scholarly journals Remote sensing classification method of vegetation dynamics based on time series Landsat image: a case of opencast mining area in China

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Jiaxing Xu ◽  
Hua Zhao ◽  
Pengcheng Yin ◽  
Duo Jia ◽  
Gang Li
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Dynamics ◽  
Mining Area ◽  
Landsat Image ◽  
Classification Method ◽  
Opencast Mining ◽  
Remote Sensing Classification

scholarly journals Monitoring Land Cover Change and Disturbance of the Mount Wutai World Cultural Landscape Heritage Protected Area, Based on Remote Sensing Time-Series Images from 1987 to 2018

2019 ◽  
Vol 11 (11) ◽  
pp. 1332 ◽  
Author(s):  
Xuyu Bai ◽  
Peijun Du ◽  
Shanchuan Guo ◽  
Peng Zhang ◽  
Cong Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Cultural Landscape ◽  
Public Awareness ◽  
Mining Area ◽  
Research Area ◽  
Integrated System ◽  
Mount Wutai ◽  
Heritage Area

The contextual-based multi-source time-series remote sensing and proposed Comprehensive Heritage Area Threats Index (CHATI) index are used to analyze the spatiotemporal land use/land cover (LULC) and threats to the Mount Wutai World Heritage Area. The results show disturbances, such as forest coverage, vegetation conditions, mining area, and built-up area, in the research area changed dramatically. According to the CHATI, although different disturbances have positive or negative influences on environment, as an integrated system it kept stable from 1987 to 2018. Finally, this research uses linear regression and the F-test to mark the remarkable spatial-temporal variation. In consequence, the threats on Mount Wutai be addressed from the macro level and the micro level. Although there still have some drawbacks, the effectiveness of threat identification has been tested using field validation and the results are a reliable tool to raise the public awareness of WHA protection and governance.

Validation of seasonal time series of remote sensing derived LAI for hydrological modelling

2020 ◽  
Author(s):  
Charlotte Wirion ◽  
Boud Verbeiren ◽  
Sindy Sterckx
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Environment ◽  
Vegetation Dynamics ◽  
Heat Waves ◽  
Field Work ◽  
Hydrological Modelling ◽  
Urban Environments ◽  
Urban Vegetation ◽  
Area Index

<p>In urban environments, due to climate change urban heat waves are predicted to occur more frequently. Urban vegetation and the linked evapotranspiration rate can play a mitigating role. However, a major challenge in urban hydrological modelling remains the mapping of vegetation dynamics and its role in hydrological processes in particular interception storage and evapotranspiration. Conventional mapping of vegetation usually implies intensive labor and time consuming field work. We explore the potential of different remote sensing sensors (Proba-V, Landsat, Sentinel2, Apex) to characterize the urban vegetation dynamics for hydrological modelling. The here proposed remote sensing sensors show differences in the spectral and spatial resolutions as well as in their revisit time. However, in the urban environment we need a high spatial and spectral resolution to distinguish the urban landcover and a frequent revisit time to capture seasonal vegetation dynamics. Therefore, we propose a combination of different remote sensing sensors to derive leaf area index (LAI) timeseries in the urban environment. To improve the consistency in time series generated from different remote sensing sources a harmonization of the multi-sensor time series is proposed and validated with a multi-resolution validation approach using ground-truthing LAI (BELHARMONY project). The LAI timeseries, derived from the different remote sensing sensors, are then introduced into the hydrological modelling framework for a location- and time- specific assessment of the interception storage and evapotranspiration component. The effect of the sensor differences to the LAI timeseries on the hydrological response is analyzed.</p>

scholarly journals MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON QUANTUM ENTANGLEMENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 667-670
Author(s):  
F. Yang ◽  
G. Q. Zhou ◽  
J. R. Xiao ◽  
Q. Li ◽  
B. Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Quantum Entanglement ◽  
Remote Sensing Image ◽  
The State ◽  
Classification Method ◽  
Remote Sensing Classification ◽  
Quantum Particles ◽  
Remote Sensing Image Classification ◽  
State Configuration

Abstract. Aiming at the problems of low accuracy and slow speed in the current remote sensing image classification algorithm,In order to improve remote sensing image classification, a quantum entanglement algorithm is proposed.The model transforms the classification process of remote sensing image into a random self-organization process of quantum particles in the state configuration space. The state configuration formed by entanglement of quantum particles evolves with time and finally converges to an average probability distribution.Taking Kunming city of Yunnan province as the research area, this paper compares the classification method in this paper with the traditional remote sensing classification method by using the 02C image data of yuanyuan1.Compared with other classification methods, the classification accuracy of this paper meets the requirements.

scholarly journals A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)

2021 ◽  
Vol 11 (4) ◽  
pp. 1859
Author(s):  
Francisco Carreño-Conde ◽  
Ana Elizabeth Sipols ◽  
Clara Simón de Blas ◽  
David Mostaza-Colado
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Dynamics ◽  
Environmental Changes ◽  
Multiple Scales ◽  
Climatic Factors ◽  
Vegetation Indices ◽  
Arid Zones ◽  
Electromagnetic Spectrum ◽  
Combination Of Forecasts

Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.

Remote Sensing Image Classification Based on Object-Oriented Method and Support Vector Machine: A Case Study in Harbin City

2014 ◽  
Vol 912-914 ◽  
pp. 1331-1334
Author(s):  
Qiu Xia Yang ◽  
Chuan Wen Luo ◽  
Tian Kai Chen
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Support Vector ◽  
Object Oriented Method ◽  
Remote Sensing Classification ◽  
Remote Sensing Image Classification ◽  
Harbin City

Remote sensing classification, as an important means of urban planning and construction, has been widely concerned. Urban land use classification is extremely challenging tasks because of some land covers are spectrally too similar to be separated using only the spectral information of remote sensing image. Object-oriented remote sensing image classification method overcomes the drawbacks of traditional pixel-based classification method. It combines the spectral, special structure and texture features of the images, can effectively avoid the phenomenon of "different objects share the same spectrum" or "the same objects differ in spectrum. Support Vector Machine (SVM) is an excellent tool for remote sensing classification. Combination of both can develop their own advantages to do high-resolution remote sensing image classification. Using a public image in Harbin city as an example, classification based on object-oriented method and SVM has achieved better results than traditional pixel-based classification method.

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