Understanding the Resilience of Sal and Teak Forests to Climate Variability Using NDVI and EVI Time Series

2021 ◽  
Vol 67 (2) ◽  
pp. 192-204
Author(s):  
Maneesh Kumar Patasaraiya ◽  
Rinku Moni Devi ◽  
Bhaskar Sinha ◽  
Jigyasa Bisaria ◽  
Sameer Saran ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Central India ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Spatial And Temporal Scales ◽  
Changing Climate ◽  
Tiger Reserve ◽  
Sal Forest

Abstract This study attempts to understand the climatic resilience of two forest types of central India—that is, Tectona grandis (Teak) forest of Satpura Tiger Reserve and Shorea robusta (Sal) forest of Kanha Tiger Reserve—using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) extracted from MODIS, and climate variable data sets at highest spatial and temporal scales. Teak and Sal forests within the core area of the selected tiger reserves represent the least anthropogenic disturbances, and therefore, the observed changes in NDVI and EVI over the past 16 years could be analyzed in the context of climate change. The correlation analysis between climatic variables (minimum temperature, maximum temperature, mean temperature, and total annual rainfall) and forest response indicators (NDVI/EVI) at seasonal and annual scales revealed that Teak and Sal forests are more sensitive to change in past temperature as compared with rainfall. Also, the changes in NDVI and EVI of Sal forest are correlated more to minimum temperature, and that of Teak forest to maximum temperature. The analysis of sapling girth class of Sal and Teak further revealed that Sal as compared with Teak is more affected because of the changing climate variables of the recent past. The findings of the study will help manage forests more efficiently in the context of changing climate.

scholarly journals Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peixin Ren ◽  
Zelin Liu ◽  
Xiaolu Zhou ◽  
Changhui Peng ◽  
Jingfeng Xiao ◽  
...  
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Time Lag ◽  
Evergreen Forest ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Study Region ◽  
Cumulative Precipitation

Abstract Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

scholarly journals Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

2021 ◽  
Vol 13 (2) ◽  
pp. 323
Author(s):  
Liang Chen ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Chao Yang ◽  
Xiaorong Lu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Maximum Temperature Difference ◽  
Rapid Urbanization ◽  
The Impact ◽  
The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals Surveillance along the Rio Grande during the 2020 Vesicular Stomatitis Outbreak Reveals Spatio-Temporal Dynamics of and Viral RNA Detection in Black Flies

Pathogens ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1264
Author(s):  
Katherine I. Young ◽  
Federico Valdez ◽  
Christina Vaquera ◽  
Carlos Campos ◽  
Lawrence Zhou ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Rio Grande ◽  
Maximum Temperature ◽  
Black Flies ◽  
Southern Mexico ◽  
Black Fly ◽  
The Usa ◽  
Vesicular Stomatitis

Vesicular stomatitis virus (VSV) emerges periodically from its focus of endemic transmission in southern Mexico to cause epizootics in livestock in the US. The ecology of VSV involves a diverse, but largely undefined, repertoire of potential reservoir hosts and invertebrate vectors. As part of a larger program to decipher VSV transmission, we conducted a study of the spatiotemporal dynamics of Simulium black flies, a known vector of VSV, along the Rio Grande in southern New Mexico, USA from March to December 2020. Serendipitously, the index case of VSV-Indiana (VSIV) in the USA in 2020 occurred at a central point of our study. Black flies appeared soon after the release of the Rio Grande’s water from an upstream dam in March 2020. Two-month and one-year lagged precipitation, maximum temperature, and vegetation greenness, measured as Normalized Difference Vegetation Index (NDVI), were associated with increased black fly abundance. We detected VSIV RNA in 11 pools comprising five black fly species using rRT-PCR; five pools yielded a VSIV sequence. To our knowledge, this is the first detection of VSV in the western US from vectors that were not collected on premises with infected domestic animals.

scholarly journals Rainfall Trend and Its Relationship with Normalized Difference Vegetation Index in a Restored Semi-Arid Wetland of South Africa

2020 ◽  
Vol 12 (21) ◽  
pp. 8919
Author(s):  
Florence M. Murungweni ◽  
Onisimo Mutanga ◽  
John O. Odiyo
Keyword(s):  
South Africa ◽  
Quantile Regression ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rainfall Data ◽  
Ecosystem Change ◽  
Annual Rainfall ◽  
Rainfall Trend ◽  
Vegetation Productivity ◽  
Rainfall Trends

Clearance of terrestrial wetland vegetation and rainfall variations affect biodiversity. The rainfall trend–NDVI (Normalized Difference Vegetation Index) relationship was examined to assess the extent to which rainfall affects vegetation productivity within Nylsvley, Ramsar site in Limpopo Province, South Africa. Daily rainfall data measured from eight rainfall stations between 1950 and 2016 were used to generate seasonal and annual rainfall data. Mann-Kendall and quantile regression were applied to assess trends in rainfall data. NDVI was derived from satellite images from between 1984 and 2003 using Zonal statistics and correlated with rainfall of the same period to assess vegetation dynamics. Mann-Kendall and Sen’s slope estimator showed only one station had a significant increasing rainfall trend annually and seasonally at p < 0.05, whereas all the other stations showed insignificant trends in both rainfall seasons. Quantile regression showed 50% and 62.5% of the stations had increasing annual and seasonal rainfall, respectively. Of the stations, 37.5% were statistically significant at p < 0.05, indicating increasing and decreasing rainfall trends. These rainfall trends show that the rainfall of Nylsvley decreased between 1995 and 2003. The R2 between rainfall and NDVI of Nylsvley is 55% indicating the influence of rainfall variability on vegetation productivity. The results underscore the impact of decadal rainfall patterns on wetland ecosystem change.

scholarly journals Estimation of Crop Water Deficit in Lower Bari Doab, Pakistan Using Reflection-Based Crop Coefficient

2020 ◽  
Vol 9 (3) ◽  
pp. 173
Author(s):  
Muhammad Asif Javed ◽  
Sajid Rashid Ahmad ◽  
Wakas Karim Awan ◽  
Bilal Ahmed Munir
Keyword(s):  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Coefficient ◽  
Maximum Temperature ◽  
Filter Method ◽  
Command Area ◽  
Landsat 8 ◽  
Crop Water ◽  
Crop Coefficients

There is a global realization in all governmental setups of the need to provoke the efficient appraisal of crop water budgeting in order to manage water resources efficiently. This study aims to use the satellite remote sensing techniques to determine the water deficit in the crop rich Lower Bari Doab Canal (LBDC) command area. Crop classification was performed using multi-temporal NDVI profiles of Landsat-8 imagery by distinguishing the crop cycles based on reflectance curves. The reflectance-based crop coefficients (Kc) were derived by linear regression between normalized difference vegetation index (NDVI) cycles of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MYD13Q1 products and Food and Agriculture Organization (FAO) defined crop coefficients. A MODIS 250 m NDVI product of the last 10 years (2004-2013) was used to identify the best performing crop cycle using Fourier filter method. The meteorological parameters including rainfall and temperature substantiated the reference evapotranspiration (ET0) calculated using the Hargreaves method. The difference of potential ET and actual ET, derived from the reflectance-based Kc calculated using reference NDVI and current NDVI, generates the water deficit. Results depict the strong correlation between ET, temperature and rainfall, as the regions having maximum temperature resulted in high ET and low rainfall and vice versa. The derived Kc values were observed to be accurate when compared with the crop calendar. Results revealed maximum water deficit at middle stage of the crops, which were observed to be particularly higher at the tail of the canal command. Moreover, results also depicted that kharif (summer) crops suffer higher deficit in comparison to rabi (winter) crops due to higher ET demand caused by higher temperature. Results of the research can be utilized for rational allocation of canal supplies and guiding farmers towards usage of alternate sources to avoid crop water stress.

scholarly journals Hydro-climatic and land use/cover changes in Nasia catchment of the White Volta basin in Ghana

2021 ◽  
Author(s):  
Emmanuel Nyadzi ◽  
Enoch Bessah ◽  
Gordana Kranjac-Berisavljevic ◽  
Fulco Ludwig
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Resources ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Sunshine Duration ◽  
Climatic Variables ◽  
Northern Ghana ◽  
Maximum Temperature

AbstractThe Nasia catchment is the reservoir with significant surface water resources in Northern Ghana and home to numerous subsistence farmers engaged in rainfed and dry season irrigation farming. Yet, there is little understanding of the hydro-climatic and land use/cover conditions of this basin. This study investigated trends, relationships and changes in hydro-climatic variables and land use/cover in addition to implications of the observable changes in the Nasia catchment over a period of 50 years. Parameters used for the study were minimum (Tmin) and maximum temperature (Tmax), wind speed (WS), sunshine duration (S), rainfall (R), relative humidity (RH), discharge (D) and potential evapotranspiration (PET) data, 15 years of remotely sensed normalized difference vegetation index (NDVI) data and 30 years of land use/cover image data. Results show that Tmin, Tmax, WS and PET have increased significantly (p < 0.05) over time. RH and S significantly declined. R, D and NDVI have not decreased significantly (p > 0.05). A significant abrupt change in almost all hydro-climatic variables started in the 1980s, a period that coincides with the occurrence of drought events in the region, except WS in 2001, R in 1968 and D in 1975, respectively. Also, D showed a positive significant correlation with RH, R and PET, but an insignificant positive relationship with S. D also showed a negative insignificant correlation with Tmin, Tmax and WS. Areas covered with shrubland and settlement/bare lands have increased to the disadvantage of cropland, forest, grassland and water bodies. It was concluded that climate change impact is quite noticeable in the basin, indicating water scarcity and possibilities of droughts. The analysis performed herein is a vital foundation for further studies to simulate and predict the effect of climate change on the water resources, agriculture and livelihoods in the Nasia catchment.

scholarly journals Vertical Differences in the Long-Term Trends and Breakpoints of NDVI and Climate Factors in Taiwan

2021 ◽  
Vol 13 (22) ◽  
pp. 4707
Author(s):  
Hui Ping Tsai ◽  
Geng-Gui Wang ◽  
Zhong-Han Zhuang
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Positive Effects ◽  
Central Regions ◽  
Long Term Trends

This study explored the long-term trends and breakpoints of vegetation, rainfall, and temperature in Taiwan from overall and regional perspectives in terms of vertical differences from 1982 to 2012. With time-series Advanced Very-High-Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data and Taiwan Climate Change Estimate and Information Platform (TCCIP) gridded monthly climatic data, their vertical dynamics were investigated by employing the Breaks for Additive Seasonal and Trend (BFAST) algorithm, Pearson’s correlation analysis, and the Durbin–Watson test. The vertical differences in NDVI values presented three breakpoints and a consistent trend from positive (1982 to 1989) to negative at varied rates, and then gradually increased after 2000. In addition, a positive rainfall trend was discovered. Average and maximum temperature had similar increasing trends, while minimum temperature showed variations, especially at higher altitudes. In terms of regional variations, the vegetation growth was stable in the north but worse in the central region. Higher elevations revealed larger variations in the NDVI and temperature datasets. NDVI, along with average and minimum temperature, showed their largest changes earlier in higher altitude areas. Specifically, the increasing minimum temperature direction was more prominent in the mid-to-high-altitude areas in the eastern and central regions. Seasonal variations were observed for each region. The difference between the dry and wet seasons is becoming larger, with the smallest difference in the northern region and the largest difference in the southern region. Taiwan’s NDVI and climatic factors have a significant negative correlation (p < 0.05), but the maximum and minimum temperatures have significant positive effects at low altitudes below 500 m. The northern and central regions reveal similar responses, while the south and east display different feedbacks. The results illuminate climate change evidence from assessment of the long-term dynamics of vegetation and climatic factors, providing valuable references for establishing correspondent climate-adaptive strategies in Taiwan.

scholarly journals Vegetation Change and Its Response to Climate Change between 2000 and 2016 in Marshes of the Songnen Plain, Northeast China

2020 ◽  
Vol 12 (9) ◽  
pp. 3569 ◽  
Author(s):  
Yanji Wang ◽  
Xiangjin Shen ◽  
Ming Jiang ◽  
Xianguo Lu
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Maximum Temperature ◽  
Climate Data ◽  
Songnen Plain ◽  
Asymmetric Effects ◽  
Climate Change Effects

Songnen Plain is a representative semi-arid marshland in China. The Songnen Plain marshes have undergone obvious loss during the past decades. In order to protect and restore wetland vegetation, it is urgent to investigate the vegetation change and its response to climate change in the Songnen Plain marshes. Based on the normalized difference vegetation index (NDVI) and climate data, we investigated the spatiotemporal change of vegetation and its relationship with temperature and precipitation in the Songnen Plain marshes. During 2000–2016, the growing season mean NDVI of the Songnen Plain marshes significantly (p < 0.01) increased at a rate of 0.06/decade. For the climate change effects on vegetation, the growing season precipitation had a significant positive effect on the growing season NDVI of marshes. In addition, this study first found asymmetric effects of daytime maximum temperature (Tmax) and nighttime minimum temperature (Tmin) on NDVI of the Songnen Plain marshes: The growing season NDVI correlated negatively with Tmax but positively with Tmin. Considering the global asymmetric warming of Tmax and Tmin, more attention should be paid to these asymmetric effects of Tmax and Tmin on the vegetation of marshes.

scholarly journals Agrometeorological conditions associated with phenological responses of tucumã-do-pará (Astrocaryum vulgare Mart.)

2018 ◽  
Vol 42 (2) ◽  
pp. 127-137
Author(s):  
Lucieta Guerreiro Martorano ◽  
Maria do Socorro Padilha de Oliveira ◽  
Gleidson Guilherme Caldas Mendes ◽  
José Reinaldo da Silva Cabral de Moraes ◽  
Daniel Pereira Pinheiro ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Amazon Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Positive Association ◽  
High Rate ◽  
Maximum Temperature ◽  
Palm Species ◽  
Thermal Amplitude

ABSTRACT Palms are among the class of hyperdominant species in the Amazon region, and for the tucumã palm (Astrocaryum vulgare Mart.) demand of climatic and phenological information in order to support strategic planning and sustainable management of this palm species native to the Amazon basin. The objective of this work was to evaluate agrometeorological conditions associated to phenological responses of tucumã as a species that has high economic potential for fruit pulp production. Meteorological data were collected during the period in which data were also collected for the phenology of the germplasm bank. Sensors were installed to monitor temperature and air relative humidity to where they are observed as phenophases. Analyses were conducted to identify the responses of the tucumã stems in function of agrometeorological conditions of the study area. Precipitation, thermal amplitude, and insolation showed positive correlations principally with respect to the percent of stems with bracts, inflorescences, or with fertilized inflorescences. In the fruiting phenological phase precipitation and air relative humidity influenced the percentage of stems with fruit clusters that were immature and also ones with mature clusters. High maximum temperatures compromise the expression of the percentage of stems with green fruit clusters. The tucumã stems were photosynthesizing and carrying out metabolic processes at a very high rate during the study period based on the high Normalized Difference Vegetation Index which was superior to 0.41 during the three years of this study. The tucumã phenological phases, demonstrating a strong positive association with insolation, maximum temperature and thermal amplitude.

Sign in / Sign up

Export Citation Format

Share Document