scholarly journals Drought Influence on Forest Plantations in Zululand, South Africa, Using MODIS Time Series and Climate Data

Forests ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 528 ◽  
Author(s):  
Sifiso Xulu ◽  
Kabir Peerbhay ◽  
Michael Gebreslasie ◽  
Riyad Ismail
Keyword(s):  
South Africa ◽  
Time Series ◽  
El Niño ◽  
Vegetation Index ◽  
El Nino ◽  
Google Earth ◽  
Series Data ◽  
Trend Test ◽  
Drought Severity ◽  
Severe Drought

South Africa has a long history of recurrent droughts that have adversely affected its economic performance. The recent 2015 drought has been declared the most serious in 26 years and impaired key agricultural sectors including the forestry sector. Research on the forests’ responses to drought is therefore essential for management planning and monitoring. The effects of the latest drought on the forests in South Africa have not been studied and are uncertain. The study reported here addresses this gap by using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) and precipitation data retrieved and processed using the JavaScript code editor in the Google Earth Engine (GEE) and the corresponding normalized difference infrared index (NDII), Palmer drought severity index (PDSI), and El Niño time series data for KwaMbonambi, northern Zululand, between 2002 and 2016. The NDVI and NDII time series were decomposed using the Breaks for Additive Seasonal and Trend (BFAST) method to establish the trend and seasonal variation. Multiple linear regression and Mann–Kendall tests were applied to determine the association of the NDVI and NDII with the climate variables. Plantation trees displayed high NDVI values (0.74–0.78) from 2002 to 2013; then, they decreased sharply to 0.64 in 2015. The Mann–Kendall trend test confirmed a negative significant (p = 0.000353) trend between 2014 and 2015. This pattern was associated with a precipitation deficit and low NDII values during a strong El Niño phase. The PDSI (−2.6) values indicated severe drought conditions. The greening decreased in 2015, with some forest remnants showing resistance, implying that the tree species had varying sensitivity to drought. We found that the plantation trees suffered drought stress during 2015, although it seems that the trees began to recover, as the NDVI signals rose in 2016. Overall, these results demonstrated the effective use of the NDVI- and NDII-derived MODIS data coupled with climatic variables to provide insights into the influence of drought on plantation trees in the study area.

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 Phenological Monitoring of Paddy Crop Using Time Series MODIS Data

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 19
Author(s):  
C. Dineshkumar ◽  
S. Nitheshnirmal ◽  
Ashutosh Bhardwaj ◽  
K. Nivedita Priyadarshini
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Vegetation Index ◽  
Google Earth ◽  
Paddy Fields ◽  
Series Data ◽  
Local Minima ◽  
Phenological Stages ◽  
Local Maxima ◽  
Modis Data

Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India, using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show that in the initial stage the pixel value of EVI in the paddy field shows local minima (0.23), whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data were cross-validated using the field data.

A new MODIS-Landsat fusion method to reconstruct Landsat NDVI time-series data

2021 ◽  
Author(s):  
Xiaofang Ling ◽  
Ruyin Cao
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Vegetation Index ◽  
Time Series Data ◽  
Similarity Index ◽  
Google Earth ◽  
Series Data ◽  
Fusion Method ◽  
Landsat Data ◽  
Ndvi Time Series

<p>The Normalized Difference Vegetation Index (NDVI) data provided by the satellite Landsat have rich historical archive data with a spatial resolution of 30 m. However, the Landsat NDVI time-series data are quite discontinuous due to its 16-day revisit cycle, cloud contamination and some other factors. The spatiotemporal data fusion technology has been proposed to reconstruct continuous Landsat NDVI time-series data by blending the MODIS data with the Landsat data. Although a number of spatiotemporal fusion algorithms have been developed during the past decade, most of the existing algorithms usually ignore the effective use of partially cloud-contaminated images. In this study, we presented a new spatiotemporal fusion method, which employed the cloud-free pixels in the partially cloud-contaminated images to improve the performance of MODIS-Landsat data fusion by <strong>C</strong>orrecting the inconsistency between MODIS and Landsat data in <strong>S</strong>patiotemporal <strong>DA</strong>ta <strong>F</strong>usion (called CSDAF). We tested the new method at three sites covered by different vegetation types, including deciduous forests in the Shennongjia Forestry District of China (SNJ), evergreen forests in Southeast Asia (SEA), and the irrigated farmland in the Coleambally irrigated area of Australia (CIA). Two experiments were designed. In experiment I, we first simulated different cloud coverages in cloud-free Landsat images and then used both CSDAF and the recently developed IFSDAF method to restore these “missing” pixels for quantitative assessments. Results showed that CSDAF performed better than IFSDAF by achieving the smaller average Root Mean Square Error (RMSE) values (0.0767 vs. 0.1116) and the larger average Structural SIMilarity index (SSIM) values (0.8169 vs. 0.7180). In experiment II, we simulated the scenario of “inconsistence” between MODIS and Landsat by simulating different levels of noise on MODIS and Landsat data. Results showed that CSDAF was able to reduce the influence of the inconsistence between MODIS and Landsat data on MODIS-Landsat data fusion to some extent. Moreover, CSDAF is simple and can be implemented on the Google Earth Engine. We expect that CSDAF is potentially to be used to reconstruct Landsat NDVI time-series data at the regional and continental scales.</p>

scholarly journals A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Roman Olson ◽  
Soon-Il An ◽  
Soong-Ki Kim ◽  
Yanan Fan
Keyword(s):  
Time Series ◽  
El Niño ◽  
Probabilistic Models ◽  
Time Series Data ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
Series Data ◽  
El Nino Southern Oscillation ◽  
Stochastic Dynamical System

AbstractStochastic differential equations (SDEs) are ubiquitous across disciplines, and uncovering SDEs driving observed time series data is a key scientific challenge. Most previous work on this topic has relied on restrictive assumptions, undermining the generality of these approaches. We present a novel technique to uncover driving probabilistic models that is based on kernel density estimation. The approach relies on few assumptions, does not restrict underlying functional forms, and can be used even on non-Markov systems. When applied to El Niño–Southern Oscillation (ENSO), the fitted empirical model simulations can almost perfectly capture key time series properties of ENSO. This confirms that ENSO could be represented as a two-variable stochastic dynamical system. Our experiments provide insights into ENSO dynamics and suggest that state-dependent noise does not play a major role in ENSO skewness. Our method is general and can be used across disciplines for inverse and forward modeling, to shed light on structure of system dynamics and noise, to evaluate system predictability, and to generate synthetic datasets with realistic properties.

scholarly journals Spatial Patterns of NDVI Variation over Indonesia and Their Relationship to ENSO Warm Events during the Period 1982–2006

2009 ◽  
Vol 22 (24) ◽  
pp. 6612-6623 ◽  
Author(s):  
Stefan Erasmi ◽  
Pavel Propastin ◽  
Martin Kappas ◽  
Oleg Panferov
Keyword(s):  
Time Series ◽  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
Spatially Explicit ◽  
Forest Conversion ◽  
Climate Anomalies ◽  
Indonesian Archipelago

Abstract The present study is based on the assumption that vegetation in Indonesia is significantly affected by climate anomalies that are related to El Niño–Southern Oscillation (ENSO) warm phases (El Niño) during the past decades. The analysis builds upon a monthly time series from the normalized difference vegetation index (NDVI) gridded data from the Advanced Very High Resolution Radiometer (AVHRR) and two ENSO proxies, namely, sea surface temperature anomalies (SSTa) and Southern Oscillation index (SOI), and aims at the analysis of the spatially explicit dimension of ENSO impact on vegetation on the Indonesian archipelago. A time series correlation analysis between NDVI anomalies and ENSO proxies for the most recent ENSO warm events (1982–2006) showed that, in general, anomalies in vegetation productivity over Indonesia can be related to an anomalous increase of SST in the eastern equatorial Pacific and to decreases in SOI, respectively. The net effect of these variations is a significant decrease in NDVI values throughout the affected areas during the ENSO warm phases. The 1982/83 ENSO warm episode was rather short but—in terms of ENSO indices—the most extreme one within the study period. The 1997/98 El Niño lasted longer but was weaker. Both events had significant impact on vegetation in terms of negative NDVI anomalies. Compared to these two major warm events, the other investigated events (1987/88, 1991/92, 1994/95, and 2002/03) had no significant effect on vegetation in the investigated region. The land cover–type specific sensitivity of vegetation to ENSO anomalies revealed thresholds of vegetation response to ENSO warm events. The results for the 1997/98 ENSO warm event confirm the hypothesis that the vulnerability of vegetated tropical land surfaces to drought conditions is considerably affected by land use intensity. In particular, it could be shown that natural forest areas are more resistant to drought stress than degraded forest areas or cropland. Comparing the spatially explicit patterns of El Niño–related vegetation variation during the major El Niño phases, the spatial distribution of affected areas reveals distinct core regions of ENSO drought impact on vegetation for Indonesia that coincide with forest conversion and agricultural intensification hot spots.

scholarly journals Revisiting the 1992 severe drought episode in South Africa: the role of El Niño in the anomalies of atmospheric circulation types in Africa south of the equator

2021 ◽  
Vol 146 (1-2) ◽  
pp. 723-740
Author(s):  
Chibuike Chiedozie Ibebuchi
Keyword(s):  
South Africa ◽  
High Pressure ◽  
Indian Ocean ◽  
El Niño ◽  
El Nino ◽  
Severe Drought ◽  
The South ◽  
Southwest Indian Ocean ◽  
Drought Episode ◽  
South Indian

AbstractDuring strong El Niño events, below-average rainfall is expected in large parts of southern Africa. The 1992 El Niño season was associated with one of the worst drought episodes in large parts of South Africa. Using reanalysis data set from NCEP-NCAR, this study examined circulation types (CTs) in Africa south of the equator that are statistically related to the El Niño signal in the southwest Indian Ocean and the implication of this relationship during the 1992 drought episode in South Africa. A statistically significant correlation was found between the above-average Nino 3.4 index and a CT that features widespread cyclonic activity in the tropical southwest Indian Ocean, coupled with a weaker state of the south Indian Ocean high-pressure. During the analysis period, it was found that the El Niño signal enhanced the amplitude of the aforementioned CT. The impacts of the El Niño signal on CTs in southern Africa, which could have contributed to the 1992 severe drought episode in South Africa, were reflected in (i) robust decrease in the frequency of occurrence of the austral summer climatology pattern of atmospheric circulation that favors southeasterly moisture fluxes, advected by the South Indian Ocean high-pressure; (ii) modulation of easterly moisture fluxes, advected by the South Atlantic Ocean high-pressure, ridging south of South Africa; (iii) and enhancement of the amplitude of CTs that both enhances subsidence over South Africa, and associated with the dominance of westerlies across the Agulhas current. Under the ssp585 scenario, the analyzed climate models suggested that the impact of radiative heating on the CT significantly related to El Niño might result in an anomalous increase in surface pressure at the eastern parts of South Africa.

scholarly journals A Skewed perspective of the Indian rainfall-ENSO Relationship

2019 ◽  
Author(s):  
Justin Schulte ◽  
Fredrick Policielli ◽  
Benjamin Zaitchik
Keyword(s):  
Time Series ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Wavelet Coherence ◽  
Drought Severity ◽  
Nonlinear Phenomenon ◽  
Central Pacific ◽  
Central Pacific El Niño ◽  
Highly Nonlinear

Abstract. The application of higher-order wavelet analysis to India rainfall and the El Niño/Southern Oscillation (ENSO) is presented. An auto-bicoherence analysis is used to extract the frequency modes contributing to the skewness of India rainfall and ENSO. A nonlinear wavelet coherence method is proposed for diagnosing why the time-domain correlation between two time series temporally changes when at least one time series has changing nonlinear characteristics. The results indicate the India rainfall and ENSO are highly nonlinear phenomenon. It is also demonstrated that the sea surface temperature (SST) patterns associated with different nonlinear ENSO modes depend on the frequency components participating in the nonlinear phase coupling. The SST pattern associated with coupling between ENSO modes with periods of 31 and 15.5 months is reminiscent of a central Pacific El Niño and intensifies around 1995, contrasting with the coupling between the 62- and 31-month modes that became active around the 1970s ENSO regime shift. A nonlinear coherence analysis showed that the skewness of India rainfall is weakly correlated with that of 4 ENSO time series after the 1970s, indicating that increases in ENSO skewness after 1970's at least partially contributed to the weakening India rainfall-ENSO relationship in recent decades. The implication of this result is that the intensity of skewed El Niño events is likely to overestimate India drought severity, which was the case in the 1997 monsoon season, a time point when the nonlinear wavelet coherence between All-India rainfall and ENSO reached its lowest value in the 1871–2016 period.

scholarly journals Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought

Climate ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 95 ◽  
Author(s):  
Nkanyiso Mbatha ◽  
Sifiso Xulu
Keyword(s):  
El Niño ◽  
Vegetation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Phase Relationship ◽  
Google Earth ◽  
Area Index ◽  
Mk Test ◽  
Significant Downward Trend

The variability of temperature and precipitation influenced by El Niño-Southern Oscillation (ENSO) is potentially one of key factors contributing to vegetation product in southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the ENSO and Indian Ocean Dipole/Dipole Mode Index (DMI) is important. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform was used to analyze the vegetation response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP) to climatic variability. The MODIS enhanced vegetation index (EVI), burned area index (BAI), and normalized difference infrared index (NDII) were also analyzed. The study used the Modern Retrospective Analysis for the Research Application (MERRA) model monthly mean soil temperature and precipitations. The Global Land Data Assimilation System (GLDAS) evapotranspiration (ET) data were used to investigate the HiP vegetation water stress. The region in the southern part of the HiP which has land cover dominated by savanna experienced the most impact of the strong El Niño. Both the HiP NDVI inter-annual Mann–Kendal trend test and sequential Mann–Kendall (SQ-MK) test indicated a significant downward trend during the El Niño years of 2003 and 2014–2015. The SQ-MK significant trend turning point which was thought to be associated with the 2014–2015 El Niño periods begun in November 2012. The wavelet coherence and coherence phase indicated a positive teleconnection/correlation between soil temperatures, precipitation, soil moisture (NDII), and ET. This was explained by a dominant in-phase relationship between the NDVI and climatic parameters especially at a period band of 8–16 months.

Projection of Future Drought Characteristics under Multiple Drought Indices

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1238
Author(s):  
Muhammad Imran Khan ◽  
Xingye Zhu ◽  
Xiaoping Jiang ◽  
Qaisar Saddique ◽  
Muhammad Saifullah ◽  
...  
Keyword(s):  
Series Data ◽  
Natural Phenomenon ◽  
Drought Indices ◽  
Drought Severity ◽  
Severe Drought ◽  
Drought Analysis ◽  
Songhua River ◽  
Drought Intensity ◽  
Drought Characteristics ◽  
Rcp 8.5

Drought is a natural phenomenon caused by the variability of climate. This study was conducted in the Songhua River Basin of China. The drought events were estimated by using the Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI) which are based on precipitation (P) and potential evapotranspiration (PET) data. Furthermore, drought characteristics were identified for the assessment of drought trends in the study area. Short term (3 months) and long term (12 months) projected meteorological droughts were identified by using these drought indices. Future climate precipitation and temperature time series data (2021–2099) of various Representative Concentration Pathways (RCPs) were estimated by using outputs of the Global Circulation Model downscaled with a statistical methodology. The results showed that RCP 4.5 have a greater number of moderate drought events as compared to RCP 2.6 and RCP 8.5. Moreover, it was also noted that RCP 8.5 (40 events) and RCP 4.5 (38 events) showed a higher number of severe droughts on 12-month drought analysis in the study area. A severe drought conditions projected between 2073 and 2076 with drought severity (DS-1.66) and drought intensity (DI-0.42) while extreme drying trends were projected between 2097 and 2099 with drought severity (DS-1.85) and drought intensity (DI-0.62). It was also observed that Precipitation Decile predicted a greater number of years under deficit conditions under RCP 2.6. Overall results revealed that more severe droughts are expected to occur during the late phase (2050–2099) by using RDI and SPI. A comparative analysis of 3- and 12-month drying trends showed that RDI is prevailing during the 12-month drought analysis while almost both drought indices (RDI and SPI) indicated same behavior of drought identification at 3-month drought analysis between 2021 and 2099 in the research area. The results of study will help to evaluate the risk of future drought in the study area and be beneficial for the researcher to make an appropriate mitigation strategy.

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