scholarly journals Capsule network-based approach for estimating grassland coverage using time series data from enhanced vegetation index

2021 ◽  
Author(s):  
Yaqi Sun ◽  
Hailong Liu ◽  
Zhengqiang Guo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Enhanced Vegetation Index

scholarly journals Spatial Pattern of Agricultural Productivity Trends in Malawi

2020 ◽  
Vol 12 (4) ◽  
pp. 1313
Author(s):  
Leah M. Mungai ◽  
Joseph P. Messina ◽  
Sieglinde Snapp
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Agricultural Productivity ◽  
Household Survey ◽  
Maize Yield ◽  
Series Data ◽  
Maize Crop

This study aims to assess spatial patterns of Malawian agricultural productivity trends to elucidate the influence of weather and edaphic properties on Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) seasonal time series data over a decade (2006–2017). Spatially-located positive trends in the time series that can’t otherwise be accounted for are considered as evidence of farmer management and agricultural intensification. A second set of data provides further insights, using spatial distribution of farmer reported maize yield, inorganic and organic inputs use, and farmer reported soil quality information from the Malawi Integrated Household Survey (IHS3) and (IHS4), implemented between 2010–2011 and 2016–2017, respectively. Overall, remote-sensing identified areas of intensifying agriculture as not fully explained by biophysical drivers. Further, productivity trends for maize crop across Malawi show a decreasing trend over a decade (2006–2017). This is consistent with survey data, as national farmer reported yields showed low yields across Malawi, where 61% (2010–11) and 69% (2016–17) reported yields as being less than 1000 Kilograms/Hectare. Yields were markedly low in the southern region of Malawi, similar to remote sensing observations. Our generalized models provide contextual information for stakeholders on sustainability of productivity and can assist in targeting resources in needed areas. More in-depth research would improve detection of drivers of agricultural variability.

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

scholarly journals Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting

2019 ◽  
Vol 11 (21) ◽  
pp. 2558 ◽  
Author(s):  
Emily Myers ◽  
John Kerekes ◽  
Craig Daughtry ◽  
Andrew Russ
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Model Fitting ◽  
Series Data ◽  
Sigmoid Function ◽  
Absolute Deviation ◽  
Shape Model ◽  
Area Index ◽  
The Impact

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

scholarly journals Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

2019 ◽  
Vol 11 (21) ◽  
pp. 2515 ◽  
Author(s):  
Ana Navarro ◽  
Joao Catalao ◽  
Joao Calvao
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Cork Oak ◽  
Series Data ◽  
Tree Level ◽  
Oak Decline ◽  
Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

scholarly journals Phenological response of vegetation to upstream river flow in the Heihe Rive basin by time series analysis of MODIS data

2011 ◽  
Vol 15 (3) ◽  
pp. 1047-1064 ◽  
Author(s):  
L. Jia ◽  
H. Shang ◽  
G. Hu ◽  
M. Menenti
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Water Yield ◽  
Heihe River ◽  
Transform Method ◽  
Ejina Oasis ◽  
Ndvi Time Series ◽  
Terra Modis

Abstract. Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe River basin in northwestern China. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remotely sensed NDVI data acquired by Terra-MODIS are used to monitor the vegetation dynamic for a seven years period between 2000 and 2006. Due to cloud-contamination, atmospheric influence and different solar and viewing angles, however, the quality and consistence of time series of remotely sensed NDVI data are degraded. A Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – is used to reconstruct cloud- and noise-free NDVI time series data from the Terra-MODIS NDVI dataset. Modification is made on HANTS by adding additional parameters to deal with large data gaps in yearly time series in combination with a Temporal-Similarity-Statistics (TSS) method developed in this study to seek for initial values for the large gap periods. Secondly, the same Fourier Transform method is used to model time series of the vegetation phenology. The reconstructed cloud-free NDVI time series data are used to study the relationship between the water availability (i.e. the local precipitation and upstream water yield) and the evolution of vegetation conditions in Ejina Oasis from 2000 to 2006. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in the spring monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis. Warmer climate expressed by the degree-days showed positive influence on the vegetation phenology in particular during drier years. The time of maximum green-up is uniform throughout the oasis during wetter years, but showed a clear S-N gradient (downstream) during drier years.

Modeling Research on 1982-2000 NDVI Time Series Data of Chinese Different Vegetation Types Based on Autoregressive Moving Average Model

2014 ◽  
Vol 955-959 ◽  
pp. 863-868
Author(s):  
Rong Yu ◽  
Bo Feng Cai ◽  
Xiang Qin Su ◽  
Ya Zi He ◽  
Jing Yang
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Moving Average ◽  
Original Data ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Research Areas ◽  
Noaa Avhrr ◽  
Index Time Series

Vegetation index time series data modeling is widely used in many research areas, such as analysis of environmental change, estimation of crop yield, and the precision of the traditional vegetation index time series data fitting model is lower. This paper conducts the modeling with introducing the autoregressive moving average time series model, and using NOAA/AVHRR normalized differential vegetation index time series data, to estimate the errors of original data which are between under the situation that the parameters to be estimated are lesser, and on the basis gives the fitted equation to the six kinds of main land covers’ vegetation index time series data of Northeast China region.

scholarly journals NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 41-46
Author(s):  
D. Ratha ◽  
D. Mandal ◽  
S. Dey ◽  
A. Bhattacharya ◽  
A. Frery ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Geodesic Distance ◽  
Vegetation Indices ◽  
Series Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Plant Area Index ◽  
Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

scholarly journals PREDICTING SUGARCANE YIELDS IN KHUZESTAN USING A LARGE TIME-SERIES OF REMOTE SENSING IMAGERY REGION

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 645-648 ◽  
Author(s):  
M. Khosravirad ◽  
M. Omid ◽  
F. Sarmadian ◽  
S. Hosseinpour
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Linear Regression Models ◽  
Khuzestan Province ◽  
Second Growth ◽  
Ndvi Time Series ◽  
Sugarcane Yield

Abstract. This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ = 1.4) vegetation index relative to first period of growth with R2 = 0.66 and RMSE = 3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R2 = 0.66 and RMSE = 3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R2 = 0.78 and RMSE = 3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.

scholarly journals Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4660
Author(s):  
Fa Zhao ◽  
Guijun Yang ◽  
Hao Yang ◽  
Yaohui Zhu ◽  
Yang Meng ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Time Series Data ◽  
Short Term Memory ◽  
Combination Method ◽  
Series Data ◽  
Medium Term ◽  
Short Term ◽  
Deep Recurrent Neural Network

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

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