scholarly journals A Rapid Model (COV_PSDI) for Winter Wheat Mapping in Fallow Rotation Area Using MODIS NDVI Time-Series Satellite Observations: The Case of the Heilonggang Region

2021 ◽  
Vol 13 (23) ◽  
pp. 4870
Author(s):  
Xiaoyuan Zhang ◽  
Kai Liu ◽  
Shudong Wang ◽  
Xin Long ◽  
Xueke Li
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
The Other ◽  
Percentage Error ◽  
Growth Stages ◽  
Traditional Methods ◽  
Rapid Mapping ◽  
Art Methods ◽  
Ndvi Time Series ◽  
The Impact

Rapid and accurate monitoring of spatial distribution patterns of winter wheat over a long period is of great significance for crop yield prediction and farmland water consumption estimation. However, weather conditions and relatively long revisit cycles often result in an insufficient number of continuous medium-high resolution images over large areas for many years. In addition, the cropland pattern changes frequently in the fallow rotation area. A novel rapid mapping model for winter wheat based on the normalized difference vegetation index (NDVI) time-series coefficient of variation (NDVI_COVfp) and peak-slope difference index (PSDI) is proposed in this study. NDVI_COVfp uses the time-series index volatility to distinguish cultivated land from background land-cover types. PSDI combines the key growth stages of winter wheat phenology and special bimodal characteristics, substantially reducing the impact of abandoned land and other crops. Taking the Heilonggang as an example, this study carried out a rapid mapping of winter wheat for four consecutive years (2014–2017), and compared the proposed COV_PSDI with two state-of-the-art methods and traditional methods (the Spectral Angle Mapping (SAM) and the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA)). The verification results revealed that the COV_PSDI model improved the overall accuracy (94.10%) by 4% compared with the two state-of-art methods (90.80%, 89.00%) and two traditional methods (90.70%, 87.70%). User accuracy was the highest, which was 93.74%. Compared with the other four methods, the percentage error (PE) of COV_PSDI for four years was the lowest in the same year, with the minimum variation range of PE being 1.6–3.6%. The other methods resulted in serious overestimation. This demonstrated the effectiveness and stability of the method proposed in the rapid and accurate extraction of winter wheat in a large area of fallow crop rotation region. Our study provides insight for remote sensing monitoring of spatiotemporal patterns of winter wheat and evaluation of “fallow rotation” policy implementation.

Assessing and monitoring the vulnerability to drought and climate anomalies of Mediterranenan oak forests by using NDVI

2020 ◽  
Author(s):  
Maria Castellaneta ◽  
Angelo Rita ◽  
J. Julio Camarero ◽  
Michele Colangelo ◽  
Angelo Nolè ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Tree Mortality ◽  
Vegetation Index ◽  
Google Earth ◽  
Oak Forests ◽  
Summer Drought ◽  
Area Index ◽  
Ndvi Time Series ◽  
The Impact

<p>Several die-off episodes related to heat weaves and drought spells have evidenced the high vulnerability of Mediterranean oak forests. These events consisted in the loss in tree vitality and manifested as growths decline, elevated crown transparency (defoliation) and rising tree mortality rate. In this context, the changes in vegetation productivity and canopy greenness may represent valuable proxies to analyze how extreme climatic events trigger forest die-off. Such changes in vegetation status may be analyzed using remote-sensing data, specifically multi-temporal spectral information. For instance, the Normalized Difference Vegetation Index (NDVI) measures changes in vegetation greenness and is a proxy of changes in leaf area index (LAI), forest aboveground biomass and productivity. In this study, we analyzed the temporal patterns of vegetation in three Mediterranean oak forests showing recent die-off in response to the 2017 severe summer drought. For this purpose, we used an open-source platform (Google Earth Engine) to extract collections of MODIS NDVI time-series from 2000 to 2019. The analysis of both NDVI trends and anomalies were used to infer differential patterns of vegetation phenology among sites comparing plots where most trees were declining and showed high defoliation (test) versus plots were most trees were considered healthy (ctrl) and showed low or no defoliation. Here we discuss: i) the likely offset in NDVI time-series between test- versus ctrl- sites; and ii) the impact of summer droughts  on NDVI.</p><p><strong>Keywords</strong>: climate change, forest vulnerability, time series, remote sensing.</p>

scholarly journals Predicting drought using Bayesian structural time series model

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Sophia Wang ◽  
Connor Lee ◽  
XL Pang
Keyword(s):  
Time Series ◽  
Well Being ◽  
The United States ◽  
Fire Season ◽  
Series Data ◽  
Percentage Error ◽  
Structural Time Series ◽  
Structural Time ◽  
Model Training ◽  
The Impact

The western U.S. has been experiencing a mega-scale drought since 2000. By killing trees and drying out forests, the drought triggers widespread wildfire activities. In the 2020 California fire season alone, more than 10.3 million acres of land were burned and over 10000 structures were damaged. The estimated cost is over $12 billion. Drought also devastates agriculture and drains the social and emotional well-being of impacted communities.  This work aims at predicting the occurrence and severity of drought, and thus helping mitigate drought related adversaries. A machine learning based framework was developed, including time series data collection, model training, forecast and visualization. The data source is from the National Drought Monitor center with FIPS (Federal Information Processing Standards) geographic identification codes. For model training and forecasting, a Bayesian structural time series (BSTS) based statistical model was employed for a time-series forecasting of drought spatially and temporally. In the model, a time-series component captures the general trend and seasonal patterns in the data; a regression component captures the impact of the drought in measurements such as severity of drought, temperature, etc. The statistical measure, Mean Absolute Percentage Error, was used as the model accuracy metric. The last 10 years of drought data up to 2020-09-01 was used for model training and validation. Back-testing was implemented to validate the model . Afterwards, the drought forecast was generated for the upcoming 3 weeks of the United States based on the unit of county level. 2-D heat maps were also integrated for visual reference.   

scholarly journals Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

2019 ◽  
Vol 11 (14) ◽  
pp. 1665 ◽  
Author(s):  
Tianle He ◽  
Chuanjie Xie ◽  
Qingsheng Liu ◽  
Shiying Guan ◽  
Gaohuan Liu
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Winter Wheat ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Training Sample ◽  
Central China ◽  
Surface Reflectance ◽  
The Impact

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.

scholarly journals Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology

2020 ◽  
Vol 12 (1) ◽  
pp. 117 ◽  
Author(s):  
Jiaqi Tian ◽  
Xiaolin Zhu ◽  
Jin Wu ◽  
Miaogen Shen ◽  
Jin Chen
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Rural Areas ◽  
Urban Areas ◽  
Satellite Images ◽  
Landsat 8 ◽  
Spring Phenology ◽  
Rural And Urban Areas ◽  
Ndvi Time Series ◽  
The Impact

Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural–urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural–urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural–urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.

scholarly journals Assessing shaded-leaf effects on photochemical reflectance index (PRI) for water stress detection in winter wheat

2019 ◽  
Vol 16 (15) ◽  
pp. 2937-2947 ◽  
Author(s):  
Xin Yang ◽  
Shishi Liu ◽  
Yinuo Liu ◽  
Xifeng Ren ◽  
Hang Su
Keyword(s):  
Water Stress ◽  
Winter Wheat ◽  
Water Status ◽  
Control Treatment ◽  
Growth Stages ◽  
Linear Regression Models ◽  
Photochemical Reflectance Index ◽  
Crop Density ◽  
The Difference ◽  
The Impact

Abstract. The photochemical reflectance index (PRI) has emerged to be a pre-visual indicator of water stress. However, whether the varying shaded-leaf fractions, which may be caused by multiple view angles or the changing crop density in the field, affect the performance of PRI in detecting water stress of crops is still uncertain. This study evaluated the impact of the varying shaded-leaf fractions on estimating relative water content (RWC) across growth stages of winter wheat using seven formulations of PRI. Results demonstrated that for the control treatment the mean PRI of sunlit leaves was slightly higher than those of shaded leaves, but the difference between PRI of sunlit and shaded leaves increased as water resources became more limiting. Despite the difference between PRI of sunlit and shaded leaves, the significance of the linear relationship between RWC and most studied formulations of PRI did not show obvious variations with shadow fractions, except for the 100 % shaded-leaf condition. Among the studied formulations of PRI, PRI3 based on reflectance at 512 nm as the reference band provided the most accurate estimates of RWC with varying shaded-leaf fractions, except for the 100 % shaded-leaf condition. The slope and the intercept of linear regression models with PRI3 also showed minimized variations with shaded-leaf fractions. We then applied a uniform RWC prediction model to the data of varying shaded-leaf fractions and found that the accuracy of RWC predictions was not significantly affected in the mixture of sunlit and shaded leaves. However, RWC estimated with PRI of the 100 % shaded-leaf condition had the highest root mean square error (RMSE), implying that PRI of the pure shaded leaves may yield inaccurate estimates of plant water status.

scholarly journals Suitability of the MODIS-NDVI Time-Series for a Posteriori Evaluation of the Citrus Tristeza Virus Epidemic

2020 ◽  
Vol 12 (12) ◽  
pp. 1965
Author(s):  
Daniela Vanella ◽  
Simona Consoli ◽  
Juan Miguel Ramírez-Cuesta ◽  
Matilde Tessitori
Keyword(s):  
Time Series ◽  
Plant Pathogens ◽  
Management Practices ◽  
Citrus Tristeza Virus ◽  
Plant Diseases ◽  
Control Measures ◽  
Ndvi Time Series ◽  
The Impact ◽  
Tristeza Virus ◽  
Citrus Tristeza

The technological advances of remote sensing (RS) have allowed its use in a number of fields of application including plant disease depiction. In this study, an RS approach based on an 18-year (i.e., 2001–2018) time-series analysis of Normalized Difference Vegetation Index (NDVI) data, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and processed with TIMESAT free software, was applied in Sicily (insular Italy). The RS approach was carried out in four orchards infected by Citrus tristeza virus (CTV) at different temporal stages and characterized by heterogeneous conditions (e.g., elevation, location, plant age). The temporal analysis allowed the identification of specific metrics of the NDVI time-series at the selected sites during the study period. The most reliable parameter which was able to identify the temporal evolution of CTV syndrome and the impact of operational management practices was the “Base value” (i.e., average NDVI during the growing seasons, which reached R2 values up to 0.88), showing good relationships with “Peak value”, “Small integrated value” and “Amplitude”, with R2 values of 0.63, 0.70 and 0.75, respectively. The approach herein developed is valid to be transferred to regional agencies involved in and/or in charge of the management of plant diseases, especially if it is integrated with ground-based early detection methods or high-resolution RS approaches, in the case of quarantine plant pathogens requiring control measures at large-scale level.

scholarly journals Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak

2021 ◽  
Vol 7 ◽  
pp. e746
Author(s):  
Muhammad Naeem ◽  
Jian Yu ◽  
Muhammad Aamir ◽  
Sajjad Ahmad Khan ◽  
Olayinka Adeleye ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Statistical Measures ◽  
The Impact

Background Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results Statistical measures—Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)—are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.

scholarly journals Assessing shadow effects on Photochemical Reflectance Index (PRI) for the water stress detection in winter wheat

2018 ◽  
Author(s):  
Xin Yang ◽  
Shishi Liu ◽  
Yinuo Liu ◽  
Xifeng Ren ◽  
Hang Su
Keyword(s):  
Water Stress ◽  
Linear Regression ◽  
Winter Wheat ◽  
Growth Stages ◽  
Stress Detection ◽  
Photochemical Reflectance Index ◽  
Crop Density ◽  
Relative Water ◽  
The Difference ◽  
The Impact

Abstract. The photochemical reflectance index (PRI) has emerged to be a pre-visual indicator of water stress. However, whether the varying shadow fraction, which may be caused by multiple view angles or the changing crop density in the field, affects the performance of PRI in detecting water stress of crops is still uncertain. This study evaluated the impact of the varying shadow fraction on estimating relative water content (RWC) across growth stages of winter wheat using different formulations of PRI. Results demonstrated that PRI570, PRI1, and PRI2 of shadow were higher than those of sunlit leaves for unstressed plants, but the contrary results were achieved for stressed plants. Despite the difference between PRI_shadow and PRI_leaf, the significance of the linear relationship between RWC and PRI did not change with the different ratio of sunlit leaves and shadow. For most studied PRI formulations, the slope and intercept of the linear regression model between PRI and RWC changed proportionally with the shadow fractions. We applied a uniform RWC prediction model to the data of varying shadow fractions and found that the accuracy of RWC predictions was not significantly affected, indicating that the effect of varying shadow fractions was minimal to the seasonal water stress detection in winter wheat using PRI.

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