scholarly journals Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1333
Author(s):  
Giuseppe Francesco Cesare Lama ◽  
Mariano Crimaldi ◽  
Vittorio Pasquino ◽  
Roberta Padulano ◽  
Giovanni Battista Chirico
Keyword(s):  
Riparian Vegetation ◽  
Vegetation Index ◽  
Arundo Donax ◽  
Water Bodies ◽  
Giant Reed ◽  
Multispectral Images ◽  
Drainage Channel ◽  
Area Index ◽  
Riparian Plants ◽  
The Impact

Estimating the main hydrodynamic features of real vegetated water bodies is crucial to assure a balance between their hydraulic conveyance and environmental quality. Riparian vegetation stands have a high impact on vegetated channels. The present work has the aim to integrate riparian vegetation’s reflectance indices and hydrodynamics of real vegetated water flows to assess the impact of riparian vegetation morphometry on bulk drag coefficients distribution along an abandoned vegetated drainage channel fully covered by 9–10 m high Arundo donax (commonly known as giant reed) stands, starting from flow average velocities measurements at 30 cross-sections identified along the channel. A map of riparian vegetation cover was obtained through digital processing of Unnamed Aerial Vehicle (UAV)-acquired multispectral images, which represent a fast way to observe riparian plants’ traits in hardly accessible areas such as vegetated water bodies in natural conditions. In this study, the portion of riparian plants effectively interacting with flow was expressed in terms of ground-based Leaf Area Index measurements (LAI), which easily related to UAV-based Normalized Difference Vegetation Index (NDVI). The comparative analysis between Arundo donax stands NDVI and LAI map enabled the analysis of the impact of UAV-acquired multispectral imagery on bulk drag predictions along the vegetated drainage channel.

The effect of reed beds distributions on the Ecohydraulic dynamics of wetlands and lowlands: experimental analyses and simulations

2021 ◽  
Author(s):  
Giuseppe Francesco Cesare Lama
Keyword(s):  
Water Quality ◽  
Spatial Distribution ◽  
Riparian Vegetation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Terrestrial Ecosystems ◽  
Water Bodies ◽  
Area Index ◽  
Reed Beds ◽  
Riparian Species

<p>The interplay between riparian vegetation and water flow in vegetated water bodies has a key role in the dynamic evolution of aquatic and terrestrial ecosystems in wetlands and lowlands. The present study analyzes the effects of the spatial distribution of reed (<em>Phragmites australis</em> (Cav.) Trin. ex Steud.) beds, an invasive riparian species extremely widespread in wetland and lowlands worldwide, on the main hydraulic and hydrodynamic properties of an abandoned vegetated reclamation channel located in Northern Tuscany, Italy. A field campaign was carried out to obtain Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) of reed beds through both ground-based and Unmanned Aerial Vehicle (UAV) methodologies, and to correlate them to the channel’s flow dynamic and water quality main features. Then, Hydrodynamic simulations of the vegetated reclamation channel were performed and validated based on the experimental measurements of the hydraulic and vegetational parameters acquired in the field to build up a robust model to be employed also in future Ecohydraulic researches. The evidences of this study constitute useful insights in the quantitative analysis of the correlation between the spatial distribution of riparian vegetation stands in natural and manmade vegetated water bodies and their hydrodynamic and water quality main features.</p>

scholarly journals Monitoring Vegetation Greenness in Response to Climate Variation along the Elevation Gradient in the Three-River Source Region of China

2021 ◽  
Vol 10 (3) ◽  
pp. 193
Author(s):  
Zhaoqi Wang ◽  
Xiang Liu ◽  
Hao Wang ◽  
Kai Zheng ◽  
Honglin Li ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Source Region ◽  
Elevation Gradient ◽  
Climate Variation ◽  
Mean Annual Precipitation ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Vegetation Greenness ◽  
The Mean ◽  
The Impact

The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation)) and digital elevation model data to study the changes of VGEG (Vegetation Greenness along the Elevation Gradient) in the TRSR from 2001 to 2016. Results showed that the areas with a positive correlation of vegetation greenness and elevation accounted for 36.34 ± 5.82% of the study areas. The interannual variations of VGEG showed that the significantly changed regions were mainly observed in the elevation gradient of 4–5 km. The VGEG was strongest in the elevation gradient of 4–5 km and weakest in the elevation gradient of >5 km. Correlation analysis showed that the mean annual temperature was positively correlated with VIs, and the effect of the mean annual precipitation on VIs was more obvious at low altitude than in high altitude. This study contributes to our understanding of the VGEG variation in the TRSR under global climate variation and also helps in the prediction of future carbon cycle patterns.

scholarly journals Estimating the Biomass of Waterhyacinth (Eichhornia crassipes) Using the Normalized Difference Vegetation Index Derived from Simulated Landsat 5 TM

2015 ◽  
Vol 8 (2) ◽  
pp. 203-211 ◽  
Author(s):  
Wilfredo Robles ◽  
John D. Madsen ◽  
Ryan M. Wersal
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Simulated Data ◽  
Water Bodies ◽  
Biomass Estimation ◽  
Aquatic Weed ◽  
Target Area ◽  
Data Set ◽  
Area Index ◽  
Landsat 5 Tm

Waterhyacinth is a free-floating aquatic weed that is considered a nuisance worldwide. Excessive growth of waterhyacinth limits recreational use of water bodies as well as interferes with many ecological processes. Accurate estimates of biomass are useful to assess the effectiveness of control methods to manage this aquatic weed. While large water bodies require significant labor inputs with respect to ground-truth surveys, available technology like remote sensing could be capable of providing temporal and spatial information from a target area at a much reduced cost. Studies were conducted at Lakes Columbus and Aberdeen (Mississippi) during the growing seasons of 2005 and 2006 over established populations of waterhyacinth. The objective was to estimate biomass based on nondestructive methods using the normalized difference vegetation index (NDVI) derived from Landsat 5 TM simulated data. Biomass was collected monthly using a 0.10m2 quadrat at 25 randomly-located locations at each site. Morphometric plant parameters were also collected to enhance the use of NDVI for biomass estimation. Reflectance measurements using a hyperspectral sensor were taken every month at each site during biomass collection. These spectral signatures were then transformed into a Landsat 5 TM simulated data set using MatLab® software. A positive linear relationship (r2 = 0.28) was found between measured biomass of waterhyacinth and NDVI values from the simulated dataset. While this relationship appears weak, the addition of morphological parameters such as leaf area index (LAI) and leaf length enhanced the relationship yielding an r2 = 0.66. Empirically, NDVI saturates at high LAI, which may limit its use to estimate the biomass in very dense vegetation. Further studies using NDVI calculated from narrower spectral bands than those contained in Landsat 5 TM are recommended.

scholarly journals RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

2020 ◽  
Vol 12 (18) ◽  
pp. 2982 ◽  
Author(s):  
Christelle Gée ◽  
Emmanuel Denimal
Keyword(s):  
Biomass Production ◽  
Precision Agriculture ◽  
Weed Management ◽  
Vegetation Index ◽  
Management Strategies ◽  
Weed Competition ◽  
Growth Stages ◽  
Area Index ◽  
Crop Weed Competition ◽  
The Impact

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

scholarly journals Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6732
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Zeyu Wu ◽  
Yu Liang ◽  
Jianwen Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Field Experiments ◽  
Predictive Accuracy ◽  
Vegetation Indices ◽  
Planting Density ◽  
Coefficient Of Determination ◽  
Multispectral Images ◽  
Area Index

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

scholarly journals Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices

2018 ◽  
Vol 23 ◽  
pp. 00030 ◽  
Author(s):  
Anshu Rastogi ◽  
Subhajit Bandopadhyay ◽  
Marcin Stróżecki ◽  
Radosław Juszczak
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Environmental Manipulation ◽  
Chlorophyll Index ◽  
The Impact ◽  
Remote Sensing Indices

The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

scholarly journals Estimating malaria burden in Nigeria: a geostatistical modelling approach

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Nnadozie Onyiri
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Model ◽  
Vegetation Index ◽  
Malaria Prevalence ◽  
Malaria Risk ◽  
Water Bodies ◽  
Area Index ◽  
Environmental Covariates

This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA) database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age) and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC), distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria’s belt of middle regions generally has malaria prevalence of 40% and above.

scholarly journals Vegetation and Fluvial Geomorphology Dynamics after an Urban Fire

Geosciences ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 317
Author(s):  
Lauren E. H. Mathews ◽  
Alicia M. Kinoshita
Keyword(s):  
Laser Scanning ◽  
Small Sample Size ◽  
Arundo Donax ◽  
Small Sample ◽  
Giant Reed ◽  
Grass Species ◽  
Native Vegetation ◽  
Absolute Maximum ◽  
Field Observations ◽  
The Impact

The goal of this research was to characterize the impact of invasive riparian vegetation on burn severity patterns and fluvial topographic change in an urban Mediterranean riverine system (Med-sys) after fire in San Diego, California. We assessed standard post-fire metrics under urban conditions with non-native vegetation and utilized field observations to quantify vegetation and fluvial geomorphic processes. Field observations noted both high vegetation loss in the riparian area and rapidly resprouting invasive grass species such as Arundo donax (Giant Reed) after fire. Satellite-based metrics that represent vegetation biomass underestimated the initial green canopy loss, as did volumetric data derived from three-dimensional terrestrial laser scanning data. Field measurements were limited to a small sample size but demonstrated that the absolute maximum topographic changes were highest in stands of Arundo donax (0.18 to 0.67 m). This work is the first quantification of geomorphic alterations promoted by non-native vegetation after fire and highlights potential grass–fire feedbacks that can contribute to geomorphic disruption. Our results support the need for ground-truthing or higher resolution when using standard satellite-based indices to assess post-fire conditions in urban open spaces, especially when productive invasive vegetation are present, and they also emphasize restoring urban waterways to native vegetation conditions.

scholarly journals Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting

2019 ◽  
Vol 11 (21) ◽  
pp. 2558 ◽  
Author(s):  
Emily Myers ◽  
John Kerekes ◽  
Craig Daughtry ◽  
Andrew Russ
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Model Fitting ◽  
Series Data ◽  
Sigmoid Function ◽  
Absolute Deviation ◽  
Shape Model ◽  
Area Index ◽  
The Impact

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

Assessing and monitoring the vulnerability to drought and climate anomalies of Mediterranenan oak forests by using NDVI

2020 ◽  
Author(s):  
Maria Castellaneta ◽  
Angelo Rita ◽  
J. Julio Camarero ◽  
Michele Colangelo ◽  
Angelo Nolè ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Tree Mortality ◽  
Vegetation Index ◽  
Google Earth ◽  
Oak Forests ◽  
Summer Drought ◽  
Area Index ◽  
Ndvi Time Series ◽  
The Impact

<p>Several die-off episodes related to heat weaves and drought spells have evidenced the high vulnerability of Mediterranean oak forests. These events consisted in the loss in tree vitality and manifested as growths decline, elevated crown transparency (defoliation) and rising tree mortality rate. In this context, the changes in vegetation productivity and canopy greenness may represent valuable proxies to analyze how extreme climatic events trigger forest die-off. Such changes in vegetation status may be analyzed using remote-sensing data, specifically multi-temporal spectral information. For instance, the Normalized Difference Vegetation Index (NDVI) measures changes in vegetation greenness and is a proxy of changes in leaf area index (LAI), forest aboveground biomass and productivity. In this study, we analyzed the temporal patterns of vegetation in three Mediterranean oak forests showing recent die-off in response to the 2017 severe summer drought. For this purpose, we used an open-source platform (Google Earth Engine) to extract collections of MODIS NDVI time-series from 2000 to 2019. The analysis of both NDVI trends and anomalies were used to infer differential patterns of vegetation phenology among sites comparing plots where most trees were declining and showed high defoliation (test) versus plots were most trees were considered healthy (ctrl) and showed low or no defoliation. Here we discuss: i) the likely offset in NDVI time-series between test- versus ctrl- sites; and ii) the impact of summer droughts  on NDVI.</p><p><strong>Keywords</strong>: climate change, forest vulnerability, time series, remote sensing.</p>

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