Mapping of Scotch Broom (Cytisus scoparius) with Landsat Imagery

Methods were developed and tested for mapping the distribution of Scotch broom, an invasive shrub species expanding its range and disrupting native species and habitats in several parts of the world. During spring, the Scotch broom produces yellow flowers. Landsat imagery during the flower bloom period and during summer was acquired for several years for a study area on Vancouver Island, British Columbia, Canada. Ground-based reflectance measurements plus statistical separability tests were conducted to determine the effectiveness for identifying Scotch broom with Landsat spectral bands, band ratios, vegetation indices, and combinations of bloom and nonbloom imagery. Maximum likelihood classifications of three Scotch broom density classes (dense, ≥ 75% cover; moderate, 25 to 75%; low, 10 to 25%) and other land covers were run with various image and band sets and tested against independent reference sites. Accuracies of classifications using the better band combinations for moderate and dense Scotch broom patches combined were on the order of 80%, with unreliable results for sites of low Scotch broom density. Scotch broom patches less than 0.5 ha were often missed. Some commission error occurred (areas erroneously classified as Scotch broom). Suggested improvements are the use of time series of classifications over multiple years, incorporating knowledge of Scotch broom spread mechanisms or temperature and elevation limitations, and use of higher resolution satellites if the expense warrants it. Despite some limitations, a satellite-based remote sensing approach may be useful for aspects of Scotch broom management.

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

Remote Sensing ◽

10.3390/rs13030536 ◽

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.

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High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

Journal of Forestry Research ◽

10.1007/s11676-021-01360-6 ◽

2021 ◽

Author(s):

Marcus Vinicius Vieira Borges ◽

Janielle de Oliveira Garcia ◽

Tays Silva Batista ◽

Alexsandra Nogueira Martins Silva ◽

Fabio Henrique Rojo Baio ◽

...

Keyword(s):

Neural Networks ◽

High Throughput ◽

Mean Squared Error ◽

Vegetation Indices ◽

Plant Size ◽

Eucalyptus Species ◽

Forest Inventories ◽

Spectral Bands ◽

High Throughput Phenotyping ◽

Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

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Winter wheat vegetation indices calculated from combinations of seven spectral bands

Remote Sensing of Environment ◽

10.1016/0034-4257(85)90061-6 ◽

1985 ◽

Vol 18 (3) ◽

pp. 255-267 ◽

Cited By ~ 26

Author(s):

D.A Dusek ◽

R.D Jackson ◽

J.T Musick

Keyword(s):

Winter Wheat ◽

Vegetation Indices ◽

Spectral Bands

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Soil texture and other site-level factors differentially affect growth of Scotch broom (Cytisus scoparius) and Douglas-fir (Pseudotsuga menziesii) seedlings in the western Pacific Northwest

Canadian Journal of Forest Research ◽

10.1139/cjfr-2021-0011 ◽

2021 ◽

Author(s):

David Carter ◽

Robert A. Slesak ◽

Timothy B. Harrington ◽

Anthony W. D’Amato

Keyword(s):

Pacific Northwest ◽

Pseudotsuga Menziesii ◽

Height Growth ◽

Douglas Fir ◽

Western Pacific ◽

Limiting Factor ◽

Cytisus Scoparius ◽

Invasive Shrub ◽

Scotch Broom ◽

The Western Pacific

The invasive shrub Scotch broom (Cytisus scoparius (L.) Link) is a pervasive threat to regenerating Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) stands in the Pacific Northwest, USA. Field observations indicate that the susceptibility of areas to Scotch broom invasion and dominance can vary by site. We selected ten sites throughout the western Pacific Northwest that spanned a gradient of soil textures and other factors to test the site-specific susceptibility of Douglas-fir to overtopping by Scotch broom. We expected to find that the ability of Scotch broom to dominate a site was mediated by site-level factors, particularly those influencing soil water – the most limiting factor to growth in the region. We found Scotch broom and Douglas-fir were inversely affected by site-level factors. In general, Douglas-fir absolute height growth rates were more competitive with those of Scotch broom on fine-textured soils than on more coarsely textured soils. We also found Douglas-fir to have a more dramatic response to increasing down woody material than Scotch broom. Scotch broom height growth approached an asymptote at 3 m. Sites with fast-growing Douglas-fir were able to surpass this height six to seven years after planting and appear likely to avoid suppression by Scotch broom.

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

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702016000100006 ◽

2016 ◽

Vol 22 (1) ◽

pp. 95-107 ◽

Cited By ~ 9

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.

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Vegetation indices and NIR-SWIR spectral bands as a phenotyping tool for water status determination in soybean

Precision Agriculture ◽

10.1007/s11119-020-09740-4 ◽

2020 ◽

Vol 22 (1) ◽

pp. 249-266

Author(s):

P. Braga ◽

L. G. T. Crusiol ◽

M. R. Nanni ◽

A. L. H. Caranhato ◽

M. B. Fuhrmann ◽

...

Keyword(s):

Water Status ◽

Vegetation Indices ◽

Spectral Bands

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Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

Remote Sensing ◽

10.3390/rs11050570 ◽

2019 ◽

Vol 11 (5) ◽

pp. 570 ◽

Cited By ~ 6

Author(s):

Inacio Bueno ◽

Fausto Acerbi Júnior ◽

Eduarda Silveira ◽

José Mello ◽

Luís Carvalho ◽

...

Keyword(s):

Time Series ◽

Change Detection ◽

Seasonal Changes ◽

Vegetation Indices ◽

Detection Methods ◽

Object Based Image Analysis ◽

Spectral Bands ◽

Object Based ◽

Shortwave Infrared ◽

Spectral Channels

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.

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Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery

Remote Sensing ◽

10.3390/rs12162534 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2534

Author(s):

Aliny A. Dos Reis ◽

João P. S. Werner ◽

Bruna C. Silva ◽

Gleyce K. D. A. Figueiredo ◽

João F. G. Antunes ◽

...

Keyword(s):

Aboveground Biomass ◽

Vegetation Indices ◽

Canopy Height ◽

Gradient Boosting ◽

Remotely Sensed Data ◽

Spatiotemporal Resolution ◽

Spectral Bands ◽

Extreme Gradient Boosting ◽

Intensively Managed ◽

New Generation

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

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Can Medium-Resolution Satellite Imagery Measure Economic Activity at Small Geographies? Evidence from Landsat in Vietnam

The World Bank Economic Review ◽

10.1093/wber/lhz001 ◽

2019 ◽

Vol 34 (3) ◽

pp. 635-653 ◽

Cited By ~ 1

Author(s):

Ran Goldblatt ◽

Kilian Heilmann ◽

Yonatan Vaizman

Keyword(s):

Cross Section ◽

Economic Activity ◽

Regression Models ◽

Landsat Imagery ◽

Spectral Bands ◽

Nighttime Lights ◽

Medium Resolution ◽

Changes Over Time ◽

Simple Regression ◽

Over Time

Abstract This study explores the potential and the limits of medium-resolution satellite data as a proxy for economic activity at small geographic units. Using a commune-level dataset from Vietnam, it compares the performance of commonly used nightlight data and higher resolution Landsat imagery, which measures daytime light reflection. The analysis suggests that Landsat outperforms nighttime lights at predicting enterprise counts, employment, and expenditure in simple regression models. A parsimonious combination of the first two moments of the Landsat spectral bands can explain a reasonable share of the variation in economic activity in the cross-section. There is, however, poor prediction power of either satellite measure for changes over time.

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Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente

Revista de Teledetección ◽

10.4995/raet.2020.13394 ◽

2020 ◽

pp. 31

Author(s):

M. P. Martín ◽

J. Pacheco-Labrador ◽

R. González-Cascón ◽

G. Moreno ◽

M. Migliavacca ◽

...

Keyword(s):

Structural Characteristics ◽

Vegetation Indices ◽

Matter Content ◽

Tree Cover ◽

Dry Matter Content ◽

Least Squares Regression ◽

Area Index ◽

Remote Sensors ◽

Spectral Bands ◽

The Relationship

Mixed vegetation systems such as wood pastures and shrubby pastures are vital for extensive and sustainable livestock production as well as for the conservation of biodiversity and provision of ecosystem services, and are mostly located in areas that are expected to be more strongly affected by climate change. However, the structural characteristics, phenology, and the optical properties of the vegetation in these mixed -ecosystems such as savanna-like ecosystems in the Iberian Peninsula which combines herbaceous and/or shrubby understory with a low density tree cover, constitute a serious challenge for the remote sensing studies. This work combines physical and empirical methods to improve the estimation of essential vegetation variables: leaf area index (LAI, m2 / m2 ), leaf (Cab,leaf, μg / cm2 ) and canopy(Cab,canopy, g / m2 ) chlorophyll content, and leaf (Cm, leaf, g / cm2 ) and canopy (Cm,canopy, g / m2 ) dry matter content in a dehesa ecosystem. For this purpose, a spectral simulated database for the four main phenological stages of the highly dynamic herbaceous layer (summer senescence, autumn regrowth, greenness peak and beginning of senescence), was built by coupling PROSAIL and FLIGHT radiative transfer models. This database was used to calibrate different predictive models based on vegetation indices (VI) proposed in the literature which combine different spectral bands; as well as Partial Least Squares Regression (PLSR) using all bands in the simulated spectral range (400-2500 nm). PLSR models offered greater predictive power (R2 ≥ 0.93, RRMSE ≤ 10.77 %) both for the leaf and canopy- level variables. The results suggest that directional and geometric effects control the relationships between simulated reflectance factors and the foliar parameters. High seasonal variability is observed in the relationship between biophysical variables and IVs, especially for LAI and Cab, which is confirmed in the PLSR analysis. The models developed need to be validated with spectral data obtained either with proximal or remote sensors.

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