Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

Ramoelo, Abel; Skidmore, Andrew K.; Cho, Moses Azong; Mathieu, Renaud; Heitkonig, I.M.A.; Dudeni-Tlhone, Nontembeko; Schlerf, Martin; Prins, Herbert H. T.

doi:10.1016/j.isprsjprs.2013.04.012

Cited by 103 publications

(76 citation statements)

References 84 publications

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“…In addition, the dimensionality of hyperspectral features might not be captured in a linear projection. Therefore, non-linear classification methods such as random forest, that produce variable selection as a by-product during the learning process, are considered efficient algorithms for the analyses of hyperspectral data especially in biomes where spectral mixing is highly non-linear (e.g., [21][22][23][24][25][26]). …”

Section: Introductionmentioning

confidence: 99%

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

et al. 2015

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Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land use/land cover (LULC) maps for a study site in Kenya. AISA Eagle imagery was captured at the early flowering period (January 2014) and at the peak flowering season (February 2013). Data on white and yellow flowering trees as well as LULC classes in the study area were collected and used as ground-truth points. We utilized all 64 AISA Eagle bands and also used variable importance in RF to identify the most important bands in both AISA Eagle data sets. The results showed that flowering was most accurately mapped using the AISA Eagle data from the peak flowering period (85.71%-88.15% overall accuracy for the peak flowering OPEN ACCESSRemote Sens. 2015, 7 13299 season imagery versus 80.82%-83.67% for the early flowering season). The variable optimization (i.e., variable selection) analysis showed that less than half of the AISA bands (n = 26 for the February 2013 data and n = 21 for the January 2014 data) were important to attain relatively reliable classification accuracies. Our study is an important first step towards the development of operational flower mapping routines and for understanding the relationship between flowering and bees' foraging behavior.

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Section: Introductionmentioning

confidence: 99%

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

et al. 2015

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“…Because these trait-driven nuances in spectral signatures may be obscured by the broad spectral sensitivity of broad band sensors, attention turns to imaging spectroscopy (also known as hyperspectral remote sensing) to register the spectral signal in many small width spectral bands [5]. Small width absorption features have thus been related to various traits, both directly on leaf level (e.g., [6,7]), at canopy level using field spectroscopy (e.g., [8][9][10]) as well as at regional scales using airborne imaging spectroscopy (e.g., [11]).…”

Section: Introductionmentioning

confidence: 99%

“…Often used are empirical, statistical methods where (derivatives of) reflectance data, such as narrow band spectral indices [9,21,22] or continuum removed absorption spectra [23], are related to observed trait values using statistical techniques such as stepwise regression [23,24] or non-linear partial least squares regression (PLSR) [5]. So far, such empirical relations have been found to be poorly transferable between different locations or between different moments at the same location [18], which may be partly related to a lack of considerations on how community-mean traits need to be expressed.…”

Section: Introductionmentioning

confidence: 99%

“…This is of relevance for ecological purposes [29], but also for remote sensing [30]. Often, community-mean traits are simply expressed on a leaf mass basis (e.g., [5,23,24]). Alternatively, traits are expressed on a per leaf surface basis (e.g., µg·cm −2 leaf surface, [8]) or per canopy surface basis (e.g., mg·m −2 canopy surface) [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Trait Estimation in Herbaceous Plant Assemblages from in situ Canopy Spectra

et al. 2013

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Estimating plant traits in herbaceous plant assemblages from spectral reflectance data requires aggregation of small scale trait variations to a canopy mean value that is ecologically meaningful and corresponds to the trait content that affects the canopy spectral signal. We investigated estimation capacities of plant traits in a herbaceous setting and how different trait-aggregation methods influence estimation accuracies. Canopy reflectance of 40 herbaceous plant assemblages was measured in situ and biomass was analysed for N, P and C concentration, chlorophyll, lignin, phenol, tannin and specific water concentration, expressed on a mass basis (mg·g −1 ). Using Specific Leaf Area (SLA) and Leaf Area Index (LAI), traits were aggregated to two additional expressions: mass per leaf surface (mg·m −2 ) and mass per canopy surface (mg·m −2 ). All traits were related to reflectance using partial least squares regression. Accuracy of trait estimation varied between traits but was mainly influenced by the trait expression. Chlorophyll and traits expressed on canopy surface were least accurately estimated. Results are attributed to damping or enhancement of the trait signal upon conversion from mass based trait values to leaf and canopy surface expressions. A priori determination of the most appropriate trait expression is viable by considering plant growing strategies. OPEN ACCESSRemote Sens. 2013, 5 6324

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“…Because there are no currently known adsorbtion features associated with foliar P, Ramoelo et al (2013) used all of the adsorption features associated with chlorophyll, protein, sugar, and starch to determine plant P levels. Starch, sugar, lignin, or protein are often associated with the adsorption of light in the Short-wave infrared range.…”

Section: Pimsteinmentioning

confidence: 99%

Simulating Everglades National Park hydrology and phosphorus transport under existing and future scenarios using numerical modeling

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Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

Cited by 103 publications

References 84 publications

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

Trait Estimation in Herbaceous Plant Assemblages from in situ Canopy Spectra

Simulating Everglades National Park hydrology and phosphorus transport under existing and future scenarios using numerical modeling

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