Chlorophyll Changes Using Neural Network and Vegetation Indices in Tropical deciduous forest

Tropical Deciduous Forest ◽

Transfer Model ◽

Landsat 8 ◽

Spatio Temporal

Abstract Background: Ongoing climate and Earth’s atmosphere changes create profound effect on distribution and composition of forest, as well as on the fauna that depends on forest. The Sentinel-2A satellite data eases the mapping of Leaf Chlorophyll Content (LCC) at higher spatial and temporal resolution. In the present study, the temporal dimension of LCC was evaluated as an indicator of plant stress. LCC was retrieved using the inversion of the radiative transfer model based on an artificial neural network. The data used for Spatio-temporal modelling of LCC was Landsat data.Result: From the Sentinel imagery derived vegetation indices, it was found that the narrowband indices having high correlation with LCC were pigment specific simple ratio and normalized difference index (45) (R2 > 0.7; p < 0.001) centred at 665 nm, 705 nm, and 740 nm. Landsat 8 infrared percentage vegetation index had a strong relationship with LCC (R2 =0.8). The Spatio-temporal (1997 to 2017) plant stress were detected using changes in LCC through an equation of correlation. The negative changes and deterioration of LCC were seen in the forest during the year 1997 to 20I7(rate = -1.2 µgcm-2year-1) showing higher rate of forest health decline. Conclusion: The 33% of plant stress increased currently in the protected forest mainly because of anthropogenic influences. These vast decline in the chlorophyll gives rise to various photosynthetic vulnerabilities in forest ecosystem and indirectly affects human including wildlife.

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

CANOPY MODELING OF AQUATIC VEGETATION: CONSTRUCTION OF SUBMERGED VEGETATION INDEX

10.5194/isprs-archives-xlii-3-1253-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1253-1258

Author(s):

Z. Ma ◽

G. Zhou

Keyword(s):

Vegetation Index ◽

Aquatic Vegetation ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Simulated Data ◽

Future Research ◽

Transfer Model ◽

Submerged Vegetation ◽

Terrestrial Vegetation

The unique spectral characteristics of submerged vegetation in wetlands determine that the conventional terrestrial vegetation index cannot be directly employed to species identification and parameter inversion of submerged vegetation. Based on the Aquatic Vegetation Radiative Transfer model (AVRT), this paper attempts to construct an index suitable for submerged vegetation, the model simulated data and a scene of Sentinel-2A image in Taihu Lake, China are utilized for assessing the performance of the newly constructed indices and the existent vegetation indices. The results show that the angle index composed by 525&thinsp;nm, 555&thinsp;nm and 670&thinsp;nm can resist the effects of water columns and is more sensitive to vegetation parameters such as LAI. Furthermore, it makes a well discrimination between submerged vegetation and water bodies in the satellite data. We hope that the new index will provide a theoretical basis for future research.

Analytical Relationship between Two-Band Spectral Vegetation Indices Measured at Multiple Sensors on a Parametric Representation of Soil Isoline Equations

Remote Sensing ◽

10.3390/rs11131620 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1620 ◽

Author(s):

Kenta Taniguchi ◽

Kenta Obata ◽

Hiroki Yoshioka

Keyword(s):

Vegetation Index ◽

Vegetation Indices ◽

Parametric Representation ◽

Analytical Relationship ◽

Transfer Model ◽

Multiple Sensors ◽

Spectral Vegetation Indices ◽

Relative Errors ◽

The Relationship

Differences between the wavelength band specifications of distinct sensors introduce systematic differences into the values of a spectral vegetation index (VI). Such relative errors must be minimized algorithmically after data acquisition, based on a relationship between the measurements. This study introduces a technique for deriving the analytical relationship between the VIs from two sensors. The derivation proceeds using a parametric form of the soil isoline equations, which relate the reflectances of two different wavelengths. First, the derivation steps are explained conceptually. Next, the conceptual steps are cast in a practical derivation by assuming a general form of the two-band VI. Finally, the derived expressions are demonstrated numerically using a coupled leaf and canopy radiative transfer model. The results confirm that the derived expression reduced the original differences between the VI values obtained from the two sensors, indicating the validity of the derived expressions. The derived expressions and numerical results suggested that the relationship between the VIs measured at different wavelengths varied with the soil reflectance spectrum beneath the vegetation canopy. These results indicate that caution is required when retrieving intersensor VI relationships over regions consisting of soil surfaces having distinctive spectra.

Joint Retrieval of Winter Wheat Leaf Area Index and Canopy Chlorophyll Density Using Hyperspectral Vegetation Indices

Remote Sensing ◽

10.3390/rs13163175 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3175

Author(s):

Naichen Xing ◽

Wenjiang Huang ◽

Huichun Ye ◽

Yu Ren ◽

Qiaoyun Xie

Keyword(s):

Winter Wheat ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Vegetation Indices ◽

Transfer Model ◽

Growth Stages ◽

Growth Monitoring ◽

Area Index

Leaf area index (LAI) and canopy chlorophyll density (CCD) are key biophysical and biochemical parameters utilized in winter wheat growth monitoring. In this study, we would like to exploit the advantages of three canonical types of spectral vegetation indices: indices sensitive to LAI, indices sensitive to chlorophyll content, and indices suitable for both parameters. In addition, two methods for joint retrieval were proposed. The first method is to develop integration-based indices incorporating LAI-sensitive and CCD-sensitive indices. The second method is to create a transformed triangular vegetation index (TTVI2) based on the spectral and physiological characteristics of the parameters. PROSAIL, as a typical radiative transfer model embedded with physical laws, was used to build estimation models between the indices and the relevant parameters. Validation was conducted against a field-measured hyperspectral dataset for four distinct growth stages and pooled data. The results indicate that: (1) the performance of the integrated indices from the first method are various because of the component indices; (2) TTVI2 is an excellent predictor for joint retrieval, with the highest R2 values of 0.76 and 0.59, the RMSE of 0.93 m2/m2 and 104.66 μg/cm2, and the RRMSE (Relative RMSE) of 12.76% and 16.96% for LAI and CCD, respectively.

A comparison of satellite-based indices for hazard assessment of MSW open dumps using spatial analysis

Waste Management & Research ◽

10.1177/0734242x18815963 ◽

2018 ◽

Vol 37 (3) ◽

pp. 219-236 ◽

Author(s):

Khalid Mahmood ◽

Zia Ul-Haq ◽

Fiza Faizi ◽

Syeda A. Batol

Keyword(s):

Hazard Assessment ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Moving Average ◽

Vegetation Indices ◽

Temporal Analysis ◽

Zone Size ◽

Vegetation Health ◽

Spatio Temporal ◽

Hazardous Zone

This study compares the suitability of different satellite-based vegetation indices (VIs) for environmental hazard assessment of municipal solid waste (MSW) open dumps. The compared VIs, as bio-indicators of vegetation health, are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) that have been subject to spatio-temporal analysis. The comparison has been made based on three criteria: one is the exponential moving average (EMA) bias, second is the ease in visually finding the distance of VI curve flattening, and third is the radius of biohazardous zone in relation to the waste heap dumped at them. NDVI has been found to work well when MSW dumps are surrounded by continuous and dense vegetation, otherwise, MSAVI is a better option due to its ability for adjusting soil signals. The hierarchy of the goodness for least EMA bias is MSAVI> SAVI> NDVI with average bias values of 101 m, 203 m, and 270 m, respectively. Estimations using NDVI have been found unable to satisfy the direct relationship between waste heap and hazardous zone size and have given a false exaggeration of 374 m for relatively smaller dump as compared to the bigger one. The same false exaggeration for SAVI and MSAVI is measured to be 86 m and -14 m, respectively. So MSAVI is the only VI that has shown the true relation of waste heap and hazardous zone size. The best visualization of distance-dependent vegetation health away from the dumps is also provided by MSAVI.

Error simulations of uncorrected NDVI and DCVI during remote sensing measurements from UAS

Miscellanea Geographica ◽

10.2478/mgrsd-2014-0017 ◽

2014 ◽

Vol 18 (2) ◽

pp. 35-45 ◽

Cited By ~ 2

Author(s):

Michał T. Chiliński ◽

Marek Ostrowski

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Vegetation Index ◽

Vegetation Mapping ◽

Unmanned Aerial Systems ◽

Transfer Model ◽

Digital Cameras ◽

Aerial Systems

Abstract Remote sensing from unmanned aerial systems (UAS) has been gaining popularity in the last few years. In the field of vegetation mapping, digital cameras converted to calculate vegetation index (DCVI) are one of the most popular sensors. This paper presents simulations using a radiative transfer model (libRadtran) of DCVI and NDVI results in an environment of possible UAS flight scenarios. The analysis of the results is focused on the comparison of atmosphere influence on both indices. The results revealed uncertainties in uncorrected DCVI measurements up to 25% at the altitude of 5 km, 5% at 1 km and around 1% at 0.15 km, which suggests that DCVI can be widely used on small UAS operating below 0.2 km.

Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing

Semina Ciências Agrárias ◽

10.5433/1679-0359.2021v42n4p2181 ◽

2021 ◽

Vol 42 (4) ◽

pp. 2181-2202

Author(s):

Taiara Souza Costa ◽

◽

Robson Argolo dos Santos ◽

Rosângela Leal Santos ◽

Roberto Filgueiras ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Crop Coefficient ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Crop Evapotranspiration ◽

Biophysical Parameters ◽

Planting Dates ◽

Maize Crop ◽

Spatio Temporal

This study proposes to estimate the actual crop evapotranspiration, using the SAFER model, as well as calculate the crop coefficient (Kc) as a function of the normalized difference vegetation index (NDVI) and determine the biomass of an irrigated maize crop using images from the Operational Land Imager (OLI) and Thermal Infrared (TIRS) sensors of the Landsat-8 satellite. Pivots 21 to 26 of a commercial farm located in the municipalities of Bom Jesus da Lapa and Serra do Ramalho, west of Bahia State, Brazil, were selected. Sowing dates for each pivot were arranged as North and South or East and West, with cultivation starting firstly in one of the orientations and subsequently in the other. The relationship between NDVI and the Kc values obtained in the FAO-56 report (KcFAO) revealed a high coefficient of determination (R2 = 0.7921), showing that the variance of KcFAO can be explained by NDVI in the maize crop. Considering the center pivots with different planting dates, the crop evapotranspiration (ETc) pixel values ranged from 0.0 to 6.0 mm d-1 during the phenological cycle. The highest values were found at 199 days of the year (DOY), corresponding to around 100 days after sowing (DAS). The lowest BIO values occur at 135 DOY, at around 20 DAS. There is a relationship between ETc and BIO, where the DOY with the highest BIO are equivalent to the days with the highest ETc values. In addition to this relationship, BIO is strongly influenced by soil water availability.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

Ecology and Industry of Russia ◽

10.18412/1816-0395-2021-9-30-37 ◽

2021 ◽

Vol 25 (9) ◽

pp. 30-37

Author(s):

N.N. Sliusar ◽

A.P. Belousova ◽

G.M. Batrakova ◽

R.D. Garifzyanov ◽

M. Huber-Humer ◽

...

Keyword(s):

Remote Sensing ◽

Waste Disposal ◽

Vegetation Cover ◽

Vegetation Index ◽

Vegetation Indices ◽

Cost Effective ◽

Google Earth ◽

The State ◽

Landsat 8 ◽

The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Sensors ◽

10.3390/s20092460 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2460 ◽

Author(s):

Yangyang Zhang ◽

Jian Yang ◽

Xiuguo Liu ◽

Lin Du ◽

Shuo Shi ◽

...

Keyword(s):

Gaussian Process ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Gaussian Process Regression ◽

Transfer Model ◽

Area Index ◽

Look Up Table ◽

Band Combinations

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment

Remote Sensing ◽

10.3390/rs12172752 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2752

Author(s):

Christopher O. Ilori ◽

Anders Knudby

Keyword(s):

Remote Sensing ◽

Environmental Factors ◽

Complete Solution ◽

Atmospheric Correction ◽

Florida Keys ◽

Inversion Method ◽

Transfer Model ◽

Landsat 8 ◽

Depth Range

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Remote Sensing ◽

10.3390/rs12142254 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2254 ◽

Author(s):

Hafiz Ali Imran ◽

Damiano Gianelle ◽

Duccio Rocchini ◽

Michele Dalponte ◽

M. Pilar Martín ◽

...

Keyword(s):

Near Infrared ◽

Spatial Scales ◽

Vegetation Indices ◽

Transfer Model ◽

Sharp Change ◽

Full Potential ◽

Area Index ◽

Red Edge ◽

Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.