scholarly journals Multi-fractal-interslipface angle curves of a morphologically simulated sand dune

2000 ◽  
Vol 5 (1) ◽  
pp. 71-74 ◽  
Author(s):  
B. S. Daya Sagar
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Remote Sensing ◽  
Sand Dune ◽  
Fractal Dimensions ◽  
Morphological Transformation ◽  
Non Linear

A sand dune is simulated by means of a non-linear mathematical morphological transformation of which the fractal dimensions with corresponding interslipface angles are computed. This exercise has relevance to test the Validity of the model by considering various time series sand dune data that can be retrieved from the robust satellite remote sensing sensors.

scholarly journals An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1088 ◽  
Author(s):  
Yulong Wang ◽  
Xingang Xu ◽  
Linsheng Huang ◽  
Guijun Yang ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Crop Yield ◽  
Satellite Remote Sensing ◽  
Wheat Yield ◽  
Growing Season ◽  
Remote Sensing Images ◽  
Casa Model ◽  
Winter Wheat Yield

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

scholarly journals Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

2014 ◽  
Vol 11 (16) ◽  
pp. 4305-4320 ◽  
Author(s):  
S. T. Klosterman ◽  
K. Hufkens ◽  
J. M. Gray ◽  
E. Melaas ◽  
O. Sonnentag ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Curve Fitting ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spatial Scales ◽  
Visual Assessment ◽  
Statistical Uncertainty ◽  
Near Surface

Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index

2016 ◽  
Vol 183 ◽  
pp. 562-575 ◽  
Author(s):  
Fabian Löw ◽  
François Waldner ◽  
Alexandre Latchininsky ◽  
Chandrashekhar Biradar ◽  
Maximilian Bolkart ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Migratory Locust

scholarly journals Inspecting the Food–Water Nexus in the Ogallala Aquifer Region Using Satellite Remote Sensing Time Series

2020 ◽  
Vol 12 (14) ◽  
pp. 2257
Author(s):  
Yuting Zhou ◽  
Hamed Gholizadeh ◽  
G. Thomas LaVanchy ◽  
Emad Hasan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Agricultural Production ◽  
Satellite Remote Sensing ◽  
Crop Production ◽  
The United States ◽  
Farm Income ◽  
Ogallala Aquifer ◽  
Sustainable Solutions ◽  
Sensing Time

Agricultural production in the Great Plains provides a significant amount of food for the United States while contributing greatly to farm income in the region. However, recurrent droughts and expansion of crop production are increasing irrigation demand, leading to extensive pumping and attendant depletion of the Ogallala aquifer. In order to optimize water use, increase the sustainability of agricultural production, and identify best management practices, identification of food–water conflict hotspots in the Ogallala Aquifer Region (OAR) is necessary. We used satellite remote sensing time series of agricultural production (net primary production, NPP) and total water storage (TWS) to identify hotspots of food–water conflicts within the OAR and possible reasons behind these conflicts. Mean annual NPP (2001–2018) maps clearly showed intrusion of high NPP, aided by irrigation, into regions of historically low NPP (due to precipitation and temperature). Intrusion is particularly acute in the northern portion of OAR, where mean annual TWS (2002–2020) is high. The Oklahoma panhandle and Texas showed large decreasing TWS trends, which indicate the negative effects of current water demand for crop production on TWS. Nebraska demonstrated an increasing TWS trend even with a significant increase of NPP. A regional analysis of NPP and TWS can convey important information on current and potential conflicts in the food–water nexus and facilitate sustainable solutions. Methods developed in this study are relevant to other water-constrained agricultural production regions.

Non-linear and chaos characteristics of heart sound time series

2008 ◽  
Vol 222 (3) ◽  
pp. 265-272 ◽  
Author(s):  
B-B Li ◽  
Z-F Yuan
Keyword(s):  
Time Series ◽  
Heart Sound ◽  
Fractal Dimensions ◽  
Abnormal Movements ◽  
Mitral Incompetence ◽  
Non Linear ◽  
Second Heart Sound ◽  
The Many ◽  
Non Linear System ◽  
Physical Phenomena

Analysis of chaotic time series is common in many fields of science and engineering. It arises primarily from massive interactions between the many different parts of a non-linear system or in non-linear physical phenomena that are intrinsically complex. It is important to analyse the time series of these non-linear dynamic systems based on chaos theory. In recent years, many researchers on heart dynamics have demonstrated that chaos really exists in heart movements. In this study the non-linear and chaos characteristics are investigated and the fractal dimensions (FDs) and largest Lyapunov exponents (LLE) of the heart sound time series are calculated. First, the C—C method is used to estimate the time delay and embedding dimensions which are used to reconstruct the phase spaces. Then, the FDs and LLEs of the different heart sound signals are calculated and analysed, including the healthy heart sound, splitting of the second heart sound, mitral incompetence, and abnormal aortic shrinkage. From the results, the non-linear and chaotic characteristics in heart dynamic movement are found, and the results of LLEs show that the healthy heart has more obvious chaotic movements than abnormal movements.

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