scholarly journals PHENOLOGICAL CHARACTERIZATION OF COFFEE CROP (Coffea arabica L.) FROM MODIS TIME SERIES

2013 ◽  
Vol 31 (4) ◽  
pp. 569 ◽  
Author(s):  
Antônio Felipe Couto Júnior ◽  
Osmar Abílio de Carvalho Júnior ◽  
Éder De Souza Martins ◽  
Antônio Fernando Guerra
Keyword(s):  
Time Series ◽  
Coffea Arabica ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Median Filter ◽  
Series Data ◽  
Arabica Coffee ◽  
Enhanced Vegetation Index ◽  
Original Dataset ◽  
Coffea Arabica L

ABSTRACT. Arabica Coffee (Coffea arabica L.) demonstrates a two-year phenological cycle, this knowledge is important for crop forecast in Brazil. This work aimed to describe the coffee crop phenology from MODIS vegetation index time series. The study area is located in the western Bahia State, Brazil, due to its remarkable agribusiness development. MODIS time series data comprehended 10-year Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). However, these times series are usually contaminated by noise caused by atmospheric variations that are harmful to the surface discrimination. Median filter and the Minimum Noise Fraction (MNF) were used together to smooth the original dataset. NDVI and EVI temporal profiles showed differences of amplitude and gradient. The results evidenced the Arabica Coffee phenological stages, as described in previous fieldworks. These results showed potential application for large-area land cover monitoring.Keywords: vegetation index, remote sensing, digital image processing. RESUMO. O Café Arábica (Coffea arabica L.) apresenta um ciclo fenológico de dois anos, sendo relevante o seu conhecimento para a previsão de safras no Brasil. O objetivo deste trabalho foi caracterizar a fenologia da cultura de café a partir de séries temporais de índices de vegetação do sensor MODIS. A área de estudo está localizada no oeste do estado da Bahia, Brasil, devido ao seu notável desenvolvimento do agronegócio. As séries temporais MODIS compreendem10 anos do Normalized Difference Vegetation Index (NDVI) e Enhanced Vegetation Index (EVI). Contudo, essas séries temporais apresentam ruídos ocasionados por efeitos atmosféricos queprejudicam a discriminação dos alvos da superfície. O filtro de mediana e a transformação Fração Mínima de Ruído (FMR) foram usados em conjunto para suavizar os dados originais. Os perfis temporais NDVI e EVI apresentam diferenças de amplitude e gradiente. Os resultados evidenciaram os estágios fenológico do Café Arábica, como descritos em prévios trabalhos de campo. Esses resultados possuem potencial de aplicação para o monitoramento do uso da terra em extensas áreas.Palavras-chave: índices de vegetação, sensoriamento remoto, processamento digital de imagem.

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.

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 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 MODIS DERIVED NDVI BASED TIME SERIES ANALYSIS OF VEGETATION IN THE JODHPUR AREA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 535-539 ◽  
Author(s):  
S. K. Yadav ◽  
S. L. Borana
Keyword(s):  
Time Series ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Arid Zone ◽  
Temporal Analysis ◽  
Series Data ◽  
Modis Ndvi ◽  
Post Monsoon

<p><strong>Abstract.</strong> Arid region of India shows vast variation in climate and vegetation during last two decades. In order to analysis impact of monsoonal patterns on the vegetation indices of the arid zone, a three years (2015&amp;ndash;2017) temporal series Moderate Resolution Image Spectrometer (MODIS) data for Pre &amp; Post Monsoon was used for computing Normalized Difference Vegetation Index (NDVI). The cloud-free NDVI time series data are used to study the relationship between the rainfall pattern and the vegetation changes in Jodhpur District. ENVI and ArcGIS image processing software are used to evaluate and monitor the vegetation for the pre-monsoon and post-monsoon seasons for three years. Enormous changes were observed during pre and post monsoon temporal analysis. This study shows that MODIS NDVI data is best suited for quick vegetation assessment in arid region.</p>

scholarly journals Assessment of Rangeland Degradation in New Mexico Using Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND)

2021 ◽  
Vol 13 (9) ◽  
pp. 1618
Author(s):  
Melakeneh G. Gedefaw ◽  
Hatim M. E. Geli ◽  
Temesgen Alemayehu Abera
Keyword(s):  
Time Series ◽  
New Mexico ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rangeland Management ◽  
Environmental Benefits ◽  
Drought Severity ◽  
Assessment Framework ◽  
Rangeland Degradation

Rangelands provide significant socioeconomic and environmental benefits to humans. However, climate variability and anthropogenic drivers can negatively impact rangeland productivity. The main goal of this study was to investigate structural and productivity changes in rangeland ecosystems in New Mexico (NM), in the southwestern United States of America during the 1984–2015 period. This goal was achieved by applying the time series segmented residual trend analysis (TSS-RESTREND) method, using datasets of the normalized difference vegetation index (NDVI) from the Global Inventory Modeling and Mapping Studies and precipitation from Parameter elevation Regressions on Independent Slopes Model (PRISM), and developing an assessment framework. The results indicated that about 17.6% and 12.8% of NM experienced a decrease and an increase in productivity, respectively. More than half of the state (55.6%) had insignificant change productivity, 10.8% was classified as indeterminant, and 3.2% was considered as agriculture. A decrease in productivity was observed in 2.2%, 4.5%, and 1.7% of NM’s grassland, shrubland, and ever green forest land cover classes, respectively. Significant decrease in productivity was observed in the northeastern and southeastern quadrants of NM while significant increase was observed in northwestern, southwestern, and a small portion of the southeastern quadrants. The timing of detected breakpoints coincided with some of NM’s drought events as indicated by the self-calibrated Palmar Drought Severity Index as their number increased since 2000s following a similar increase in drought severity. Some breakpoints were concurrent with some fire events. The combination of these two types of disturbances can partly explain the emergence of breakpoints with degradation in productivity. Using the breakpoint assessment framework developed in this study, the observed degradation based on the TSS-RESTREND showed only 55% agreement with the Rangeland Productivity Monitoring Service (RPMS) data. There was an agreement between the TSS-RESTREND and RPMS on the occurrence of significant degradation in productivity over the grasslands and shrublands within the Arizona/NM Tablelands and in the Chihuahua Desert ecoregions, respectively. This assessment of NM’s vegetation productivity is critical to support the decision-making process for rangeland management; address challenges related to the sustainability of forage supply and livestock production; conserve the biodiversity of rangelands ecosystems; and increase their resilience. Future analysis should consider the effects of rising temperatures and drought on rangeland degradation and productivity.

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