EXTRACTION OF DEBILITATED TREES ALONG THE ROAD BY BLOCKED NDVI

Abstract. A method for extracting degraded trees was developed by using a near-infrared camera modified from a digital camera to photograph roadside trees. Traditionally, remote sensing has utilized vegetation index using near-infrared and red bands as a method to extract degraded trees. However, it was not possible to assess the health of roadside trees sufficiently because the observation from above only observed the canopy of the roadside trees. Observations from the ground can cover the shortcomings because they observe the sides as well as the canopy of the tree. However, ground-based observations are strongly influenced by sunlight, which needs to be compensated for. Also, since the target is trees on the side of the road, it is desirable to take a video of the trees from above the vehicle. The basic idea of this study is simple: a tree where the vegetation index is lower than other trees is considered a cautionary tree, and a tree where the vegetation index changes over time or month is lower than other trees is extracted as a degraded tree. In order to compare videos shot at different times, frame matching of videos and geometric correction between frames were performed. To account for geometric accuracy, pixels were grouped together as blocks, and changes in vegetation indices from block to block were analyzed. In order to improve the accuracy of the analysis, non-vegetation areas were removed from the images. As a result, blocks of debilitated trees were extracted from the trees along the road.

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Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

10.5194/egusphere-egu21-5171 ◽

2021 ◽

Author(s):

Georg Wohlfahrt ◽

Albin Hammerle ◽

Barbara Rainer ◽

Florian Haas

Keyword(s):

Chlorophyll Fluorescence ◽

Vegetation Index ◽

Near Infrared ◽

Census Data ◽

Meteorological Data ◽

Vegetation Indices ◽

Wavelength Region ◽

Remote Sensing Data ◽

Warning Signs ◽

Mitigation Measures

Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.

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Using remote sensing for identification of late-season grass weed patches in wheat

Weed Science ◽

10.1614/ws-05-54.2.346 ◽

2006 ◽

Vol 54 (02) ◽

pp. 346-353 ◽

Cited By ~ 41

Author(s):

Francisca López-Granados ◽

Montse Jurado-Expósito ◽

Jose M. Peña-Barragán ◽

Luis García-Torres

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Field Research ◽

Aerial Images ◽

Wild Oat ◽

Late Season ◽

High Resolution Satellite Imagery ◽

Grass Weed

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

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Evaluation and Normalization of Topographic Effects on Vegetation Indices

Remote Sensing ◽

10.3390/rs12142290 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2290

Author(s):

Rui Chen ◽

Gaofei Yin ◽

Guoxiang Liu ◽

Jing Li ◽

Aleixandre Verger

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Vegetation Indices ◽

Topographic Effects ◽

Terrestrial Vegetation ◽

Mountainous Areas ◽

Near Infrared Reflectance ◽

Illumination Condition ◽

Vegetation Monitoring ◽

Local Illumination

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas.

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Evaluation of a Low-cost Camera for Agricultural Applications

Journal of Experimental Agriculture International ◽

10.9734/jeai/2019/v32i530117 ◽

2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Abdon Francisco Aureliano Netto ◽

Rodrigo Nogueira Martins ◽

Guilherme Silverio Aquino De Souza ◽

Fernando Ferreira Lima Dos Santos ◽

Jorge Tadeu Fim Rosas

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Low Cost ◽

Vegetation Indices ◽

Paspalum Notatum ◽

Crop Canopy ◽

N Application ◽

Satisfactory Accuracy ◽

Agricultural Applications

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

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The use of RGB cameras in defining crop development in legumes

Advances in Animal Biosciences ◽

10.1017/s2040470017000498 ◽

2017 ◽

Vol 8 (2) ◽

pp. 224-228 ◽

Cited By ~ 4

Author(s):

I. Travlos ◽

A. Mikroulis ◽

E. Anastasiou ◽

S. Fountas ◽

D. Bilalis ◽

...

Keyword(s):

Human Population ◽

Vegetation Index ◽

Growth Parameters ◽

Vegetation Indices ◽

Digital Camera ◽

High Yield ◽

Legume Crop ◽

Crop Varieties ◽

Crop Development ◽

Crop Parameters

The human population is expected to reach 9 billion by 2050 and thus high yield crop varieties need to be developed. Remote sensing can estimate crop parameters non-destructively and quickly. The aim of this study was to compare and evaluate the use of a commercial RGB camera with an expensive canopy sensor in the crop development of two legumes. The RGB camera based vegetation index (NGRDI) was compared with the canopy sensor derived vegetation indices (NDVI and NDRE) for estimating legume crop growth parameters. The results indicated that the use of a simple digital camera RGB can in some cases replace spectral canopy sensors.

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Seasonal dynamics of lingonberry and blueberry spectra

Silva Fennica ◽

10.14214/sf.10150 ◽

2019 ◽

Vol 53 (2) ◽

Author(s):

Petri Forsström ◽

Jouni Peltoniemi ◽

Miina Rautiainen

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Reference Data ◽

Leaf Growth ◽

Vegetation Indices ◽

Growing Season ◽

Moisture Stress ◽

Stress Index ◽

Infrared Spectral

Accurate mapping of the spatial distribution of understory species from spectral images requires ground reference data which represent the prevailing phenological stage at the time of image acquisition. We measured the spectral bidirectional reflectance factors (BRFs, 350â2500 nm) at varying view angles for lingonberry ( L.) and blueberry ( L.) throughout the growing season of 2017 using Finnish Geospatial Research Instituteâs FIGIFIGO field goniometer. Additionally, we measured spectra of leaves and berries of both species, and flowers of lingonberry. Both lingonberry and blueberry showed seasonality in visible and near-infrared spectral regions which was linked to occurrences of leaf growth, flowering, berrying, and leaf senescence. The seasonality of spectra differed between species due to different phenologies (evergreen vs. deciduous). Vegetation indices, normalized difference vegetation index (NDVI), moisture stress index (MSI), plant senescence reflectance index (PSRI), and red-edge inflection point (REIP2), showed characteristic seasonal trends. NDVI and PSRI were sensitive to the presence of flowers and berries of lingonberry, while with blueberry the effects were less evident. Off-nadir observations supported differentiating the dwarf shrub species from each other but showed little improvement for detection of flowers and berries. Lingonberry and blueberry can be identified by their spectral signatures if ground reference data are available over the entire growing season. The spectral data measured in this study are reposited in the publicly open SPECCHIO Spectral Information System.Vaccinium vitis-idaeaVaccinium myrtillus

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Replacing the Red Band with the Red-SWIR Band (0.74ρred+0.26ρswir) Can Reduce the Sensitivity of Vegetation Indices to Soil Background

Remote Sensing ◽

10.3390/rs11070851 ◽

2019 ◽

Vol 11 (7) ◽

pp. 851 ◽

Cited By ~ 4

Author(s):

Xuehong Chen ◽

Zhengfei Guo ◽

Jin Chen ◽

Wei Yang ◽

Yanming Yao ◽

...

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Vegetation Indices ◽

Bare Soil ◽

Color Variation ◽

Vegetation Coverage ◽

Large Area ◽

Area Index ◽

Soil Line ◽

Swir Band

Most vegetation indices (VIs) of remote sensing were designed based on the concept of soil-line, which represents a linear correlation between bare soil reflectance at the red and near-infrared (NIR) bands. Unfortunately, the soil-line can only suppress brightness variation, not color differences of bare soil. Consequently, soil variation has a considerable impact on vegetation indices, although significant efforts have been devoted to this issue. In this study, a new soil-line is established in a new feature space of the NIR band and a virtual band that combines the red and shortwave-infrared (SWIR) bands (0.74ρred+0.26ρswir). Then, plus versions of vegetation indices (VI+), i.e., normalized difference vegetation index plus (NDVI+), enhanced vegetation index plus (EVI+), soil-adjusted vegetation index plus (SAVI+), and modified soil-adjusted vegetation index plus (MSAVI+), are proposed based on the new soil-line, which replaces the red band with the red-SWIR band in the vegetation indices. Soil spectral data from several spectral libraries confirm that bare soil has much less variation for VI+ than the original VI. Simulation experiments show that VI+ correlates better with fractional vegetation coverage (FVC) and leaf area index (LAI) than original VI. Ground measured LAI data collected from BigFoot, VALERI, and other previous references also confirm that VI+ derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data correlates better with ground measured LAI than original VI. These data analyses suggest that replacing the red band with the red-SWIR band can reduce the sensitivity of VIs to soil background. We recommend employing the proposed NDVI+, EVI+, SAVI+, and MSAVI+ in applications of large area, sparse vegetation, or when soil color variation cannot be neglected, although sensitivity to soil moisture and clay content might cause slight side effects for the proposed VI+s.

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

Remote Sensing ◽

10.3390/rs12010136 ◽

2020 ◽

Vol 12 (1) ◽

pp. 136 ◽

Cited By ~ 3

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.

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Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat

Remote Sensing ◽

10.3390/rs11222667 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2667 ◽

Cited By ~ 2

Author(s):

Jiang ◽

Cai ◽

Zheng ◽

Cheng ◽

Tian ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Near Infrared ◽

Vegetation Indices ◽

Crop Growth ◽

Digital Cameras ◽

Status Monitoring ◽

Aerial Vehicle ◽

Rgb Images ◽

N Status

Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.

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Crop Type Classification Using Vegetation Indices of RapidEye Imagery

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

10.5194/isprsarchives-xl-7-195-2014 ◽

2014 ◽

Vol XL-7 ◽

pp. 195-198 ◽

Cited By ~ 23

Author(s):

M. Ustuner ◽

F. B. Sanli ◽

S. Abdikan ◽

M. T. Esetlili ◽

Y. Kurucu

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Support Vector ◽

Red Edge ◽

Crop Type ◽

Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

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