale ◽  
Atmospheric Effects ◽  
Wide Field ◽  
Ground Truth Data ◽  
Automated Method ◽  
Urban Monitoring ◽  
Sar Data

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

&lt;p&gt;Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R&lt;sup&gt;2&lt;/sup&gt; = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R&lt;sup&gt;2&lt;/sup&gt; = 0.75 and 0.78 respectively) while DL performs better in TDS (R&lt;sup&gt;2&lt;/sup&gt; =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.&lt;/p&gt;

scholarly journals Characterization of Arctic mixed-phase cloud properties at small scale and coupling with satellite remote sensing

2017 ◽  
Author(s):  
Guillaume Mioche ◽  
Olivier Jourdan ◽  
Julien Delanoë ◽  
Christophe Gourbeyre ◽  
Guy Febvre ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crystal Size ◽  
Mixed Phase ◽  
Ice Crystals ◽  
Small Scale ◽  
Vertical Profiles ◽  
Liquid Droplets ◽  
Ice Crystal ◽  
High Concentration ◽  
Ice Crystal Size

Abstract. This study aims to characterize the microphysical and optical properties of ice crystals and supercooled liquid droplets within low-level Arctic mixed-phase clouds (MPC). We compiled and analyzed cloud in situ measurements from 4 airborne campaigns (18 flights, 71 vertical profiles in MPC) over the Greenland Sea and the Svalbard region. Cloud phase discrimination and representative vertical profiles of number, size, mass and shapes of ice crystals and liquid droplets are assessed. The results show that the liquid phase dominates the upper part of the MPC with high concentration of small droplets (120 cm−3, 15&amp;tinsp;μm), and averaged LWC around 0.2 g m−3. The ice phase is found everywhere within the MPC layers, but dominates the properties in the lower part of the cloud and below where ice crystals precipitate down to the surface. The analysis of the ice crystal morphology highlights that irregulars and rimed are the main particle habit followed by stellars and plates. We hypothesize that riming and condensational growth processes (including the Wegener-Bergeron-Findeisein mechanism) are the main growth mechanisms involved in MPC. The differences observed in the vertical profiles of MPC properties from one campaign to another highlight that large values of LWC and high concentration of smaller droplets are possibly linked to polluted situations which lead to very low values of ice crystal size and IWC. On the contrary, clean situations with low temperatures exhibit larger values of ice crystal size and IWC. Several parameterizations relevant for remote sensing or modeling are also determined, such as IWC (and LWC) – extinction relationship, ice and liquid integrated water paths, ice concentration and liquid water fraction according to temperature. Finally, 4 flights collocated with active remote sensing observations from CALIPSO and CloudSat satellites are specifically analyzed to evaluate the cloud detection and cloud thermodynamical phase DARDAR retrievals. This comparison is valuable to assess the sub-pixel variability of the satellite measurements as well as their shortcomings/performance near the ground.

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