Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms’ status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 µg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

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Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040257 ◽

2020 ◽

Vol 9 (4) ◽

pp. 257 ◽

Cited By ~ 3

Author(s):

Kiwon Lee ◽

Kwangseob Kim ◽

Sun-Gu Lee ◽

Yongseung Kim

Keyword(s):

Open Source ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Atmospheric Correction ◽

Urban Forestry ◽

Google Earth ◽

Landsat 8 ◽

Surface Reflectance ◽

Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

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HIERARCHICAL CLASSIFICATION FOR ASSESSMENT OF HORTICULTURAL CROPS IN MIXED CROPPING PATTERN USING UAV-BORNE MULTI-SPECTRAL SENSOR

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

10.5194/isprs-archives-xliii-b3-2020-67-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 67-74 ◽

Cited By ~ 1

Author(s):

B. K. Handique ◽

C. Goswami ◽

C. Gupta ◽

S. Pandit ◽

S. Gogoi ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Hierarchical Classification ◽

Cropping System ◽

Cropping Pattern ◽

Mixed Cropping ◽

Horticultural Crops ◽

The North ◽

Wide Range

Abstract. Assessment of horticultural crops under mixed cropping system has been a challenge, both for horticulturists and also to the remote sensing communities. But the recent developments in wide range of sensors onboard Unmanned Aerial Vehicles (UAVs) has opened up new possibilities in identification, mapping and monitoring of horticultural crops. This paper presents the results made from a pilot exercise on horticultural crop discrimination using Parrot Sequoia multi-spectral sensor onboard a UAV. This exercise was carried out in Nongkhrah village, Ri-Bhoi district of Meghalaya state located in the north eastern part of India having mixed horticultural crops. A two level hierarchical classification system was followed for identification and delineation of the major horticultural crops in the village. Parrot Sequoia multi-spectral sensor having four bands has been found to be effective in discrimination of horticultural crops based on variation in spectral response of six horticultural crops viz., pineapple, banana, orange, papaya, ginger and turmeric using three commonly used indices viz., Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI). NDVI and GNDVI showed nearly similar spectral response, whereas separability among the horticultural crops significantly improved with the use of NDRE. The first level of classification involving the five broad land cover classes has resulted an overall accuracy of about 91%, whereas the second level of classification for delineating the five selected horticultural crops has provided an overall accuracy of 79.8%.

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

Remote Sensing ◽

10.3390/rs12122015 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2015 ◽

Cited By ~ 2

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.

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Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture

Agriculture ◽

10.3390/agriculture8070116 ◽

2018 ◽

Vol 8 (7) ◽

pp. 116 ◽

Cited By ~ 30

Author(s):

Alessandro Matese ◽

Salvatore Di Gennaro

Keyword(s):

Water Stress ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Stress Index ◽

Crop Water Stress Index ◽

Practical Applications ◽

Water Stress Index ◽

Sensing Applications ◽

Wide Range ◽

Precision Viticulture

High spatial ground resolution and highly flexible and timely control due to reduced planning time are the strengths of unmanned aerial vehicle (UAV) platforms for remote sensing applications. These characteristics make them ideal especially in the medium–small agricultural systems typical of many Italian viticulture areas of excellence. UAV can be equipped with a wide range of sensors useful for several applications. Numerous assessments have been made using several imaging sensors with different flight times. This paper describes the implementation of a multisensor UAV system capable of flying with three sensors simultaneously to perform different monitoring options. The intra-vineyard variability was assessed in terms of characterization of the state of vines vigor using a multispectral camera, leaf temperature with a thermal camera and an innovative approach of missing plants analysis with a high spatial resolution RGB camera. The normalized difference vegetation index (NDVI) values detected in different vigor blocks were compared with shoot weights, obtaining a good regression (R2 = 0.69). The crop water stress index (CWSI) map, produced after canopy pure pixel filtering, highlighted the homogeneous water stress areas. The performance index developed from RGB images shows that the method identified 80% of total missing plants. The applicability of a UAV platform to use RGB, multispectral and thermal sensors was tested for specific purposes in precision viticulture and was demonstrated to be a valuable tool for fast multipurpose monitoring in a vineyard.

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Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe

Remote Sensing ◽

10.3390/rs11151823 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1823 ◽

Cited By ~ 9

Author(s):

Xiaojuan Huang ◽

Jingfeng Xiao ◽

Mingguo Ma

Keyword(s):

Primary Productivity ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Gross Primary Productivity ◽

Surface Reflectance ◽

Monthly Scale ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Annual Scale

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

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An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Earth Science Informatics ◽

10.1007/s12145-015-0217-3 ◽

2015 ◽

Vol 8 (2) ◽

pp. 327-335 ◽

Cited By ~ 37

Author(s):

Daniel Hölbling ◽

Barbara Friedl ◽

Clemens Eisank

Keyword(s):

Change Detection ◽

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Detection Methods ◽

Detection And Attribution ◽

Object Based ◽

Wide Range ◽

Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

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Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution and Vegetation Cover on Boothia Peninsula, Nunavut

ARCTIC ◽

10.14430/arctic2 ◽

2009 ◽

Vol 61 (1) ◽

pp. 1 ◽

Cited By ~ 50

Author(s):

Gita J. Laidler ◽

Paul M. Treitz ◽

David M. Atkinson

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Moisture Regime ◽

Percent Cover ◽

Multispectral Data ◽

Arctic Vegetation

Arctic tundra environments are thought to be particularly sensitive to changes in climate, whereby alterations in ecosystem functioning are likely to be expressed through shifts in vegetation phenology, species composition, and net ecosystem productivity (NEP). Remote sensing has shown potential as a tool to quantify and monitor biophysical variables over space and through time. This study explores the relationship between the normalized difference vegetation index (NDVI) and percent-vegetation cover in a tundra environment, where variations in soil moisture, exposed soil, and gravel till have significant influence on spectral response, and hence, on the characterization of vegetation communities. IKONOS multispectral data (4 m spatial resolution) and Landsat 7 ETM+ data (30 m spatial resolution) were collected for a study area in the Lord Lindsay River watershed on Boothia Peninsula, Nunavut. In conjunction with image acquisition, percent cover data were collected for twelve 100 m × 100 m study plots to determine vegetation community composition. Strong correlations were found for NDVI values calculated with surface and satellite sensors, across the sample plots. In addition, results suggest that percent cover is highly correlated with the NDVI, thereby indicating strong potential for modeling percent cover variations over the region. These percent cover variations are closely related to moisture regime, particularly in areas of high moisture (e.g., water-tracks). These results are important given that improved mapping of Arctic vegetation and associated biophysical variables is needed to monitor environmental change.

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Monitoring the variations of evapotranspiration due to land use/cover change in a semiarid shrubland

Hydrology and Earth System Sciences ◽

10.5194/hess-21-863-2017 ◽

2017 ◽

Vol 21 (2) ◽

pp. 863-877 ◽

Cited By ~ 15

Author(s):

Tingting Gong ◽

Huimin Lei ◽

Dawen Yang ◽

Yang Jiao ◽

Hanbo Yang

Keyword(s):

Land Use ◽

Vegetation Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Hydrological Cycle ◽

Vegetation Type ◽

Soil Surface ◽

Surface Characteristics ◽

Observation Data ◽

Semiarid Shrubland

Abstract. Evapotranspiration (ET) is an important process in the hydrological cycle, and vegetation change is a primary factor that affects ET. In this study, we analyzed the annual and inter-annual characteristics of ET using continuous observation data from eddy covariance (EC) measurement over 4 years (1 July 2011 to 30 June 2015) in a semiarid shrubland of Mu Us Sandy Land, China. The Normalized Difference Vegetation Index (NDVI) was demonstrated as the predominant factor that influences the seasonal variations in ET. Additionally, during the land degradation and vegetation rehabilitation processes, ET and normalized ET both increased due to the integrated effects of the changes in vegetation type, topography, and soil surface characteristics. This study could improve our understanding of the effects of land use/cover change on ET in the fragile ecosystem of semiarid regions and provide a scientific reference for the sustainable management of regional land and water resources.

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The Effects of Serendipita indica and Guanidine-Modified Nanomaterial on Growth and Development of Cabbage Seedlings and Black Spot Infestation

Agriculture ◽

10.3390/agriculture11121295 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1295

Author(s):

Sana Saleem ◽

Zuzana Bytešníková ◽

Lukáš Richtera ◽

Robert Pokluda

Keyword(s):

Plant Growth ◽

Chlorophyll A ◽

Seed Treatment ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Positive Impact ◽

Disease Incidence ◽

Black Spot ◽

Carotenoid Content ◽

Combined Application

To ensure sustainable agricultural production and protection of crops from various biotic and abiotic stresses, while keeping in view environmental protection, by minimal usage of chemicals, the exploitation of beneficial microorganisms and modern nanotechnologies in the field of agriculture is of paramount importance. This study aimed to investigate the effects of Serendipita indica and guanidine-modified nanomaterial on the growth, and other selected parameters, of cabbage, as well as incidence of black spot disease. S. indica was applied in substrate and by seed inoculation. S. indica had a positive impact on the development of plants, and resulted in reduced black spot severity. The maximum plant height (119 mm) and number of leaves (8.3) were observed in S. indica-treated plants. Pigments were enhanced, i.e., chlorophyll a (0.79 mg/g), chlorophyll b (0.22 mg/g), and carotenoid content (0.79 mg/g), by substrate treatment. The highest antioxidant capacity (9.5 mM/L), chlorophyll a and b (1.8 and 0.6 mg/g), and carotenoid content (1.8 mg/L) were reported in S. indica seed treatment. S. indica treatment resulted in 59% and 41% disease incidence decrease in substrate and seed treatment, respectively. Guanidine-modified nanomaterial was seen to be effective in improving plant growth and reducing disease incidence; however, it did not perform better than S. indica. Application of nanoparticles resulted in enhanced normalized difference vegetation index and fluorescence by increasing chlorophyll a, b, and carotenoid content. Nitrogen content was the highest in plants treated with nanoparticles. However, the effect of the combined application of fungus and nanoparticles was similar to that of S. indica alone in substrate treatment, although negative impacts were reported in the biochemical parameters of cabbage. S. indica has great potential to enhance plant growth and manage Alternaria incidence in cabbage crops.

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Post-fire vegetation recovery in Portugal based on spot/vegetation data

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-673-2010 ◽

2010 ◽

Vol 10 (4) ◽

pp. 673-684 ◽

Cited By ~ 51

Author(s):

C. Gouveia ◽

C. C. DaCamara ◽

R. M. Trigo

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Soil Surface ◽

Recovery Process ◽

Vegetation Recovery ◽

Fire Season ◽

Burned Area ◽

Vegetation Density ◽

Fire Event ◽

Central Portugal

Abstract. A procedure is presented that allows identifying large burned scars and the monitoring of vegetation recovery in the years following major fire episodes. The procedure relies on 10-day fields of Maximum Value Composites of Normalized Difference Vegetation Index (MVC-NDVI), with a 1 km×1 km spatial resolution obtained from the VEGETATION instrument. The identification of fire scars during the extremely severe 2003 fire season is performed based on cluster analysis of NDVI anomalies that persist during the vegetative cycle of the year following the fire event. Two regions containing very large burned scars were selected, located in Central and Southwestern Portugal, respectively, and time series of MVC-NDVI analysed before the fire events took place and throughout the post-fire period. It is shown that post-fire vegetation dynamics in the two selected regions may be characterised based on maps of recovery rates as estimated by fitting a monoparametric model of vegetation recovery to MVC-NDVI data over each burned scar. Results indicated that the recovery process in the region located in Central Portugal is mostly related to fire damage rather than to vegetation density before 2003, whereas the latter seems to have a more prominent role than vegetation conditions after the fire episode, e.g. in the case of the region in Southwestern Portugal. These differences are consistent with the respective predominant types of vegetation. The burned area located in Central Portugal is dominated by Pinus Pinaster whose natural regeneration crucially depends on the destruction of seeds present on the soil surface during the fire, whereas the burned scar in Southwestern Portugal was populated by Eucalyptus that may quickly re-sprout from buds after fire. Besides its simplicity, the monoparametric model of vegetation recovery has the advantage of being easily adapted to other low-resolution satellite data, as well as to other types of vegetation indices.

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