scholarly journals Monitoring Mangrove Forest Degradation and Regeneration: Landsat Time Series Analysis of Moisture and Vegetation Indices at Rabigh Lagoon, Red Sea

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 52
Author(s):  
Mohammed Othman Aljahdali ◽  
Sana Munawar ◽  
Waseem Razzaq Khan
Keyword(s):  
Time Series ◽  
Red Sea ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Road Construction ◽  
Least Square ◽  
Eastern Coast ◽  
Positive Trend ◽  
Study Region ◽  
Disturbance And Recovery

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.

scholarly journals Lightning characteristics over the eastern coast of the Mediterranean during different synoptic systems

2015 ◽  
Vol 15 (11) ◽  
pp. 2449-2459 ◽  
Author(s):  
Y. Ben Ami ◽  
O. Altaratz ◽  
Y. Yair ◽  
I. Koren
Keyword(s):  
Red Sea ◽  
Eastern Mediterranean ◽  
Convective Available Potential Energy ◽  
Lightning Activity ◽  
Eastern Coast ◽  
Land Breeze ◽  
Late Winter ◽  
Data Set ◽  
Study Region ◽  
Synoptic System

Abstract. Thunderstorm activity takes place in the eastern Mediterranean mainly through the boreal fall and winter seasons during synoptic systems of Red Sea Trough (RST), Red Sea Trough that closed a low over the sea (RST-CL), and Cyprus Low (during fall – FCL and winter – WCL). In this work we used the Israeli Lightning Location System ground strokes data set, between October 2004 and December 2010, for studying the properties of lightning strokes and their link to the thermodynamic conditions in each synoptic system. It is shown that lightning activity dominates over sea during WCL and FCL systems (with maximum values of 1.5 in WCL, and 2.2 km−2 day−1 in FCL) and have a dominant component over land during the RST and RST-CL days. The stronger instability (high Convective Available Potential Energy (CAPE) values of 762 ± 457 J kg−1) during RST-CL days together with the higher altitude of the clouds' mixed-phase region (3.6 ± 0.3 km), result in a slightly higher density of ground strokes during this system but a lower fraction of positive ground strokes (3 ± 0.5 %). In general the fraction of positive strokes was found to be inversely correlated with the sea surface temperature: it increases from 1.2 % in early fall to 17.7 % in late winter, during FCL and WCL days. This change could be linked to the variation in the charge center's vertical location during those months. The diurnal cycle in the lightning activity was examined for each synoptic system. During WCL conditions, no preferred times were found through the day, as it relates to the random passage timing of the frontal systems over the study region. During the fall systems (FCL and RST-CL) there is a peak in lightning activity during the morning hours, probably related to the enhanced convection driven by the convergence between the eastern land breeze and the western synoptic winds. The distributions of peak currents in FCL and WCL systems also change from fall to winter and include more strong negative and positive strokes toward the end of the winter.

scholarly journals Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5431 ◽  
Author(s):  
Pengyu Hao ◽  
Huajun Tang ◽  
Zhongxin Chen ◽  
Zhengjia Liu
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Immune Network ◽  
Artificial Immune ◽  
Study Region ◽  
Early Season ◽  
Spring Maize ◽  
Artificial Immune Network ◽  
Sentinel 2

Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.

scholarly journals Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery

2007 ◽  
Vol 58 (4) ◽  
pp. 316 ◽  
Author(s):  
A. B. Potgieter ◽  
A. Apan ◽  
P. Dunn ◽  
G. Hammer
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
High Accuracy ◽  
Correct Classification ◽  
Pixel Classification ◽  
Study Region ◽  
Enhanced Vegetation Index ◽  
Multi Temporal ◽  
Crop Area ◽  
Pixel Scale

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April–November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

scholarly journals Greening and Browning Trends across Peru’s Diverse Environments

2020 ◽  
Vol 12 (15) ◽  
pp. 2418 ◽  
Author(s):  
Molly H. Polk ◽  
Niti B. Mishra ◽  
Kenneth R. Young ◽  
Kumar Mainali
Keyword(s):  
Time Series ◽  
Protected Area ◽  
Landscape Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Elevation Gradient ◽  
Positive Trend ◽  
Scientific Legacy ◽  
Alexander Von Humboldt ◽  
Moderate Resolution Imaging Spectroradiometer

If he were living today, Alexander von Humboldt would be using current technology to evaluate change in the Andes. Inspired by von Humboldt’s scientific legacy and the 2019 celebrations of his influence, we utilize a Moderate Resolution Imaging Spectroradiometer (MODIS)time-series vegetation index to ask questions of landscape change. Specifically, we use an 18-year record of Normalized Difference Vegetation Index (NDVI) data as a proxy to evaluate landscape change in Peru, which is well known for its high biological and ecological diversity. Continent-level evaluations of Latin America have shown sites with a positive trend in NDVI, or “greening” and “browning”, a negative trend in NDVI that suggests biophysical or human-caused reductions in vegetation. Our overall hypothesis was that the major biomes in Peru would show similar NDVI change patterns. To test our expectations, we analyzed the NDVI time-series with Thiel-Sen regression and evaluated Peru overall, by protected area status, by biome, and by biome and elevation. Across Peru overall, there was a general greening trend. By protected area status, surprisingly, the majority of greening occurred outside protected areas. The trends were different by biome, but there were hotspots of greening in the Amazon, Andean Highlands, and Drylands where greening dominated. In the Tropical Subtropical Dry Broadleaf Forest biome, greening and browning signals were mixed. Greening trends varied across the elevation gradient, switching from greening, to browning, and then back to greening as elevation increased. By biome and elevation, the results were variable. We further explored biome-specific drivers of greening and browning drawing on high-resolution imagery, the literature, and field expertise, much as we imagine von Humboldt might have approached similar questions of landscape dynamism.

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

scholarly journals VEGETATION DYNAMICS TREND USING SATELLITE TIME SERIES IMAGERY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 783-788
Author(s):  
Z. Najafi ◽  
P. Fatehi ◽  
A. A. Darvishsefat
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Vegetation Dynamics ◽  
Kendall Test ◽  
Significant Trend ◽  
Least Square ◽  
Positive Trend ◽  
Vegetation Dynamic ◽  
Ordinary Least Square ◽  
Ordinary Least Square Regression

Abstract. In this study, the trend of vegetation dynamics in Kermanshah city assessed using NDVI MOD13Q1 product over the time period of 2000–2017. Based on time series imagery the pick of vegetation phenology stage (maximum NDVI) identified, then the trend of vegetation dynamic was investigated using the Ordinary Least Square regression and the Theil-Sen approaches. To generate a pixel-wise trend map, a pixel-based vegetation dynamics was also implemented. A non-parametric Mann-Kendall statistics approach was used to examine a statistically significant trend analysis. The mean maximum NDVI observed for the first half or second half of April. Trend analysis using regression and Theil-Sen methods indicated a no-trend in vegetation fractions. The pixel-based trend assessment using regression showed that a 50% of the study area faced a positive trend and reaming part faced a negative trend. The Theil-Sen method revealed the no-trend for a large majority of area. The Mann-Kendall test indicated that only 20 percent of the area shows a statistically significant trend.

scholarly journals Forest disturbances and the attribution derived from yearly Landsat time series over 1990–2020 in the Hengduan Mountains Region of Southwest China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yating Li ◽  
Zhenzi Wu ◽  
Xiao Xu ◽  
Hui Fan ◽  
Xiaojia Tong ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Management ◽  
Biodiversity Conservation ◽  
Forest Disturbance ◽  
Road Construction ◽  
Google Earth ◽  
Forest Area ◽  
Hengduan Mountains ◽  
Natural Forests ◽  
Forest Disturbances

Abstract Background Natural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation. Methods We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model. Results The conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km2 (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value. Conclusions This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China’s logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.

scholarly journals THE ESTIMATE OF THE SPATIAL-TEMPORAL FEATURES OF VEGETATION COVER OF KAZAKHSTAN BASED ON TIME SERIES SATELLITE INDECES IN 2000-2015

2016 ◽  
Vol XLI-B8 ◽  
pp. 1067-1071
Author(s):  
I. Vitkovskaya ◽  
M. Batyrbayeva ◽  
L. Spivak
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Condition Index ◽  
Negative Trend ◽  
Positive Trend ◽  
High Productivity ◽  
Temporal Features

The article presents the evaluation of spatial-temporal characteristics of Kazakhstan arid and semi-arid areas' vegetation on the basis of time series of differential and integral vegetation indices. It is observed the negative trend of integral indices for the period of 2000-2015. This fact characterizes the increase of stress influence of weather conditions on vegetation in Kazakhstan territory during last decade. Simultaneously there is a positive trend of areas of zones with low values of IVCI index. Zoning of the territory of Kazakhstan was carried out according to the long-term values of the normalized integral vegetation index, which is characteristic of the accumulated amount of green season biomass. Negative trend is marked for areas of high productivity zones, long-term changes in the areas of low productivity zones have tend to increase. However long-term values of the area of the middle zone are insignificantly changed. Location boundaries of this zone in the latitudinal direction connects with a weather conditions of the year: all wet years, the average area is located between 46°- 49°N, and the all dry years - between 47°30'- 54°N. The map of frequency of droughts was formed by low values of the integral vegetation condition index which calculated from satellite data.

Sign in / Sign up

Export Citation Format

Share Document