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.

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 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 The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

2020 ◽  
Vol 12 (12) ◽  
pp. 1979
Author(s):  
Dandan Xu ◽  
Deshuai An ◽  
Xulin Guo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Area Index ◽  
Plant Area Index ◽  
The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

scholarly journals Mapping Regional Ecosystem Functional Types Based on Sentinel-2 Satellite Imagery

2020 ◽  
Vol 194 ◽  
pp. 05047
Author(s):  
Rong Liu ◽  
Fang Huang ◽  
Yue Ren
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Terrestrial Ecosystems ◽  
Ecosystem Functions ◽  
Series Data ◽  
Fine Scale ◽  
Functional Types ◽  
Sentinel 2

Ecosystem functional types (EFTs) are the patches of land surface showing similar in carbon dynamics. EFTs are not defined by the structure and composition of vegetation and represent the spatial heterogeneity of ecosystem functions. Identifying EFTs based on low-resolution satellite remote sensing data cannot satisfy the needs of fine-scale characterization of regional ecosystem functional patterns. Here, taking Zhenlai County, Northeast China as an example, the heterogeneity in ecosystem functions was characterized by identifying EFTs from Sentinel-2 time series data using ISODATA algorithm. Ecosystem functional attributes derived from dynamics of the normalized difference vegetation index (NDVI), the fraction of absorbed photosynthetically active radiation (FAPAR), and canopy water content (CWC) in the growing season were calculated. The correspondence analysis (CA) method was used to reveal relationships between the EFTs and land cover types. Our results showed that the nine selected remotely sensed variables indicating carbon and water flux of the regional ecosystems could be adopted in ecosystem functions classification. The obtained EFTs based on Sentinel-2 images reflected the internal structure of carbon balance well and the distribution pattern of ecosystem functional diversity a fine scale. This study helps to understand the functional heterogeneity pattern of temperate terrestrial ecosystems.

scholarly journals A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

2020 ◽  
Vol 12 (8) ◽  
pp. 3227 ◽  
Author(s):  
Anjar Dimara Sakti ◽  
Wataru Takeuchi
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Time Series Data ◽  
Global Analysis ◽  
Local Level ◽  
Series Data ◽  
Pixel Resolution ◽  
Cropping Intensity

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.

scholarly journals UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification

2020 ◽  
Vol 12 (3) ◽  
pp. 529 ◽  
Author(s):  
Hualiang Liu ◽  
Feizhou Zhang ◽  
Lifu Zhang ◽  
Yukun Lin ◽  
Siheng Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Series Data ◽  
Land Cover Mapping ◽  
Landsat 8 ◽  
System Modelling ◽  
Temporal Profiles

Land cover data is crucial for earth system modelling, natural resources management, and conservation planning. Remotely sensed time-series data capture dynamic behavior of vegetation, and have been widely used for land cover mapping. Temporal profiles of vegetation index (VI), especially normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are the most used features derived from time-series spectral data. Whether NDVI or EVI is optimal to generate temporal profiles has not been evaluated. The universal normalized vegetation index (UNVI), a relatively new index with all spectral bands incorporated, has been proved to be more effective than several commonly used satellite-derived VIs in some application scenarios. In this study, we explored the ability of UNVI time series for discriminating different vegetation types in Chaoyang prefecture, northeast China, in comparison with normalized NDVI, EVI, triangle vegetation index (TVI), and tasseled cap transformation greenness (TCG). These five indices were calculated using Landsat 8 surface reflectance data, and two comparative experiments were conducted. The first experiment analyzed class separabilities using pairwise JM (Jeffries–Matusita) distance as indicator, and the results showed that UNVI was superior to EVI, TVI, and TCG, and almost equivalent to NDVI, especially during the peak of vegetation growing season and for the most indistinguishable vegetation pair broadleaf and shrubs. The second experiment compared the vegetation classification accuracies using the features of these VI temporal profiles and the corresponding phenological parameters, and the results showed that UNVI can better classify the five major vegetation in Chaoyang prefecture than other four indices. Therefore, we conclude that UNVI time series has considerable potential for regional land cover mapping, and we recommend that the use of the UNVI is considered in the future time series related studies.

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 Assessment of Ecological Disturbance Caused by Flood and Fire in Assam Forests, India, Using MODIS Time Series Data of 2001-2011

2020 ◽  
Author(s):  
Dibyendu Dutta ◽  
Akanksha Balha ◽  
Prabir Kumar Das ◽  
Pragyan Jain ◽  
Libeesh Lukose ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Fire ◽  
Land Surface ◽  
Vegetation Index ◽  
Time Series Data ◽  
Forest Area ◽  
Series Data ◽  
Ecological Disturbance ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

The forest area of Assam State is known for its rich biodiversity. In the present study, the disturbance regime within the Assam forest area caused by periodic flood and forest fire, was assessed using the Moderate Resolution Imaging Spectroradiometer (MODIS) time-series (2001–2011) data. The MODIS Global Disturbance Index (MGDI) images were generated using MODIS derived Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) images. The temporal intensity of flood and forest fire in sixteen representative forests was analyzed to develop the MGDI based thresholds for detecting the disturbed area. The threshold for the non-instantaneous disturbance, i.e. flood, was found to be 107% whereas it was 111% for instantaneous disturbance, i.e. forest fire. The thresholds were applied on the MGDI images to delineate disturbed caused by flood and fire, separately for each year. The time-series disturbance areas were integrated over the years (2001–2011) to generate the classified disturbance prone maps.

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