Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring

Optical remote sensing indices play an important role in vegetation information extraction and have been widely serving ecology, agriculture and forestry, urban monitoring, and other communities. Remote sensing indices are constructed from individual bands depending on special characteristics to enhance the typical spectral features for the identification or distinction of surface land covers. With the development of quantitative remote sensing, there is a rapidly increasing requirement for accurate data processing and modeling. It is well known that the geometry-induced variation observed on surface reflectance is not ignorable, but the situation of uncertainty thereby introduced into these indices still needs further detailed understanding. We adopted the ground multi-angle hyperspectrum, spectral response function (SRF) of Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), Operational Land Imager (OLI), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Multi-Spectral Instrument (MSI) optical sensors and simulated their sensor-like spectral reflectance; then, we investigated the potential angle effect uncertainty on optical indices that have been frequently involved in vegetation monitoring and examined the forward/backward effect over both the ground-based level and the actual Landsat TM/ETM+ overlapped region. Our results on the discussed indices and sensors show the following: (1) Identifiable angle effects exist with a more elevated influence than that introduced by band difference among sensors; (2) The absolute difference of forward and backward direction can reach up to −0.03 to 0.1 within bands of the TM/ETM+ overlapped region; (3) The investigation at ground level indicates that there are different variations of angle effect transmitted to each remote sensing index. Regarding cases of crop canopy at various growth phases, most of the discussed indices have more than a 20% relative difference in nadir value except Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), at less than 10%, and Normalized Burn Ratio (NBR) at less than 16%. For the case of wax maturity stage, the relative difference in nadir value of Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Ratio Vegetation Index (RVI), Char Soil Index (CSI), NBR, Normalized Difference Moisture Index (NDMI), and SWIR2/NIR exceeded 50%, among which the values for NBR and NDMI reached up to 115.8% and 206.7%, respectively; (4) Various schemes of index construction imply different developments of angle effect uncertainty. The “difference” indices can partially suppress the directional influence, while the “ratio” indices show high potential to amplify the angle effect. This study reveals that the angle-induced uncertainty of these indices is greater than that induced by the spectrum mismatch among sensors, especially under the senescence period. In addition, based on this work, indices with a suppressed potential of angle effect are recommended for vegetation monitoring or information retrieval to avoid unexpected effects.

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Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Land ◽

10.3390/land10111221 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1221

Author(s):

Yuki Hamada ◽

Colleen R. Zumpf ◽

Jules F. Cacho ◽

DoKyoung Lee ◽

Cheng-Hsien Lin ◽

...

Keyword(s):

Remote Sensing ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Environmental Sustainability ◽

Vegetation Index ◽

Mean Squared Error ◽

Spectral Band ◽

Great Promise ◽

Optical Remote Sensing

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

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ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

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

10.5194/isprsarchives-xli-b7-223-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 223-227 ◽

Cited By ~ 2

Author(s):

Yi-Ta Hsieh ◽

Shou-Tsung Wu ◽

Chaur-Tzuhn Chen ◽

Jan-Chang Chen

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Vegetation Index ◽

Spectral Characteristics ◽

Aerial Images ◽

Aerial Image ◽

Optical Remote Sensing ◽

Shadow Area ◽

Vegetation Indexes ◽

Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

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Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

Remote Sensing ◽

10.3390/rs12213524 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3524

Author(s):

Feng Gao ◽

Martha C. Anderson ◽

W. Dean Hively

Keyword(s):

Remote Sensing ◽

Time Series ◽

Cover Crops ◽

Cover Crop ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Research ◽

Agricultural Landscapes ◽

Absolute Difference ◽

Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

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

ITM Web of Conferences ◽

10.1051/itmconf/20182300030 ◽

2018 ◽

Vol 23 ◽

pp. 00030 ◽

Cited By ~ 2

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.

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Tree height inversion combining light detection and ranging and optical remote sensing data

E3S Web of Conferences ◽

10.1051/e3sconf/202016503020 ◽

2020 ◽

Vol 165 ◽

pp. 03020

Author(s):

Kunlin Wang ◽

Yi Ma ◽

Fangrong Zhou

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Vegetation Index ◽

Remote Sensing Data ◽

Tree Height ◽

Edge Length ◽

Optical Remote Sensing ◽

Sensing Data ◽

Inversion Model ◽

Feature Parameters

Tree barriers in transmission line corridors are an important safety hazard.Scientific prediction of tree height and monitoring tree height changes are essential to solve this hidden danger. In this paper, the advantages of airborne lidar and optical remote sensing data are combined to research the method of tree height inversion. Based on glas data of lidar,waveform parameters such as waveform length, waveform leading edge length and waveform trailing edge length were extracted from waveform data by gaussian decomposition method.Terrain feature parameters were extracted from aster gdem data.The tree crown information was extracted from the optical remote sensing image by means of the mean shift algorithm. Then extract the vegetation index with high correlation with tree height.Finally, the extracted waveform feature parameters, topographic feature parameters, and crown index and vegetation index with high correlation are used as model input variables. The tree height inversion model was established using four regression methods, including multiple linear regression (mlr), support vector machine (svm), random forest (rf), and bp neural network (bpnn). The accuracy evaluation was conducted, and it was concluded that the tree height inversion model based on random forest obtained the best accuracy effect.

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Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques

Geosciences ◽

10.3390/geosciences10050164 ◽

2020 ◽

Vol 10 (5) ◽

pp. 164

Author(s):

Valentine Piroton ◽

Romy Schlögel ◽

Christian Barbier ◽

Hans-Balder Havenith

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Mining Area ◽

Mass Movements ◽

Optical Remote Sensing ◽

Velocity Anomaly ◽

In Situ Data ◽

Remote Sensing Techniques ◽

The Impact

Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.

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Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

Remote Sensing ◽

10.3390/rs11121398 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1398 ◽

Cited By ~ 6

Author(s):

Xuanlong Ma ◽

Alfredo Huete ◽

Ngoc Nguyen Tran

Keyword(s):

Remote Sensing ◽

Time Series ◽

Vegetation Cover ◽

Vegetation Index ◽

Growing Season ◽

The Sun ◽

Space And Time ◽

Ndvi Time Series ◽

Angle Effect ◽

Phenological Metrics

Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.

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Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR

Sensors ◽

10.3390/s20030935 ◽

2020 ◽

Vol 20 (3) ◽

pp. 935

Author(s):

Xingmin Zhao ◽

Shuo Shi ◽

Jian Yang ◽

Wei Gong ◽

Jia Sun ◽

...

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Point Clouds ◽

Kappa Coefficient ◽

Optical Remote Sensing ◽

Vegetation Monitoring ◽

Agricultural Applications ◽

Multi Wavelength ◽

Normal Vectors ◽

Point Cloud Classification

Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system’s performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.

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Tropical Peatland Identification using L-Band Full Polarimetric Synthetic Aperture Radar (SAR) Imagery (Study Case: Siak Regency, Riau Province)

HAYATI Journal of Biosciences ◽

10.4308/hjb.26.2.63 ◽

2019 ◽

Vol 26 (2) ◽

pp. 63

Author(s):

Desti Ayunda ◽

Ketut Wikantika ◽

Dandy A. Novresiandi ◽

Agung B. Harto ◽

Riantini Virtriana ◽

...

Keyword(s):

Remote Sensing ◽

Synthetic Aperture Radar ◽

Vegetation Index ◽

Synthetic Aperture ◽

Optical Remote Sensing ◽

Error Matrix ◽

Tropical Peatland ◽

Average Value ◽

Sar Imagery ◽

Aperture Radar

From previous research reported that tropical peatland is one of terrestrial carbon storage in Earth, and has contribution to climate change. Synthetic Aperture Radar (SAR) is one of remote sensing technology which is more efcient than optical remote sensing. Its ability to penetrate cloud makes it useful to monitor tropical environment. This research is conducted in a tropical peatland in Siak Regency, Riau Province. This research was conducted to identify tropical peatland in Siak Regency using polarimetric decomposition, unsupervised classifcation ISODATA, and Radar Vegetation Index (RVI) from SAR data that had been geometrically and radiometrically corrected. Polarimetric decomposition Freeman-Durden was performed to analyze radar backscattering mechanism in tropical peatland, which shows that volume and surface scattering was dominant because of the presence of vegetation and open area. Unsupervised classifcation ISODATA was then performed to extract “shrub class”. By assessing its accuracy, the class that represents shrub class in reference map was selected as the selected “shrub class”. RVI then was calculated using a certain formula. Spatial analysis was then conducted to acquire certain information that average value of RVI in tropical peatland tend to be higher than in non-tropical peatland. By integrating selected “shrub class” and RVI, peat classes were extracted. The best peat class was selected by comparing with peatland referenced map which is acquired from the Indonesian Agency for Agricultural Resources and Development (IAARD) using error matrix. In this research, the best peat class yielded 73.5 percent of Producer’s Accuracy (PA), 81.6 percent of User’s Accuracy (UA), 66.1 percent of Overall Accuracy (OA), and 0.1079 of Kappa coefcient (Ks).

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Soil Aggregate Stability Mapping Using Remote Sensing and GIS-Based Machine Learning Technique

Frontiers in Earth Science ◽

10.3389/feart.2021.748859 ◽

2021 ◽

Vol 9 ◽

Author(s):

Yassine Bouslihim ◽

Aicha Rochdi ◽

Rachid Aboutayeb ◽

Namira El Amrani-Paaza ◽

Abdelhalim Miftah ◽

...

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Aggregate Stability ◽

Soil Aggregate ◽

Alternative Methods ◽

Soil Parameters ◽

Soil Aggregate Stability ◽

Remote Sensing Indices

Soil aggregate stability (SAS) is a critical parameter of soil quality and its mapping can help determine erosion hotspots. Despite this importance, SAS is less documented in available literature due to limited number of analyzes besides being a time consuming. For this reason, many researchers have turned to alternative methods that often use readily available variables such as soil parameters or remote sensing indices to estimate this variable. In that framework, the aim of the present study focused on the investigation of the feasibile use of adapted Leo Breiman’s random forest algorithm (RF) to mapping different mean weight diameter (MWD) tests as an index of SAS (mechanical breakdown (MWDmb), slow wetting (MWDsw), fast wetting (MWDfw) and the mean of the three tests (MWDmean)). The model was built with 77 samples distributed in the three watersheds of the study area located at Settat Ben-Ahmed, in Morocco and with the use of several environmental variables such as soil parameters (organic matter and clay), remote sensing indices (band 2, band 3, band 4, band 5, normalized difference vegetation index (NDVI) and transformed normalized difference vegetation index (TNDVI)), topography (elevation, slope, curvature plane and the topographic wetness index (TWI)) along with additional categorical variables as geological maps, land use and soil classes. The results showed a good level of accuracy for the training phase (75% of samples) for the different tests (R2 > 0.92, RMSE and MAE < 0.15) and were satisfactory for the testing phase (25% of samples, R2 > 0.65, RMSE and MAE < 0.31). Also, organic matter, topography and geology were the most important parameters in the spatial prediction of SAS. Finally, the maps build during this study could be of great use to identify areas of less stable soils in the perspective for taking the necessary measures to improve their quality.

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