Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR

Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., Green−Red/Green+Red>0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P<0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.

Download Full-text

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

Sustainability ◽

10.3390/su12187303 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7303

Author(s):

Carolina Fabbri ◽

Marco Napoli ◽

Leonardo Verdi ◽

Marco Mancini ◽

Simone Orlandini ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Sustainability Assessment ◽

Stem Elongation ◽

N Fertilization ◽

Management Tool ◽

Growth Stages ◽

Greenhouse Gasses ◽

Crop Parameters ◽

N Management

A preliminary study was conducted to analyze the sustainability of barley production through: (i) investigating sensor-based nitrogen (N) application on barley performance, compared with conventional N management (CT); (ii) assessing the potential of the Normalized Difference Vegetation Index (NDVI) at different growth stages for within-season predictions of crop parameters; and (iii) evaluating sensor-based fertilization benefits in the form of greenhouse gasses mitigation. Barley was grown under CT, sensor-based management (RF) and with no N fertilization (Control). NDVI measurements and RF fertilization were performed using a GreenSeeker™ 505 hand-held optical sensor. Gas emissions were measured using a static chamber method with a portable gas analyzer. Results showed that barley yield was not statistically different under RF and CF, while they both differed significantly from Control. Highly significant positive correlations were observed between NDVI and production parameters at harvesting from the middle of stem elongation to the medium milk stage across treatments. Our findings suggest that RF is able to decrease CO2 emission in comparison with CF. The relationship between N fertilization and CH4 emission showed high variability. These preliminary results provide an indication of the benefits achieved using a simple proximal sensing methodology to support N fertilization.

Download Full-text

Remote Sensing of Canopy Cover in Horticultural Crops

HortScience ◽

10.21273/hortsci.43.2.333 ◽

2008 ◽

Vol 43 (2) ◽

pp. 333-337 ◽

Cited By ~ 55

Author(s):

Thomas J. Trout ◽

Lee F. Johnson ◽

Jim Gartung

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Digital Camera ◽

Canopy Cover ◽

Absolute Error ◽

Growth Stages ◽

Important Indicator ◽

Horticultural Crops ◽

Wide Range

Canopy cover (CC) is an important indicator of stage of growth and crop water use in horticultural crops. Remote sensing of CC has been studied in several major crops, but not in most horticultural crops. We measured CC of 11 different annual and perennial horticultural crops in various growth stages on 30 fields on the west side of California's San Joaquin Valley with a handheld multispectral digital camera. Canopy cover was compared with normalized difference vegetation index (NDVI) values calculated from Landsat 5 satellite imagery. The NDVI was highly correlated and linearly related with measured CC across the wide range of crops, canopy structures, and growth stages (R2 = 0.95, P < 0.01) and predicted CC with mean absolute error of 0.047 up to effective full cover. These results indicate that remotely sensed NDVI may be an efficient way to monitor growth stage, and potentially irrigation water demand, of horticultural crops.

Download Full-text

Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

Remote Sensing ◽

10.3390/rs12020220 ◽

2020 ◽

Vol 12 (2) ◽

pp. 220 ◽

Cited By ~ 2

Author(s):

Han Xiao ◽

Fenzhen Su ◽

Dongjie Fu ◽

Qi Wang ◽

Chong Huang

Keyword(s):

Spatial Distribution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Mangrove Ecosystem ◽

Early Growth ◽

Growth Stages ◽

Human Disturbances ◽

Vegetation Growth ◽

Two Parameters ◽

The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

Download Full-text

Illumination Correction of Multi-Time RGB Images Obtained with an Unmanned Aerial Vehicle

Light & Engineering ◽

10.33383/2020-038 ◽

2021 ◽

pp. 50-58

Author(s):

Michael Yu. Kataev ◽

Maria M. Dadonova ◽

Dmitry S. Efremenko

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Digital Camera ◽

Quantitative Result ◽

Efficient Solutions ◽

Acquisition Time ◽

Time Interval ◽

Agricultural Field ◽

Aerial Vehicle ◽

Rgb Images

The goal of this research was to study and optimize multi-temporal RGB images obtained by a UAV (unmanned aerial vehicle). A digital camera onboard the UAV allows obtaining data with a high temporal and spatial resolution of ground objects. In the case considered by us, the object of study is agricultural fields, for which, based on numerous images covering the agricultural field, image mosaics (orthomosaics) are constructed. The acquisition time for each orthomosaic takes at least several hours, which imposes a change in the illuminance of each image, when considered separately. Orthomosaics obtained in different periods of the year (several months) will also differ from each other in terms of illuminance. For a comparative analysis of different parts of the field (orthomosaic), obtained in the same time interval or comparison of areas for different periods of time, their alignment by illumination is required. Currently, the majority of alignment approaches rely rather on colour (RGB) methods, which cannot guarantee finding efficient solutions, especially when it is necessary to obtain a quantitative result. In the paper, a new method is proposed that takes into account the change in illuminance during the acquisition of each image. The general formulation of the problem of light correction of RGB images in terms of assessing the colour vegetation index Greenness is considered. The results of processing real measurements are presented.

Download Full-text

Assessing water demand with remote sensing for two coriander varieties

Agronomía Colombiana ◽

10.15446/agron.colomb.v36n3.71809 ◽

2018 ◽

Vol 36 (3) ◽

pp. 266-273

Author(s):

Euseppe Ortiz ◽

Enrique A. Torres

Keyword(s):

Remote Sensing ◽

Water Balance ◽

Water Demand ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Digital Camera ◽

Crop Coefficient ◽

Remote Sensors ◽

The Relationship ◽

Important Basis

The use of remote sensing to determine water needs has been successfully applied by several authors to different crops, maintaining, as an important basis, the relationship between the normalized difference vegetation index (NDVI) and biophysical variables, such as the fraction of coverage (fc) and the basal crop coefficient (Kcb). Therefore, this study quantified the water needs of two varieties of coriander (UNAPAL Laurena CL and UNAPAL Precoso CP) based on the response of fc and Kcb, using remote sensors and a water balance according to the FAO-56 methodology. A Campbell Scientific meteorological station, a commercial digital camera and a portable spectro radiometer were used to obtain information on the environmental conditions and the crop. By means of remote sensing associated with a water balance, it was found that the water demand was 156 mm for CL and 151 mm for CP until the foliage harvest (41 d after sowing); additionally, the initial Kcb was 0.14, the mean Kcb was 1.16 (approximately) and the final Kcb was 0.71 (approximately).

Download Full-text

A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

Remote Sensing ◽

10.3390/rs11232757 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2757 ◽

Cited By ~ 6

Author(s):

Akash Ashapure ◽

Jinha Jung ◽

Anjin Chang ◽

Sungchan Oh ◽

Murilo Maeda ◽

...

Keyword(s):

Comparative Study ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Canopy Cover ◽

Unmanned Aircraft ◽

Estimation Methods ◽

Green Vegetation ◽

Multi Temporal ◽

Rgb Images ◽

Multispectral Sensors

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

Download Full-text

New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

Remote Sensing ◽

10.3390/rs12244170 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4170

Author(s):

Pengfei Chen ◽

Fangyong Wang

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Biomass Estimation ◽

Growth Stages ◽

Spectral Indices ◽

Green Band ◽

Validation Data ◽

Texture Parameters ◽

Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

Download Full-text

Correlation of the vegetation index (NDVI) and phytometric parameters at different stages of field crops development

PROCEEDINGS OF V INTERNATIONAL SCIENTIFIC CONFERENCE “CURRENT STATE, PROBLEMS AND PROSPECTS OF THE DEVELOPMENT OF AGRARIAN SCIENCE” ◽

10.33952/2542-0720-2020-5-9-10-123 ◽

2020 ◽

Author(s):

Yu.A. Gulyanov ◽

Keyword(s):

Spring Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Stem Elongation ◽

Grain Filling ◽

Growing Season ◽

Practical Application ◽

Field Crops ◽

The Relationship

The main goal of our research was to identify the relationship between the normalized difference vegetation index (NDVI) and the area of assimilation surface (AS) of spring wheat crops during the growing season, as well as to develop practical application of the findings. Throughout the growing season, the area of assimilation surface of T. aestivum increases much faster than the vegetation index NDVI. The smallest AS (282.7 m2/ha), which corresponded to 0.01 units of the NDVI (calculated factor) was observed during the tillering stage. It reaches its maximum values – 331.7–406.1–383.7 m2/ha (1.20–1.47–1.39 times higher) from stem elongation to the end of flowering. During the grain filling and maturation, these values decrease to 336.2 m2/ha but still are 1.19 times higher than the initial ones.

Download Full-text

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

Remote Sensing ◽

10.3390/rs13214466 ◽

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.

Download Full-text

INVESTIGATING MANGROVE FRAGMENTATION CHANGES USING LANDSCAPE METRICS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-159-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 159-162

Author(s):

M. Baharlouii ◽

D. Mafi Gholami ◽

M. Abbasi

Keyword(s):

Climate Change ◽

Landscape Metrics ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat Images ◽

Surrounding Water ◽

Maximum Likelihood Classification ◽

High Degree ◽

Drought Occurrence

Abstract. Generally, investigation of long-term mangroves fragmentation changes can be used as an important tool in assessing sensitivity and vulnerability of these ecosystems to the multiple environmental hazards. Therefore, the aim of this study was to reveal the trend of mangroves fragmentation changes in Khamir habitat using satellite imagery and Fragstats software during a 30-year period (1986–2016). To this end, Landsat images of 1986, 1998, and 2016 were used and after computing the normalized difference vegetation index (NDVI) to distinguish mangroves from surrounding water and land areas, images were further processed and classified into two types of land cover (i.e., mangrove and non-mangrove areas) using the maximum likelihood classification method. By determining the extent of mangroves in the Khamir habitat in the years of 1986, 1998 and 2017, the trend of fragmentation changes was quantified using CA, NP, PD and LPI landscape metrics. The results showed that the extent of mangroves in Khamir habitat (CA) decreased in the period post-1998 (1998–2016). The results also showed that, the NP and PD increased in the period of post-1998 and in contrast, the LPI decrease in this period. These results revealed the high degree of vulnerability of mangroves in Khamir habitat to the drought occurrence and are thus threatened by climate change. We hope that the results of this study stimulate further climate change adaptation planning efforts and help decision-makers prioritize and implement conservative measures in the mangrove ecosystems on the northern coasts of the PG and the GO and elsewhere.

Download Full-text