scholarly journals Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s

2019 ◽  
Vol 11 (2) ◽  
pp. 183 ◽  
Author(s):  
Shangmin Zhao ◽  
Shifang Zhang ◽  
Weiming Cheng ◽  
Chenghu Zhou
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Qilian Mountains ◽  
Bench Mark ◽  
Sensing Data ◽  
Mountain Permafrost ◽  
Simulation Results ◽  
The Qilian Mountains

Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as ‘permafrost probable’ (p > 0.7), ‘permafrost possible’ (0.7 ≥ p ≥ 0.3) and ‘permafrost improbable’ (p < 0.3). From the 1990s to the 2040s, the ‘permafrost probable’ type mainly degrades to that of ‘permafrost possible’, with the total area degenerating from 73.5 × 103 km2 to 66.5 × 103 km2. The ‘permafrost possible’ type mainly degrades to that of ‘permafrost impossible’, with a degradation area of 6.5 × 103 km2, which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions.

scholarly journals Fifty Years of Change in a Coniferous Forest in the Qilian Mountains, China—Advantages of High-Definition Remote Sensing

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1188
Author(s):  
Shu Fang ◽  
Zhibin He
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Qilian Mountains ◽  
High Definition ◽  
Mountain Ecosystems ◽  
Picea Crassifolia ◽  
Vegetation Shifts ◽  
Sensing Data ◽  
The Qilian Mountains

Mountain ecosystems are significantly affected by climate change. However, due to slow vegetation growth in mountain ecosystems, climate-induced vegetation shifts are difficult to detect with low-definition remote sensing images. We used high-definition remote sensing data to identify responses to climate change in a typical Picea crassifolia Kom. forest in the Qilian Mountains, China, from 1968 to 2017. We found that: (1) Picea crassifolia Kom. forests were distributed in small patches or strips on shaded and partly shaded slopes at altitudes of 2700–3250 m, (2) the number, area, and concentration of forest patches have been increasing from 1968 to 2017 in relatively flat and partly sunny areas, but the rate of area increase and ascend of the tree line slowed after 2008, and (3) the establishment of plantation forests may be one of the reasons for the changes. The scale of detected change in Picea crassifolia Kom.forest was about or slightly below 30 m, indicating that monitoring with high-resolution remote sensing data will improve detectability and accuracy.

scholarly journals Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 86
Author(s):  
Yi Ma ◽  
Qi Jiang ◽  
Xianting Wu ◽  
Renshan Zhu ◽  
Yan Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Hybrid Rice ◽  
Remote Sensing Data ◽  
Rice Cultivars ◽  
Sensing Data ◽  
Gauss Function ◽  
Low Altitude ◽  
Rice Phenology ◽  
Heading Stage

Accurate monitoring of hybrid rice phenology (RP) is crucial for breeding rice cultivars and controlling fertilizing amount. The aim of this study is to monitor the exact date of hybrid rice initial heading stage (IHSDAS) based on low-altitude remote sensing data and analyze the influence factors of RP. In this study, six field experiments were conducted in Ezhou city and Lingshui city from 2016 to 2019, which involved different rice cultivars and nitrogen rates. Three low-altitude remote sensing platforms were used to collect rice canopy reflectance. Firstly, we compared the performance of normalized difference vegetation index (NDVI) and red edge chlorophyll index (CIred edge) for monitoring RP. Secondly, double logistic function (DLF), asymmetric gauss function (AGF), and symmetric gauss function (SGF) were used to fit time-series CIred edge for acquiring phenological curves (PC), the feature: maximum curvature (MC) of PC was extracted to monitor IHSDAS. Finally, we analyzed the influence of rice cultivars, N rates, and air temperature on RP. The results indicated that CIred edge was more appropriate than NDVI for monitoring RP without saturation problem. Compared with DLF and AGF, SGF could fit CIred edge without over fitting problem. MC of SGF_CIred edge from all three platforms showed good performance in monitoring IHSDAS with good robustness, R2 varied between 0.82 and 0.95, RMSE ranged from 2.31 to 3.81. In addition, the results demonstrated that high air temperature might cause a decrease of IHSDAS, and the growth process of rice was delayed when more nitrogen fertilizer was applied before IHSDAS. This study illustrated that low-altitude remote sensing technology could be used for monitoring field-scale hybrid rice IHSDAS accurately.

Estimation of instantaneous air temperature using remote sensing data

2017 ◽  
Vol 39 (1) ◽  
pp. 258-275 ◽  
Author(s):  
Foroogh Golkar ◽  
Ali Akbar Sabziparvar ◽  
Reza Khanbilvardi ◽  
Mohammad Jafar Nazemosadat ◽  
Shahrokh Zand- Parsa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Sensing Data

Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

2020 ◽  
Vol 40 (10) ◽  
pp. 1028001
Author(s):  
陈世涵 Chen Shihan ◽  
李玲 Li Ling ◽  
蒋弘凡 Jiang Hongfan ◽  
居伟杰 Ju Weijie ◽  
张曼玉 Zhang Manyu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Satellite Remote Sensing ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Environment Change ◽  
Sensing Data

scholarly journals A Novel Rayleigh Dynamical Model for Remote Sensing Data Interpretation

2020 ◽  
Vol 58 (7) ◽  
pp. 4989-4999
Author(s):  
Fabio M. Bayer ◽  
Debora M. Bayer ◽  
Andrea Marinoni ◽  
Paolo Gamba
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Interpretation ◽  
Dynamical Model ◽  
Sensing Data

Forecasting of crop yields based on Earth remote sensing data (using soybeans as an example)

2019 ◽  
Author(s):  
А.С. Степанов
Keyword(s):  
Remote Sensing ◽  
Crop Yields ◽  
Gaussian Function ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Science System ◽  
Soybean Yield ◽  
Sensing Data ◽  
Independent Variable ◽  
Simulation Results

Описан подход к прогнозированию урожайности сельскохозяйственных культур с использованием данных дистанционного зондирования Земли. В качестве основного параметра прогностической регрессионной модели использовались значения вегетационного индекса NDVI. В статье приведена оценка возможности раннего прогнозирования до достижения индексом NDVI максимальных значений с применением гауссианы в качестве аппроксимирующей функции, соответствующей еженедельным композитам NDVI. Для пахотных земель Тамбовского р-на Амурской области рассчитана ошибка определения максимума NDVI в зависимости от календарной недели прогнозирования. Построенная модель использована для предварительной оценки урожайности сои в регионе в 2018 г. Purpose. Develop and describe a general approach to forecasting crop yields (using soybeans as an example). Methodology. Crop yields were estimated using regression models. Values of the vegetative index (NDVI) were considered with Vega-Science system. The normalized NDVI values were approximated by the Gauss function using the LevenbergMarquardt algorithm to enable early prediction with Python language. Findings. Values of the normalized index were determined by the preceding fiveyears period. For normalized values, approximating Gaussians were constructed and the parameters of the Gaussian function were calculated. The maximum was predicted for the NDVI values at various calendar weeks of the simulated year. The maximum values of NDVI composites in 20092018 were accounted for 3032 calendar weeks. According to the simulation results, it was found that the average absolute error in predicting the maximum NDVI for 10 years at the weeks 2932 did not exceed 3, for weeks 2728 4 and for the weeks 2126 7. At the next stage, a regression model was built to predict yield, where the calculated NDVI maximum was used as an independent variable, and soybean yield calculated according to the statistics of Rosstat on sown areas and gross soybean harvest in the region acted as an independent variable. Analysis of the error in predicting soybean yield for 2018 was obtained according to the simulation results of 20092017. It was shown that the absolute forecast error when using the data of 2232 calendar weeks of 2018 did not exceed 9.1. Originality/Value. The proposed approach to determining crop yields demonstrate high accuracy, while the method provides the possibility of early forecasting. The use of Earth remote sensing data and developed software modules of Python contribute to the operational formation of the forecast and, accordingly, the possibility of adjusting the agricultural plans.

Estimating basin-wide air temperature by partial integration of remote sensing data

2018 ◽  
Vol 55 (10) ◽  
pp. 1196-1206
Author(s):  
Vedran Ivezic ◽  
Damir Bekic ◽  
Igor Kerin
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Standard Methods ◽  
Sensing Data ◽  
Interpolation Methods ◽  
Partial Integration

A comparison of various methods that enable temporally continuous computation of basin-wide air temperature is presented. An approach that combines remote sensing data with measurements at meteorological stations for obtaining basin-wide air temperature is proposed and compared to the standard interpolation methods of point measurements. For a basin of over 1000 km2, the proposed approach provides significantly more reliable air temperature rasters (average Δ = 9%) than the standard interpolation methods (average Δ = 25%), all by using satellite images and measurements from only two meteorological stations in comparison to standard methods using measurements from 10 meteorological stations.

scholarly journals Air temperature estimation based on environmental parameters using remote sensing data

2018 ◽  
Vol 18 (48) ◽  
pp. 131-152
Author(s):  
Chenoor Mohammadi ◽  
Manouchehr Farajzadeh ◽  
Yousef Ghavdel Rahimi ◽  
Abbas Ali Aliakbar Bidokhti ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Remote Sensing Data ◽  
Environmental Parameters ◽  
Temperature Estimation ◽  
Sensing Data ◽  
Air Temperature Estimation

scholarly journals Improving the Efficiency of Remote Sensing Data Interpretation by Analyzing Neighborhood Descriptors

2018 ◽  
Vol 28 (3) ◽  
pp. 352-365
Author(s):  
Stanislav A. Yamashkin ◽  
Anatoliy A. Yamashkin
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
System Analysis ◽  
Remote Sensing Data ◽  
Automated Analysis ◽  
Data Interpretation ◽  
Space Time ◽  
The State ◽  
Survey Analysis ◽  
Sensing Data

Introduction. In evaluating the space-time structure of the Earth’s surface, the data of remote sensing of the Earth become more important. Increasing the effectiveness of space survey analysis tools is possible through studying the problem of obtaining an integrated space-time characterization of the state of lands. The purpose of this study is to improve the accuracy of the automated analysis of remote sensing data by taking into account the invariant and dynamic descriptors of the vicinity. Materials and Methods. In order to improve the accuracy of the remote sensing data classification, a computation of complex space-time characteristics of the state of the lands was conducted based on the system analysis of data characterizing the dynamic and invariant states of the territory surrounding the geophysical site. The formalization of this process includes methods for calculating a set of numerical descriptors of the neighborhood: local entropy, local range, standard deviation, color moment, histogram of hues, and color cortege. A technique for calculating a complex descriptor based on the Fisher vector is described. To approbate the solution, a plan for the experiment was drawn up and a sample of the initial data was sampled. Results. The approbation of the methodology and the algorithm developed on its basis, implemented as a set of programs, on the test polygon system showed a variation in the classification accuracy in the range of 81–89% (without regard to the neighborhood), and taking into account the neighborhood, it increases to 91–97%. It is revealed that a significant increase in the radius of the analyzed neighborhood leads to a decrease in the classification accuracy. Conclusions. The application of the developed set of programs allows for the rapid implementation of modeling of spatial systems for the purpose of thematic mapping of land use and analyzing the development of emergency situations. The developed methodology for analyzing lands with regard to the descriptors of the neighborhood makes it possible to improve the accuracy of classification.

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