scholarly journals RGB Spectral Indices for the Analysis of Soil Protection by Vegetation Cover against Erosive Processes

2020 ◽  
Author(s):  
Henry Antonio Pacheco Gil ◽  
Argenis de Jesús Montilla Pacheco
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Vegetation Cover ◽  
Near Infrared ◽  
Coastal Region ◽  
Soil Protection ◽  
Spectral Indices ◽  
Geospatial Technologies ◽  
Rgb Images ◽  
Airborne Sensors ◽  
Good Number

The vegetation cover plays a fundamental role in protecting the soil from erosive processes. Many researchers have developed investigations for the calculation of the RUSLE C Factor, with the use of operating bands in the near infrared. With the current advances in Geospatial Technologies, there are a good number of RGB airborne sensors in Unmanned Aerial Vehicles (UVA). The objective of this chapter is to evaluate some RGB indexes, proposed in the literature, for the protection of the soil from erosive processes by vegetation cover, in a region with a high agricultural vocation. The methodology consisted of capturing RGB images in an area of the Ecuadorian coastal region and calculating in thematic indices, within the visible one, which offer the possibility of quickly differentiating vegetation from other types of coverage on the ground. The evaluation allowed to define which indexes present the best results and adaptation to the type of crop or plant mass mapped, and to propose their use for zoning of risk of erosion under the agro-ecological conditions of the study area.

scholarly journals Fusarium Wilt of Radish Detection Using RGB and Near Infrared Images from Unmanned Aerial Vehicles

2020 ◽  
Vol 12 (17) ◽  
pp. 2863 ◽  
Author(s):  
L. Minh Dang ◽  
Hanxiang Wang ◽  
Yanfen Li ◽  
Kyungbok Min ◽  
Jin Tae Kwak ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Fusarium Wilt ◽  
Near Infrared ◽  
Wilt Disease ◽  
Pollinator Decline ◽  
Main Research ◽  
Aerial Vehicles ◽  
Segmented Images ◽  
Rgb Images ◽  
Side Dishes

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset.

scholarly journals An Experimental Study on Field Spectral Measurements to Determine Appropriate Daily Time for Distinguishing Fractional Vegetation Cover

2020 ◽  
Vol 12 (18) ◽  
pp. 2942
Author(s):  
Du Lyu ◽  
Baoyuan Liu ◽  
Xiaoping Zhang ◽  
Xihua Yang ◽  
Liang He ◽  
...  
Keyword(s):  
Local Time ◽  
Vegetation Cover ◽  
Near Infrared ◽  
Experimental Studies ◽  
Bare Soil ◽  
Spectral Indices ◽  
Spectral Measurements ◽  
Fractional Vegetation Cover ◽  
Sensing Technology ◽  
Different Seasons

Remote sensing technology has been widely used to estimate fractional vegetation cover (FVC) at global and regional scales. Accurate and consistent field spectral measurements are required to develop and validate spectral indices for FVC estimation. However, there are rarely any experimental studies to determine the appropriate times for field spectral measurements, and the existing guidelines or references are rather general or inconsistent, it is still not agreed upon and detailed experiments are missing for a local research. In this experiment, five groundcover objects were measured continuously from 07:30 a.m. to 17:30 p.m. local time in three consecutive sunny days using a portable spectrometer. The coefficients of variation (CV) were applied to investigate the reflectance variation at wavelengths corresponding to MODIS satellite channels and the derived spectral indices used to estimate FVC, including photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV). The results reveal little variation in the reflectance measured between 10:00 a.m. and 16:00 p.m., with CV values generally less than 10%. The CV values of FVC spectral indices for estimating PV, NPV and bare soil (BS) are generally less than 3%. While more experiments are yet to be carried out at different locations and in different seasons, the findings so far imply that the in situ spectrum measured between 9:00 a.m. and 17:00 p.m. local time would be useful to discriminate FVC objects and validate satellite estimates-based indices using visible, near-infrared and shortwave infrared channels.

scholarly journals Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

2021 ◽  
Vol 13 (11) ◽  
pp. 2045
Author(s):  
Anaí Caparó Bellido ◽  
Bradley C. Rundquist
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Important Variable ◽  
Field Station ◽  
Near Surface ◽  
Fractional Snow Cover ◽  
Rgb Images ◽  
Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

Reflectance characteristics of cumulative defoliation of balsam fir

1988 ◽  
Vol 18 (8) ◽  
pp. 1008-1016 ◽  
Author(s):  
D. G. Leckie ◽  
P. M. Teillet ◽  
G. Fedosejevs ◽  
D. P. Ostaff
Keyword(s):  
Near Infrared ◽  
Spectral Characteristics ◽  
Broad Band ◽  
Integrating Sphere ◽  
Simple Relationship ◽  
Spectral Bands ◽  
Infrared Wavelengths ◽  
Remote Sensing Techniques ◽  
Middle Infrared ◽  
Airborne Sensors

Knowledge of the spectral characteristics of trees with varying degrees of needle loss is essential for developing remote sensing techniques for assessing defoliation. Spectra covering the range 400–2400 nm were acquired for single tree crowns suffering varying degrees of cumulative defoliation due to the spruce budworm (Choristoneurafumiferana (Clem.)), using a spectrometer mounted in the bucket of a boom truck. Spectra over the range 360–1100 nm were also obtained for the components of defoliated trees (i.e., needles, bare branches, and lichen), using a separate spectrometer and integrating sphere. Estimates of defoliation symptoms of each tree were made from the ground and above the tree. Changes in reflectance had a close and simple relationship with the defoliation symptoms measured. The spectral differences due to cumulative defoliation that were observed were broad-band features. The best spectral regions for differentiating levels of cumulative defoliation symptoms were the blue, red, shorter near-infrared wavelengths, and middle-infrared. Although currently available satellite and airborne sensors operate in these spectral regions, defoliation assessment may be improved by the use of optimized spectral bands.

scholarly journals Satellite data interpretation of causes and controls on groundwater-seawater flow directions, Merseyside, UK: implications for assessing saline intrusions

2005 ◽  
Vol 2 (3) ◽  
pp. 887-916 ◽  
Author(s):  
S. Mukherjee ◽  
E. A. Mohammad ◽  
R. H. Worden
Keyword(s):  
Satellite Data ◽  
Intertidal Zone ◽  
Near Infrared ◽  
Water Movement ◽  
Coastal Region ◽  
Chloride Concentration ◽  
Fault Zones ◽  
Fault Rocks ◽  
Grain Crushing ◽  
Clay Deposits

Abstract. Groundwater in the Triassic Sherwood Sandstone aquifer, Liverpool, UK, has locally elevated chloride concentrations (~4000 mg/l) in parts of the coastal region although there is freshwater right up to the coast line in other areas. The aquifer is cut my numerous faults with vertical displacements of as much 300 m. SPOT satellite data have been used for the Merseyside area of Liverpool. The satellite data revealed and confirmed the location of some of the main faults since the fault zones of the aquifer have low permeability (due to grain crushing, cataclasis, and clay smearing). Where fault zones outcrop at the surface, below the well-developed regolith, there is locally elevated soil water and thus anomalous vegetation patterns in comparison to unfaulted and highly porous aquifer. The ability to identify fault zones by this satellite-based method strongly suggests that they are at least partially sealing, sub-vertical features in the aquifer. Digitally enhanced and processed satellite data were used to define the relative proportions of sand and clay in the near-coastal (inter-tidal) part of the Mersey estuary. Sand-dominated sediment has higher pixel values in comparison with clay deposits in the near infrared spectral region (NIR). Where open and weathered fault rocks crop out at the surface near the intertidal zone, water movement in these potential surface water conduits is limited where the intertidal zone is clay-dominated since clay will plug the conduit. Where these weathered and open fault-rocks crop out against sand-dominated parts of the coastline, fresh water outflux into the seawater has been imaged using the satellite data. Furthermore, the high and low chloride concentration parts of the aquifer are separated by major, sub-vertical fault zones and have allowed a very steep water table gradient to remain in the aquifer.

scholarly journals Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs)

2019 ◽  
Author(s):  
Kelly Easterday ◽  
Chippie Kislik ◽  
Tod E. Dawson ◽  
Sean Hogan ◽  
Maggi Kelly
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Water Content ◽  
Water Deficit ◽  
Water Status ◽  
Field Measurements ◽  
Flying Height ◽  
Plant Water ◽  
Spectral Indices ◽  
Aerial Vehicles ◽  
The Relationship

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors present an opportunity to monitor vegetation with on-demand high spatial and temporal resolution. In this study, we use multispectral imagery from quadcopter UAVs to monitor the progression of a water manipulation experiment on a common shrub, Baccharis pilularis (coyote brush), at the Blue Oak Ranch Reserve (BORR) near San Jose, California. We recorded multispectral data from the plants at several altitudes with nearly hourly intervals to explore the relationship between two common spectral indices, NDVI and NDRE, and plant water content and water potential, as physiological metrics of plant water status, across a gradient of water deficit. An examination of the spatial and temporal thresholds at which water limitations were most detectable revealed that the best separation between levels of water deficit were at higher resolution (lower flying height), and in the morning (NDVI) and early morning (NDRE). We found that both measures were able to identify moisture deficit in plants and distinguish them from control and watered plants; however, NDVI was better able to distinguish between treatments than NDRE and was more positively correlated with field measurements of plant water content than NDRE. Finally, we explored how relationships between spectral indices and water status changed when the imagery was scaled to courser resolutions provided by satellite-based imagery (PlanetScope) and found that PlanetScope data was able to capture the overall trend in treatments but was not able to capture subtle changes in water content. These kinds of experiments that evaluate the relationship between direct field measurements and UAV camera sensitivity are needed to enable translation of field-based physiology measurements to landscape or regional scales.

scholarly journals Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

2021 ◽  
Vol 13 (20) ◽  
pp. 4125
Author(s):  
Weiping Kong ◽  
Wenjiang Huang ◽  
Lingling Ma ◽  
Lingli Tang ◽  
Chuanrong Li ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Water Content ◽  
Near Infrared ◽  
Field Experiments ◽  
Estimation Method ◽  
Middle Layer ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Growth Stages ◽  
Spectral Indices

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

Thermal and multispectral images from Unmanned Aerial Vehicles (UAVs) for water presence detection in temporary streams: first results

2021 ◽  
Author(s):  
Massimo Micieli ◽  
Gianluca Botter ◽  
Giuseppe Mendicino ◽  
Alfonso Senatore
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Near Infrared ◽  
Infrared Band ◽  
Temporary Streams ◽  
Water Index ◽  
Long Wavelength ◽  
Research Activities ◽  
Aerial Vehicles ◽  
Normalized Difference Water Index ◽  
First Results

<p>UAVs (Unmanned Aerial Vehicles) are increasingly used for monitoring river networks with a broad range of purposes. In this contribution, we focus on the use of multispectral sensors, either in the thermal infrared band LWIR (Long-wavelength infrared, 8-15 µm) or in the infrared band NIR (Near-infrared, 0.75-1.4 µm) to map network dynamics in temporary streams. Specifically, we discuss the first results of a set of surveys carried out in 2020 within a small river catchment located in northern Calabria (southern Italy), as part of the research activities of the ERC-funded DyNET project. Preliminary, a rigorous methodology was identified to perform on-site surveys and to process and analyse the acquired images. Experimental results show that the combined use of LWIR and NIR sensors is a suitable solution for detecting water presence in channels characterized by different hydraulic and morphologic conditions. LWIR sensors alone allow one to discriminate water presence only when the thermal contrast with the surrounding environment is high. On the other hand, NIR sensors permit to detect the presence of water in most of the analyzed settings through the estimate of the Normalized Difference Water Index (NDWI). However, NIR sensors can be misled in case of shallow water depth, due to the NIR radiation emitted by the riverbed merging with that of the water. Overall, the study demonstrates that a combined LWIR/NIR approach allows addressing a broader range of conditions. Moreover, the information provided can be further enhanced by combining it with geomorphologic information and basic hydraulic concepts.</p>

scholarly journals Identifying drought events in sugarcane using drought indices derived from Modis sensor

2017 ◽  
Vol 52 (11) ◽  
pp. 1063-1071 ◽  
Author(s):  
Michelle Cristina Araujo Picoli ◽  
Daniel Garbellini Duft ◽  
Pedro Gerber Machado
Keyword(s):  
Near Infrared ◽  
Sensor Data ◽  
Drought Indices ◽  
Spectral Indices ◽  
Seasonal Fluctuations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Aqua Satellite ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Shortwave Infrared

Abstract: The objective of this work was to evaluate the potential of several spectral indices, used on moderate resolution imaging spectroradiometer (Modis) images, in identifying drought events in sugarcane. Images of Terra and Aqua satellites were used to calculate the spectral indices, using visible (red), near infrared, and shortwave infrared bands, and eight indices were selected: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI, and MSI. The indices were calculated using images between October and April of the crop years 2007/08, 2008/09, 2009/10, and 2013/14. These indices were then correlated with the standardized precipitation-evapotranspiration index (SPEI), calculated for 1, 3, and 6 months. Four of them had significant correlations with SPEI: GVMI, MSI, NDI7, and NDWI. Spectral indices from Modis sensor on board the Aqua satellite (MYD) were more suited for drought detection, and March provided the most relevant indices for that purpose. Drought indices calculated from Modis sensor data are effective for detecting sugarcane drought events, besides being able to indicate seasonal fluctuations.

scholarly journals Determination of snow-covered area in different land covers in central Alaska, U.S.A., from aircraft data — April 1995

1998 ◽  
Vol 26 ◽  
pp. 149-155 ◽  
Author(s):  
Dorothy K. Hall ◽  
James L. Foster ◽  
Alfred T. C. Chang ◽  
Carl S. Benson ◽  
Janet Y. L. Chien
Keyword(s):  
Snow Cover ◽  
Vegetation Cover ◽  
Near Infrared ◽  
Mixed Forest ◽  
Mixed Forests ◽  
Mapping Algorithm ◽  
Covered Area ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cover Density

During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance, Ri was calculated from the visible/near-infrared channels of the MAS sensor. The Ri was used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of < 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is > 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).

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