scholarly journals TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

2016 ◽  
Vol 22 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Eder Paulo Moreira* ◽  
Márcio de Morisson Valeriano ◽  
Ieda Del Arco Sanches ◽  
Antonio Roberto Formaggio
Keyword(s):  
Near Infrared ◽  
Incidence Angle ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Topographic Effect ◽  
Topographic Correction ◽  
Spectral Bands ◽  
Spectral Vegetation Indices ◽  
Current Availability ◽  
Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

scholarly journals Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 25 ◽  
Author(s):  
René Heim ◽  
Ian Wright ◽  
Peter Scarth ◽  
Angus Carnegie ◽  
Dominique Taylor ◽  
...  
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Spectral Band ◽  
Management Strategies ◽  
Vegetation Indices ◽  
Unmanned Aerial Systems ◽  
Disease Detection ◽  
Myrtle Rust ◽  
Spectral Vegetation Indices ◽  
Austropuccinia Psidii

Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models.

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

scholarly journals Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

2020 ◽  
Vol 12 (8) ◽  
pp. 1238 ◽  
Author(s):  
Andrew Fletcher ◽  
Richard Mather
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Spectral Band ◽  
Point Clouds ◽  
Tree Canopy ◽  
Solar Elevation ◽  
Spectral Bands ◽  
Eucalyptus Forest ◽  
Dense Point ◽  
Solar Noon

Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

scholarly journals New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

2011 ◽  
Vol 33 (7) ◽  
pp. 2178-2195 ◽  
Author(s):  
Loris Vescovo ◽  
Georg Wohlfahrt ◽  
Manuela Balzarolo ◽  
Sebastian Pilloni ◽  
Matteo Sottocornola ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Remote Detection ◽  
Spectral Vegetation Indices

scholarly journals Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

1996 ◽  
Vol 5 (3) ◽  
pp. 367-376 ◽  
Author(s):  
Tsuyoshi Akiyama ◽  
Y. Inoue ◽  
M. Shibayama ◽  
Y. Awaya ◽  
N. Tanaka
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Nutrient Deficiency ◽  
The Arctic ◽  
Ground Vegetation ◽  
Spectral Vegetation Indices ◽  
Project Monitoring ◽  
Crop Types ◽  
Crop Discrimination

LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to contribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indices. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR) and red (R) reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively.

scholarly journals Optimal Spatial Resolution for the Detection and Discrimination of Archaeological Proxies in Areas with Spectral Heterogeneity

2020 ◽  
Vol 12 (1) ◽  
pp. 136 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Archaeological Site ◽  
Area Of Interest ◽  
First Case

Subsurface targets can be detected from space-borne sensors via archaeological proxies, known in the literature as cropmarks. A topic that has been limited in its investigation in the past is the identification of the optimal spatial resolution of satellite sensors, which can better support image extraction of archaeological proxies, especially in areas with spectral heterogeneity. In this study, we investigated the optimal spatial resolution (OSR) for two different cases studies. OSR refers to the pixel size in which the local variance, of a given area of interest (e.g., archaeological proxy), is minimized, without losing key details necessary for adequate interpretation of the cropmarks. The first case study comprises of a simulated spectral dataset that aims to model a shallow buried archaeological target cultivated on top with barley crops, while the second case study considers an existing site in Cyprus, namely the archaeological site of “Nea Paphos”. The overall methodology adopted in the study is composed of five steps: firstly, we defined the area of interest (Step 1), then we selected the local mean-variance value as the optimization criterion of the OSR (Step 2), while in the next step (Step 3), we spatially aggregated (upscale) the initial spectral datasets for both case studies. In our investigation, the spectral range was limited to the visible and near-infrared part of the spectrum. Based on these findings, we determined the OSR (Step 4), and finally, we verified the results (Step 5). The OSR was estimated for each spectral band, namely the blue, green, red, and near-infrared bands, while the study was expanded to also include vegetation indices, such as the Simple Ratio (SR), the Atmospheric Resistance Vegetation Index (ARVI), and the Normalized Difference Vegetation Index (NDVI). The outcomes indicated that the OSR could minimize the local spectral variance, thus minimizing the spectral noise, and, consequently, better support image processing for the extraction of archaeological proxies in areas with high spectral heterogeneity.

Variation in understory and canopy reflectance during stand development in Finnish coniferous forests

2015 ◽  
Vol 45 (8) ◽  
pp. 1077-1085 ◽  
Author(s):  
Nea Kuusinen ◽  
Pauline Stenberg ◽  
Erkki Tomppo ◽  
Pierre Bernier ◽  
Frank Berninger
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Boreal Forests ◽  
Near Infrared ◽  
Spectral Band ◽  
Stand Development ◽  
Area Index ◽  
Spectral Bands ◽  
Site Fertility ◽  
Infrared Spectral

Inherent variability in the spectral properties of boreal forests complicates the retrieval of canopy properties such as canopy leaf area index from satellite images. Understanding the drivers of this variability could help provide better estimates of desired canopy cover properties. Field plot data from the Finnish National Forest Inventory and Landsat thematic mapper (TM) images were used to investigate the variation in canopy and understory reflectance during stand development in coniferous boreal forests. Spectral data for each plot were obtained from the Landsat pixel within which the plot center coordinates fell. Nonlinear unmixing was used to estimate the bidirectional reflectance factors (BRFs) of the “sunlit understory” and “canopy and shaded ground” components by site fertility and stand development classes. A forest albedo model was used to estimate the contribution of diffuse radiation reflected downwards from the canopy to the sunlit understory component. The sunlit understory BRF in the near-infrared spectral band decreased as the site fertility decreased and the forest matured, whereas the sunlit understory BRFs in the red and shortwave-infrared spectral bands concurrently increased. The BRFs of the canopy and shaded ground component decreased slightly during stand development, mostly in the near-infrared spectral band. Adding the diffuse contribution to the sunlit understory component changed the estimated component BRFs only a little (0.1%–1.7%) compared with those obtained using a linear mixing assumption. This effect was largest in the near-infrared spectral band and smallest in the red spectral band. For Norway spruce plots, the measured and estimated forest variables were well correlated with the BRFs in all of the studied spectral bands, but for the Scots pine plots, the correlations were notably weaker. Results show a greater importance of the fraction of visible sunlit understory on forest reflectance in Scots pine than in Norway spruce forests.

scholarly journals Does topographic normalization of landsat images improve fractional tree cover mapping in tropical mountains?

2015 ◽  
Vol XL-7/W3 ◽  
pp. 261-267 ◽  
Author(s):  
H. Adhikari ◽  
J. Heiskanen ◽  
E. E Maeda ◽  
P. K. E. Pellikka
Keyword(s):  
Predictive Models ◽  
Vegetation Index ◽  
Incidence Angle ◽  
Vegetation Indices ◽  
Airborne Lidar ◽  
Tree Cover ◽  
Topographic Effects ◽  
Landsat Images ◽  
Topographic Correction ◽  
Tropical Mountains

Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methods have been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive to topographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The study area was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correction method. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) and tasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, we evaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and a regional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimum and maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightness and had very small effect on the other models. Cosine of the solar incidence angle (<i>cos i</i>) derived from SRTM DEM showed stronger relationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive models based on common vegetation indices are not sensitive to topographic effects.

scholarly journals Modeling radiative transfer in tropical rainforest canopies: sensitivity of simulated albedo to canopy architectural and optical parameters

2011 ◽  
Vol 83 (4) ◽  
pp. 1231-1242 ◽  
Author(s):  
Sílvia N. M. Yanagi ◽  
Marcos H. Costa
Keyword(s):  
Radiative Transfer ◽  
Near Infrared ◽  
Tropical Rainforest ◽  
Spectral Band ◽  
Canopy Structure ◽  
Optical Parameters ◽  
Strong Response ◽  
Spectral Bands ◽  
Infrared Spectral ◽  
Integrated Biosphere Simulator

This study evaluates the sensitivity of the surface albedo simulated by the Integrated Biosphere Simulator (IBIS) to a set of Amazonian tropical rainforest canopy architectural and optical parameters. The parameters tested in this study are the orientation and reflectance of the leaves of upper and lower canopies in the visible (VIS) and near-infrared (NIR) spectral bands. The results are evaluated against albedo measurements taken above the K34 site at the INPA (Instituto Nacional de Pesquisas da Amazônia) Cuieiras Biological Reserve. The sensitivity analysis indicates a strong response to the upper canopy leaves orientation (x up) and to the reflectivity in the near-infrared spectral band (rNIR,up), a smaller sensitivity to the reflectivity in the visible spectral band (rVIS,up) and no sensitivity at all to the lower canopy parameters, which is consistent with the canopy structure. The combination of parameters that minimized the Root Mean Square Error and mean relative error are Xup = 0.86, rVIS,up = 0.062 and rNIR,up = 0.275. The parameterizations performed resulted in successful simulations of tropical rainforest albedo by IBIS, indicating its potential to simulate the canopy radiative transfer for narrow spectral bands and permitting close comparison with remote sensing products.

scholarly journals Índice de vegetação ajustado ao solo (IVAS): estado da arte e suas potencialidades (Adjusted Vegetation Index to Solo (SAVI): state of the art and its potential)

2016 ◽  
Vol 9 (6) ◽  
pp. 2054
Author(s):  
Gabrielle de Araújo Ribeiro ◽  
João Nailson De Castro Silva ◽  
Janaína Barbosa Da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
State Of The Art ◽  
Vegetation Indices ◽  
Quantitative Information ◽  
Qualitative And Quantitative ◽  
Spectral Bands ◽  
Satellite Sensors ◽  
Best Parameters

A utilização do Sensoriamento Remoto para a avaliação do meio ambiente é cada vez mais aplicado em pesquisas. As imagens adquiridas pelos sensores acoplados aos satélites fornecem dados qualitativos e quantitativos do estado da vegetação através da aplicação dos índices de vegetação. Os índices são obtidos pela combinação matemáticas das reflectâncias dos alvos nas faixas espectrais, principalmente do vermelho e infravermelho próximo e podem ser afetados por diferentes fatores tais como reflectância, irradiancia e o brilho do solo. Um dos índices comumente utilizados, principalmente em áreas semiáridas, onde se tem influencia do brilho do solo, é o índice de vegetação ajustado ao solo (IVAS). Este índice introduz um fator de ajuste (L) ao índice de vegetação normalizada (IVDN) para minimizar os efeitos da presença do solo. Porém para cada região deve-se estudar e determinar os melhores parâmetros para o mesmo. Portanto este trabalho tem como objetivo apresentar uma revisão de literatura em relação ao índice de vegetação ajustado ao solo em diferentes biomas brasileiro e outras aplicações.   A B S T R A C T The use of remote sensing for environmental assessment is increasingly applied in research. The images acquired by the satellite sensors coupled to provide qualitative and quantitative information on the state of the vegetation by the application of vegetation indices. The indices are obtained by mathematical combination of the reflectance of the targets in the spectral bands, especially the red and near infrared and can be affected by different factors such as reflectance, irradiance and the brightness of the soil. One of the commonly used indices, especially in semi-arid areas where it has influence of soil brightness, is the vegetation index adjusted to the ground (UAI). This index introduces an adjustment factor (L) normalized vegetation index (NDVI) to minimize the effects of soil present. However, for each region should study and determine the best parameters for the same. Therefore this work aims to present a literature review regarding the vegetation index adjusted to the soil in different Brazilian biomes and other applications. Keywords : Remote Sensing; vegetation index; spectral analysis, biome.   

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