Detecting nutrient deficiency in spruce forests using multispectral satellite imagery

Walshe, Dylan; McInerney, Daniel; Kerchove, Ruben Van De; Goyens, Clémence; Balaji, Preethi Malur; Byrne, Kenneth A.

doi:10.1016/j.jag.2019.101975

Cited by 13 publications

(9 citation statements)

References 62 publications

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“…The images were atmospherically corrected and all pixels were resampled to 20 × 20 m using ARCSI [31] and a nearest neighbour algorithm. The third source of auxiliary variables was a multi-spectral derived nutrient deficiency classification for the AOI [32]. The final source of auxiliary variables was the age of the forest stands from Coillte's spatial database.…”

Section: Auxiliary Variablesmentioning

confidence: 99%

“…All of the auxiliary data were georeferenced to Irish Transverse Mercator (EPSG:2157) and quality control of the spatial data suggested a high spatial accuracy between layers. Sentinel 2 Nutrient deficiency classification [32] Internal database Age of forest variables in bold were removed during modelling as they provided less than 1% increase in MSE.…”

Section: Auxiliary Variablesmentioning

confidence: 99%

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Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

et al. 2021

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Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m2 ha−1), stem density (n/ha−1), and volume (m3 ha−1), and subsequently estimates the variance of multiple pixel areas using a k-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R2 values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of k was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the k-NN variance estimation technique in a range of forests landscapes.

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Section: Auxiliary Variablesmentioning

confidence: 99%

Section: Auxiliary Variablesmentioning

confidence: 99%

Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

et al. 2021

Self Cite

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“…Agricultural managers can use the real-time information obtained from the UAS and combine ground measurements to accomplish more reliable techniques for agricultural monitoring. UAS remote sensing has been applied in agriculture to monitor or predict SWC [43], plant population [44,45], crop water stress [46], crop nutrient deficiency [47,48], plant biomass, leaf area [49,50], and crop yield [51,52].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Lin

Guo

et al. 2021

Remote Sensing

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Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. Unmanned aerial systems (UAS) can acquire images with high temporal and spatial resolutions for SWC monitoring at the field scale. The objective of this study was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The normalized difference vegetation index (NDVI) and surface temperature (Ts) derived from the UAS multispectral and thermal images were employed to construct the temperature vegetation dryness index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the texture temperature vegetation dryness index (TTVDI). For validation, 128 surface soil samples, 84 in 2019 and 44 in 2020, were collected to determine soil texture and gravimetric SWC. Based on the linear regression models, the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R2 (coefficient of determination) by 14.5% and 14.9%, and a decrease in RMSE (root mean square error) by 46.1% and 10.8%, for the 2019 and 2020 samples, respectively. The application of the TTVDI model based on high-resolution multispectral and thermal UAS images has the potential to accurately and timely retrieve SWC at the field scale.

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“…However, to the best of our knowledge, in recent years there have been a relatively limited number of studies dealing with modelling P concentration in plants at the leaf level. Moreover, most previous studies estimated the P concentration in tropical forests, crops and grasslands (Li et al 2018;Gama et al 2019;Walshe et al 2020;Munyati, Balzter, and Economon 2020). Studies estimating P concentration over Mediterranean forests are lacking, and few studies have used airborne hyperspectral sensors to predict P concentration in leaf samples.…”

Section: Introductionmentioning

confidence: 99%

Mapping phosphorus concentration in Mediterranean forests using different remote-sensing methods

Mandelmilch

Livne

Dor

et al. 2021

International Journal of Remote Sensing

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Mineral nutrition is essential for optimal plant growth. Phosphorus (P) is a relatively small component of leaf dry weight, with a concentration in plant foliage of less than 1%. Despite its low concentration, P is an essential element in plants, mainly used for energy transfer. Mapping P concentration using traditional methods is expensive and usually limited to a small area; it is timeconsuming and covers only a few plant individuals or species. In this study, we demonstrate the use of remote-sensing (RS) data acquired from the feld and airborne hyperspectral sensors to predict and map the P concentration in leaves of different woody Mediterranean plant species. Comprehensive field work included leaf sampling, laboratory analyses, and spectral measurements using a visible, near-infrared and shortwave-infrared (VIS-NIR-SWIR) field spectrometer. Using different spectral configurations, we built accurate models to predict P concentration in leaf samples. The models were built using a NIR data analysis technique with the data mining software PARACUDA II. This software allowed us to identify the correlative wavelengths for P-bearing molecules in selected woody Mediterranean plant species. The hyperspectralbased model for leaf P concentration was extracted from the reflectance data acquired using a manned aircraft carrying a hyperspectral sensor (Specim AisaFenix 1 K). The model gave a reliable correlation between points extracted from the hyperspectral image and samples measured in the field. We believe that the methodology used in this study will help forest ecologists better understand the concentration of P in the foliage of woody Mediterranean plant species.

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Detecting nutrient deficiency in spruce forests using multispectral satellite imagery

Cited by 13 publications

References 62 publications

Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Mapping phosphorus concentration in Mediterranean forests using different remote-sensing methods

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