scholarly journals Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

2019 ◽  
Vol 11 (16) ◽  
pp. 1873 ◽  
Author(s):  
Li Hua ◽  
Huidong Wang ◽  
Haigang Sui ◽  
Brian Wardlow ◽  
Michael J. Hayes ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Temporal Dynamics ◽  
Risk Mitigation ◽  
Scientific Information ◽  
Gaussian Function ◽  
Drought Risk ◽  
Severe Drought ◽  
Similar Response ◽  
Vegetation Types ◽  
Data Set

Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.

scholarly journals Hydrological Condition Constrains Vegetation Dynamics for Wintering Waterfowl in China’s East Dongting Lake Wetland

2019 ◽  
Vol 11 (18) ◽  
pp. 4936 ◽  
Author(s):  
Min Wang ◽  
Qing Gu ◽  
Guihua Liu ◽  
Jingwei Shen ◽  
Xuguang Tang
Keyword(s):  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Global Climate ◽  
Dongting Lake ◽  
Plant Resources ◽  
Data Set ◽  
Wintering Waterfowl ◽  
The Government

As an internationally important wintering region for waterfowls on the East Asian–Australasian Flyway, the national reserve of China’s East Dongting Lake wetland is abundant in animal and plant resources during winter. The hydrological regimes, as well as vegetation dynamics, in the wetland have experienced substantial changes due to global climate change and anthropogenic disturbances, such as the construction of hydroelectric dams. However, few studies have investigated how the wetland vegetation has changed over time, particularly during the wintering season, and how this has directly affected habitat suitability for migratory waterfowl. Thus, it is necessary to monitor the spatio-temporal dynamics of vegetation in the protected wetland and explore the potential factors that alter it. In this study, the data set of time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) from 2000 to 2018 was used to analyze the seasonal dynamics and interannual trends of vegetation over the wintering period from October to January. The results showed that the average NDVI exhibited an overall increasing trend, with the trend rising slowly in recent years. The largest monthly mean NDVI generally occurred in November, which is pertinent to the quantity of wintering waterfowl in the East Dongting Lake wetland. Meanwhile, the mean NDVI in the wintering season is significantly correlated to temperature and water area, with apparent lagging effects. Long-term stability analysis presented a gradually decreasing pattern from the central body of water to the surrounding area. All analyses will help the government to make appropriate management strategies to protect the habitat of wintering waterfowl in the wetland.

scholarly journals Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

2021 ◽  
Vol 13 (24) ◽  
pp. 5018
Author(s):  
Xueying Li ◽  
Wenquan Zhu ◽  
Zhiying Xie ◽  
Pei Zhan ◽  
Xin Huang ◽  
...  
Keyword(s):  
Seasonal Changes ◽  
Vegetation Index ◽  
Spline Interpolation ◽  
Gaussian Function ◽  
Growing Season ◽  
Extraction Methods ◽  
Vegetation Phenology ◽  
Vegetation Types ◽  
Trend Estimation ◽  
Interpolation Methods

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.

scholarly journals Local patterns and trends of the Standard Precipitation Index in southern Portugal (1940–1999)

2011 ◽  
Vol 30 ◽  
pp. 11-16 ◽  
Author(s):  
A. C. Costa
Keyword(s):  
Temporal Dynamics ◽  
Linear Trend ◽  
Precipitation Index ◽  
Geostatistical Simulation ◽  
Severe Drought ◽  
Standard Precipitation Index ◽  
Data Set ◽  
Spatial Homogeneity ◽  
Study Region ◽  
Geostatistical Approach

Abstract. This paper analyzes the yearly changes in precipitation from 1940 to 1999 on local and regional scales over the southern region of continental Portugal, which has large areas threatened by desertification. The Standard Precipitation Index (SPI) time series with the 12-month time scale is calculated for 43 meteorological stations. A geostatistical approach is used to evaluate the temporal dynamics of the spatial patterns of precipitation. The spatial homogeneity of the SPI is evaluated for each decade. Afterwards, a geostatistical simulation algorithm (direct sequential simulation) is used to produce 100 equiprobable maps of the SPI for each year. This gridded data set (6000 maps with 800 m × 800 m grid cells) is then used to produce yearly scenarios of the SPI from 1940 to 1999, and uncertainty evaluations of the produced scenarios. The linear trend of SPI values over the sixty years period is calculated at each grid cell of the scenarios' maps using a nonparametric estimator. Wilcoxon-Mann-Whitney one-sided tests are used to compare the local median of the SPI in 1940/1969 with its median in 1970/1999. Results show that moderate drought conditions occur frequently over the study region, except in the northwest coast. Severe drought frequency patterns are found in areas of the centre and southeast regions. A significant trend towards drying occurs in the centre region and in the northeast. Considering the amount of water consumption and irrigation already required in some municipalities, water shortage due to drought is a viable threat in most of the Alentejo region if those local trends persist.

scholarly journals Drought Risk Assessment Using Remote Sensing and GIS in Yemen

2018 ◽  
Vol 7 (2.29) ◽  
pp. 586
Author(s):  
Ali Ahmed Ali Dhaifallah ◽  
Noorazuan Bin MD. Hashim ◽  
Azahan Bin Awang
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Sand Dunes ◽  
Red Light ◽  
Remote Sensing And Gis ◽  
Environmental Indicators ◽  
Infrared Light ◽  
Drought Risk ◽  
Electromagnetic Spectrum ◽  
Severe Drought

Drought remains the most frequent and serious environmental threat in the Middle East area. In Yemen, drought has negatively been affecting both livelihood and sustainable development of the country. This research aims to monitoring and assessing the drought risk through the changes in vegetation cover and sand dunes deposit in Tihama Plain, Yemen using remote sensing and GIS. Landsat TM5 of 1985 and OLI8 of 2015 were used to evaluate the environmental indicators of drought using Normalized Difference Vegetation Index (NDVI) to recognize the progressive decline of vegetation based on the fact that vegetation absorbs red light and reflects infrared light of the electromagnetic spectrum. Through the index maps generated in a Geographic Information Systems (GIS) environment, the results showed that there was an increase of 26% in the area under severe drought during the period from 1985 to 2015. Similarly, the areas under moderate drought also increased to approximately 64%. On the other hand, areas under mild drought and those receiving normal rain experienced a decrease during the period as they gradually transformed into mild and severe drought situations. Within the study period, the area also recorded 74% increase in sand dunes deposit.  

Using machine learning to model the distribution of Vegetation Types across Norway

2021 ◽  
Author(s):  
Lasse Torben Keetz ◽  
Anders Bryn ◽  
Peter Horvath ◽  
Olav Skarpaas ◽  
Lena Merete Tallaksen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cross Validation ◽  
Google Earth ◽  
Vegetation Types ◽  
Environmental Predictors ◽  
Distribution Models ◽  
Data Set ◽  
Sentinel 2

<p>Machine learning (ML) provides a powerful set of tools that can improve the accuracy of distribution models by automatically representing underlying ecological relationships empirically captured from large sets of data. However, more recent methodological advances in ML that have been less frequently applied, e.g. in the field of deep learning, may yield additional potential for Distribution Modeling. In this project, we use two ML algorithms, Random Forest (RF) and multi-layer feed-forward artificial neural networks (ANN), to predict the occurrences of Vegetation Types (VT) across Norway. Accurate predictions may support environmental management or the validation of earth system models. The VT data (derived from the AR18x18 data set; n=31 classes) covers the entire spatial scope in 0.9 ha plots on a systematically sampled 18 km grid (n = 1,081 plots, n = 22,154 observations). It was obtained through a field-based survey by a group of trained experts between 2004 and 2014. We use the cloud-based platform "Google Earth Engine" to generate a set of remotely sensed predictor variables based on SENTINEL-2 satellite imagery (i.e. surface reflectance from 12 spectral bands and six vegetation indices). These are then combined with ancillary environmental rasters used previously to model Norwegian VT distribution, e.g. representing climate, land cover, or geological properties (n=55, before one-hot encoding). Preliminary results suggest that in both modeling approaches, the generated SENTINEL-2 variables, particularly the Normalized Difference Vegetation Index (NDVI), have the highest predictive power as measured by permutation importance. The mean overall accuracy using 5-fold cross-validation shows only minor differences between the two methods (approx. 0.45 for ANN vs. 0.44 for RF; the respective F1-scores are 0.35 for ANN and 0.34 for RF; most frequent class baseline accuracy = 0.136). The modeling challenges we currently face include a class imbalance in the VT data set, reconciling the different spatial resolutions of the environmental predictors, and discrepancies in the timing of data acquisition. The next steps in the project will be to incorporate spatial cross-validation into the workflow and to analyze the differences between the ML methods in detail (e.g. regarding the ability to model rare VTs or differences in variable importance). Moreover, we will evaluate the possibility to include additional satellite data sources. This work is a contribution to the Strategic Research Initiative ‘Land Atmosphere Interaction in Cold Environments’ (LATICE) of the University of Oslo.</p>

scholarly journals Simultaneous spatiotemporal super-resolution and multi-parametric fluorescence microscopy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jagadish Sankaran ◽  
Harikrushnan Balasubramanian ◽  
Wai Hoh Tang ◽  
Xue Wen Ng ◽  
Adrian Röllin ◽  
...  
Keyword(s):  
Single Molecule ◽  
Temporal Dynamics ◽  
Growth Factor Receptor ◽  
Super Resolution ◽  
Actin Binding ◽  
Biological Knowledge ◽  
Data Set ◽  
Epidermal Growth ◽  
Molecular Brightness ◽  
Super Resolution Microscopy

AbstractSuper-resolution microscopy and single molecule fluorescence spectroscopy require mutually exclusive experimental strategies optimizing either temporal or spatial resolution. To achieve both, we implement a GPU-supported, camera-based measurement strategy that highly resolves spatial structures (~100 nm), temporal dynamics (~2 ms), and molecular brightness from the exact same data set. Simultaneous super-resolution of spatial and temporal details leads to an improved precision in estimating the diffusion coefficient of the actin binding polypeptide Lifeact and corrects structural artefacts. Multi-parametric analysis of epidermal growth factor receptor (EGFR) and Lifeact suggests that the domain partitioning of EGFR is primarily determined by EGFR-membrane interactions, possibly sub-resolution clustering and inter-EGFR interactions but is largely independent of EGFR-actin interactions. These results demonstrate that pixel-wise cross-correlation of parameters obtained from different techniques on the same data set enables robust physicochemical parameter estimation and provides biological knowledge that cannot be obtained from sequential measurements.

scholarly journals Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

2021 ◽  
Vol 13 (9) ◽  
pp. 4926
Author(s):  
Nguyen Duc Luong ◽  
Nguyen Hoang Hiep ◽  
Thi Hieu Bui
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Temporal Dynamics ◽  
Regional Scale ◽  
Dry Season ◽  
Red River ◽  
Agricultural Drought ◽  
Severe Drought ◽  
Red River Basin ◽  
Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

2012 ◽  
Vol 34 (1) ◽  
pp. 103 ◽  
Author(s):  
Z. M. Hu ◽  
S. G. Li ◽  
J. W. Dong ◽  
J. W. Fan
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
The State ◽  
Mean Annual Precipitation ◽  
Aboveground Net Primary Productivity ◽  
Remotely Sensed Data ◽  
Normalised Difference Vegetation Index

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

Determinants of spatial variation in fire return period in a semiarid African savanna

2011 ◽  
Vol 20 (4) ◽  
pp. 540 ◽  
Author(s):  
T. G. O'Connor ◽  
C. M. Mulqueeny ◽  
P. S. Goodman
Keyword(s):  
Spatial Variation ◽  
Return Period ◽  
Vegetation Type ◽  
Fire Frequency ◽  
Fuel Load ◽  
Annual Rainfall ◽  
Vegetation Types ◽  
Terrestrial Vegetation ◽  
African Savanna ◽  
Data Set

Fire pattern is predicted to vary across an African savanna in accordance with spatial variation in rainfall through its effects on fuel production, vegetation type (on account of differences in fuel load and in flammability), and distribution of herbivores (because of their effects on fuel load). These predictions were examined for the 23 651-ha Mkuzi Game Reserve, KwaZulu-Natal, based on a 37-year data set. Fire return period varied from no occurrence to a fire every 1.76 years. Approximately 75% of the reserve experienced a fire approximately every 5 years, 25% every 4.1–2.2 years and less than 1% every 2 years on average. Fire return period decreased in relation to an increase in mean annual rainfall. For terrestrial vegetation types, median fire return periods decreased with increasing herbaceous biomass, from forest that did not burn to grasslands that burnt every 2.64 years. Fire was absent from some permanent wetlands but seasonal wetlands burnt every 5.29 years. Grazer biomass above 0.5 animal units ha–1 had a limiting influence on the maximum fire frequency of fire-prone vegetation types. The primary determinant of long-term spatial fire patterns is thus fuel load as determined by mean rainfall, vegetation type, and the effects of grazing herbivores.

Sign in / Sign up

Export Citation Format

Share Document