scholarly journals Analyzing Ecological Vulnerability and Vegetation Phenology Response Using NDVI Time Series Data and the BFAST Algorithm

2020 ◽  
Vol 12 (20) ◽  
pp. 3371
Author(s):  
Jiani Ma ◽  
Chao Zhang ◽  
Hao Guo ◽  
Wanling Chen ◽  
Wenju Yun ◽  
...  
Keyword(s):  
Time Series ◽  
Ecological Restoration ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Jilin Province ◽  
Series Data ◽  
Vegetation Growth ◽  
Growing Period ◽  
Ndvi Time Series

Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long time period. In this study, a novel method is presented to identify ecologically vulnerable areas based on the normalized difference vegetation index (NDVI) time series from MOD09A1. Here, ecologically vulnerable areas are defined as those that experienced negative changes frequently and greatly in vegetation growth after the disturbances during 2000–2018. The number and magnitude of negative changes detected by the Breaks for Additive Season and Trend (BFAST) algorithm based on the NDVI time-series data were combined to identify ecologically vulnerable areas. TIMESAT was then used to extract the phenology metrics from an NDVI time series dataset to characterize the vegetation responses due to the abrupt negative changes detected by the BFAST algorithm. Focus was given to Jilin Province, a region of China known to be ecologically vulnerable because of frequent drought. The results showed that 13.52% of the study area, mostly in Jilin Province, is ecologically vulnerable. The vulnerability of trees is the lowest, while that of sparse vegetation is the highest. The response of phenology is such that the relative amount of vegetation biomass and length of the growing period were decreased by negative changes in growth for dense vegetation types. The present research results will be useful for the protection of environments being disturbed by regional ecological restoration.

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 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.

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 The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 139 ◽  
Author(s):  
Yingying Yang ◽  
Taixia Wu ◽  
Shudong Wang ◽  
Jing Li ◽  
Farhan Muhanmmad
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Distribution Data ◽  
Landsat 8 ◽  
Oxygen Balance

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

scholarly journals Analysis of Grassland Degradation in Zona da Mata, MG, Brazil, Based on NDVI Time Series Data with the Integration of Phenological Metrics

2019 ◽  
Vol 11 (24) ◽  
pp. 2956
Author(s):  
Marcos C. Hott ◽  
Luis M. T. Carvalho ◽  
Mauro A. H. Antunes ◽  
João C. Resende ◽  
Wadson S. D. Rocha
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Multicriteria Analysis ◽  
Series Data ◽  
Full Time ◽  
Socioeconomic Indicators ◽  
Ndvi Time Series ◽  
Phenological Metrics

There is currently a lot of interest in determining the state of Brazilian grasslands. Governmental actions and programs have recently been implemented for grassland recovery in Brazilian states, with the aim of improving production systems and socioeconomic indicators. The aim of this study is to evaluate the vegetative growth, temporal vigor, and long-term scenarios for the grasslands in Zona da Mata, Minas Gerais State, Brazil, by integrating phenological metrics. We used metrics derived from the normalized difference vegetation index (NDVI) time series from moderate resolution imaging spectroradiometer (MODIS) data, which were analyzed in a geographic information system (GIS), using multicriteria analysis, the analytical hierarchy process, and a simplified expert system (ESS). These temporal metrics, i.e., the growth index (GI) for 16-day periods during the growing season; the slope; and the maximum, minimum, and mean for the time series, were integrated to investigate the grassland vegetation conditions and degradation level. The temporal vegetative vigor was successfully described using the rescaled range (R/S statistic) and the Hurst exponent, which, together with the metrics estimated for the full time series, imagery, and field observations, indicated areas undergoing degradation or areas that were inadequately managed (approximately 61.5%). Time series analysis revealed that most grasslands showed low or moderate vegetative vigor over time with long-term persistence due to farming practices associated with burning and overgrazing. A small part of the grasslands showed high and sustainable plant densities (approximately 8.5%). A map legend for grassland management guidelines was developed using the proposed method with remote sensing data, which were applied using GIS software and a field campaign.

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>

Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel

2010 ◽  
Vol 19 (1) ◽  
pp. 75 ◽  
Author(s):  
Willem J. D. van Leeuwen ◽  
Grant M. Casady ◽  
Daniel G. Neary ◽  
Susana Bautista ◽  
José Antonio Alloza ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Data Interpretation ◽  
Series Data ◽  
Vegetation Response ◽  
Burned Areas ◽  
The Usa

Due to the challenges faced by resource managers in maintaining post-fire ecosystem health, there is a need for methods to assess the ecological consequences of disturbances. This research examines an approach for assessing changes in post-fire vegetation dynamics for sites in Spain, Israel and the USA that burned in 1998, 1999 and 2002 respectively. Moderate Resolution Imaging Spectroradiometer satellite Normalized Difference Vegetation Index (NDVI) time-series data (2000–07) are used for all sites to characterise and track the seasonal and spatial changes in vegetation response. Post-fire trends and metrics for burned areas are evaluated and compared with unburned reference sites to account for the influence of local environmental conditions. Time-series data interpretation provides insights into climatic influences on the post-fire vegetation. Although only two sites show increases in post-fire vegetation, all sites show declines in heterogeneity across the site. The evaluation of land surface phenological metrics, including the start and end of the season, the base and peak NDVI, and the integrated seasonal NDVI, show promising results, indicating trends in some measures of post-fire phenology. Results indicate that this monitoring approach, based on readily available satellite-based time-series vegetation data, provides a valuable tool for assessing post-fire vegetation response.

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