Decreased photosynthetic efficiency in plant species exposed to multiple airborne pollutants along the Russian-Norwegian border

2000 ◽  
Vol 78 (8) ◽  
pp. 1021-1033 ◽  
Author(s):  
Ann Marie Odasz-Albrigtsen ◽  
Hans Tømmervik ◽  
Patrick Murphy
Keyword(s):  
Chlorophyll Fluorescence ◽  
Plant Species ◽  
Photosynthetic Efficiency ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Cover ◽  
Mixed Forest ◽  
Airborne Pollutants ◽  
Fluorescence Measurements ◽  
Study Sites

Photosynthetic efficiency was estimated by chlorophyll fluorescence measurements (Fv/Fm) in 11 plant species growing along a steep gradient of airborne pollution along the Russian-Norwegian border (70°N, 30°E). Photosynthetic efficiency was positively correlated with environmental variables including annual temperature and a maritime gradient and was negatively correlated with the airborne concentrations of Cu, Ni, and SO2 from the Cu-Ni smelters. Photosynthetic efficiency in six plant species from the mixed forest, but not pine (Pinus sylvestris L.), and three species from the birch forest was inversely correlated with SO2 and the concentrations of Ni and Cu in lichens. Measurement of fluorescence in these species was a sensitive indicator of pollutant impact. Plant cover at the 16 study sites and the photosynthetic efficiency of five target species correlated with normalized difference vegetation index (NDVI) values. This study demonstrated that it is possible to detect relations among field-measured ecophysiological responses in plants, levels of airborne pollutants, and satellite remote-sensed data.Key words: chlorophyll fluorescence, smelters, sulfur dioxide, nickel, copper, normalized difference vegetation index (NDVI).

scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

scholarly journals Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska

2015 ◽  
Author(s):  
Santonu Goswami ◽  
John Gamon ◽  
Sergio Vargas ◽  
Craig Tweedie
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Species ◽  
Vegetation Index ◽  
Plant Biomass ◽  
Plant Cover ◽  
Strongly Correlated ◽  
Area Index ◽  
Spectral Bands ◽  
Biomass Plant

Here we investigate relationships between NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species near Barrow, Alaska. We explore how key plant species differ in biomass, leaf area index (LAI) and how can vegetation spectral indices be used to estimate biomass and LAI for key plant species. A vegetation index (VI) or a spectral vegetation index (SVI) is a quantitative predictor of plant biomass or vegetative vigor, usually formed from combinations of several spectral bands, whose values are added, divided, or multiplied in order to yield a single value that indicates the amount or vigor of vegetation. For six key plant species, NDVI was strongly correlated with biomass (R2 = 0.83) and LAI (R2 = 0.70) but showed evidence of saturation above a biomass of 100 g/m2 and an LAI of 2 m2/m2. Extrapolation of a biomass-plant cover model to a multi-decadal time series of plant cover observations suggested that Carex aquatilis and Eriophorum angustifolium decreased in biomass while Arctophila fulva and Dupontia fisheri increased 1972-2008.

scholarly journals Chlorophyll Fluorescence Kinetics May Be Useful to Identify Early Drought and Irrigation Effects on Photosynthetic Apparatus in Field-Grown Wheat

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1275
Author(s):  
Lenka Botyanszka ◽  
Marek Zivcak ◽  
Erik Chovancek ◽  
Oksana Sytar ◽  
Viliam Barek ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Photosynthetic Apparatus ◽  
Optical Methods ◽  
Accurate Information ◽  
Ojip Transient ◽  
Fluorescence Kinetics ◽  
Non Invasive

To assess the reliability and sensitivity of non-invasive optical methods to detect the early effects of water deficit in the field, we analyzed the time-series of non-invasive measurements obtained in a dry season in a representative collection of wheat genotypes grown in small-plot field trials, in non-irrigated and irrigated variants. Despite a progressive water deficit and significant yield loss, the measurements indicated very minor changes in chlorophyll content or canopy cover. This corresponded well to the insignificant differences in spectral reflectance normalized difference vegetation index (NDVI) values. On the other hand, we identified the significant and rapid response of fast fluorescence kinetics data following the onset of irrigation. Analysis of parameters showed the main effects of drought were associated with changes in the amplitude of the I–P phase of the OJIP transient, indicating changes at the level of photosystem I and beyond. Statistical analyses identified the integrative parameter performance index PItot as the most sensitive parameter, which well-reflects the differences in responses of the genotypes to water deficit. Our results suggest that focusing on photosynthetic functions detected by the rapid chlorophyll fluorescence records can provide more accurate information on the drought stress level, compared to the structural data obtained by absorbance or reflectance measurements.

scholarly journals Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

2021 ◽  
Vol 13 (21) ◽  
pp. 4466
Author(s):  
Isabell Eischeid ◽  
Eeva M. Soininen ◽  
Jakob J. Assmann ◽  
Rolf A. Ims ◽  
Jesper Madsen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Predictive Ability ◽  
Ground Cover ◽  
Arctic Tundra ◽  
The Arctic ◽  
Ecosystem Monitoring ◽  
Selection Of Variables ◽  
State Changes ◽  
Study Sites

The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs.

Relationship of soil moisture with solar-induced chlorophyll fluorescence and normalized difference vegetation index in different phenological stages

2020 ◽  
Author(s):  
Qiu Shen ◽  
Jianjun Wu ◽  
Leizhen Liu ◽  
Wenhui Zhao
Keyword(s):  
Soil Moisture ◽  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Maturity Stage ◽  
Phenological Stages ◽  
Active Radiation ◽  
Relationship Of ◽  
The Relationship

&lt;p&gt;As an important part of water cycle in terrestrial ecosystem, soil moisture (SM) provides essential raw materials for vegetation photosynthesis, and its changes can affect the photosynthesis process and further affect vegetation growth and development. Thus, SM is always used to detect vegetation water stress and agricultural drought. Solar-induced chlorophyll fluorescence (SIF) is signal with close ties to photosynthesis and the normalized difference vegetation index (NDVI) can reflect the photosynthetic characteristics and photosynthetic yield of vegetations. However, there are few studies looking at the sensitivity of SIF and NDVI to SM changes over the entire growing season that includes multiple phenological stages. By making use of GLDAS-2 SM products along with GOME-2 SIF products and MODIS NDVI products, we discussed the detailed differences in the relationship of SM with SIF and NDVI in different phenological stages for a case study of Northeast China in 2014. Our results show that SIF integrates information from the fraction of photosynthetically active radiation (fPAR), photosynthetically active radiation (PAR) and SIF&lt;sub&gt;yield&lt;/sub&gt;, and is more effective than NDVI for monitoring the spatial extension and temporal dynamics of SM on a short time scale during the entire growing season. Especially, SIF&lt;sub&gt;PAR_norm&lt;/sub&gt; is the most sensitive to SM changes for eliminating the effects of seasonal variations in PAR. The relationship of SM with SIF and NDVI varies for different vegetation cover types and phenological stages. SIF is more sensitive to SM changes of grasslands in the maturity stage and &amp;#160;rainfed&amp;#160;croplands&amp;#160; in the senescence stage than NDVI, and it has significant sensitivities to SM changes of forests in different phenological stages. The sensitivity of SIF and NDVI to SM changes in the senescence stages stems from the fact that vegetation photosynthesis is relatively weaker at this time than that in the maturity stage, and vegetations in the reproductive growth stage still need much water. Relevant results are of great significance to further understand the application of SIF in SM detection.&lt;/p&gt;

scholarly journals Quantitative Assessment of Land Cover Sensitivity to Desertification in Maigatari Local Government Area, Jigawa State, Nigeria

2020 ◽  
Vol 24 (9) ◽  
pp. 1509-1517
Author(s):  
A. Ahmed ◽  
S. Abba ◽  
F. Siriki ◽  
B. Maman
Keyword(s):  
Land Cover ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Depth ◽  
Plant Cover ◽  
Organic Matter Content ◽  
Matter Content ◽  
Landsat 8 ◽  
Topographic Map

Desertification alludes to land degradation in arid, semi-arid and sub-humid regions resulting from various variables, counting climatic variations  and human activities. When land degradation transpire within the world’s drylands. It regularly makes desert-like conditions. Land degradation  occurs all over, but is characterized as desertification when it occurs within the drylands. The study employed adjusted MEDALUS methodology  using eleven indicators rainfall, evapotranspiration, aridity, soil texture, soil depth, slope gradient, drainage density, plant cover, erosion protection, sensitivity desertification index and Normalized Difference Vegetation Index (NDVI). Remote Sensing and GIS were the main techniques used in the indices computations and mapping. Thus, Shuttle Rader Topographic Map (SRTM) and Landsat 8 satellite imagery for the year 2019 with 30 meter  resolution, captured in the month of August (rainy season), covering the study area were acquired from Global Land cover Facility (GLCF) University of Maryland. The study finds that the duration and intensity of rainfall is declining especially at the edge of the desert, extreme north and western part of the area. Rain quickly drained through infiltration and surface runoff which carried the little nutrients attached to the soil. Rainfall and  climate is of arid type recording about 300-400mm of rainfall and the soil is low in organic matter content making it weak and less fertile and support only the cultivation of cereals and legumes. The study recommends that there is need to strengthen the laws and policies in controlling  desertification and land degradation, establishment of shelterbelts to control desertification and act also as wind breakers and encourage the use of  modern techniques such as drip irrigation to check the rate of infiltration and runoff. Keyword: Desertification; Sensitivity; MEDALUS; GIS; Maigatari

scholarly journals EVALUATION OF THE POTENTIAL OF GREEN ROOFS APPLIED TO AN URBAN FABRIC USING GIS AND REMOTE SENSING DATA CASE OF THE NADOR CITY / MOROCCO

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 177-184
Author(s):  
R. Lambarki ◽  
E. Achbab ◽  
M. Maanan ◽  
H. Rhinane
Keyword(s):  
Surface Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Cover ◽  
Green Roofs ◽  
Living Environment ◽  
Green Spaces ◽  
Surface Model ◽  
Landsat 8 ◽  
Gis And Remote Sensing

Abstract. Accelerated urban growth has affected many of the planet's natural processes. In cities, most of the surface is covered with asphalt and cement, which has changed the water and air cycles. To restore the balance of urban ecosystems, cities must find the means to create green spaces in an increasingly gray world. Green spaces provide the city and its inhabitants a better living environment. This article uses Nador city as a case study area, this project consists in studying the possibility for the roofs to receive vegetation. The first axis of this project is the quantification of the current vegetation cover at ground level by calculating the Normalized Difference Vegetation Index (NDVI) based on Satellite images Landsat 8, then the classification of the LiDAR point cloud, and the generation of a digital surface model (DSM) of the urban area. This type of derived data was used as the basis for the various stages of estimating the potential plant cover at the roof level. In order to study the different possible scenarios, a set of criteria was applied, such as the minimum roof area, the inclination and the duration of the sunshine on the roof, which is calculated using the linear model of angstrom Prescott based on solar radiation. The study shows that in the most conservative scenario, 21771 suitable buildings that had to be redeveloped into green roofs, with an appropriate surface area of 369.26Ha allowing a 63,40% increase in the city's green space by compared to the current state contributing to the improvement of the quality of life and urban comfort. The average budget for the installation of green roofs in a building with a surface area of 100 m2 varies between 60000dh and 170000dh depending on the type of green roofs used, extensive or intensive. These results would enable planners and researchers in green architecture sciences to carry out more detailed planning analyzes.

scholarly journals Capturing the Impact of the 2018 European Drought and Heat across Different Vegetation Types Using OCO-2 Solar-Induced Fluorescence

2020 ◽  
Vol 12 (19) ◽  
pp. 3249
Author(s):  
Ankit Shekhar ◽  
Jia Chen ◽  
Shrutilipi Bhattacharjee ◽  
Allan Buras ◽  
Antony Oswaldo Castro ◽  
...  
Keyword(s):  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Terrestrial Ecosystem ◽  
Added Value ◽  
Summer Drought ◽  
Vegetation Types ◽  
The Impact

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

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