FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2915375198> ?p ?o ?g. }

• W2915375198 startingPage "556472" @default.
• W2915375198 abstract "Abstract Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features of individuals that affect their fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions in ecosystems are generated by (1) collecting a few leaves from a small number of trees, or (2) using remote sensing images to infer traits. While the first method suffers from limited, and potentially biased, sampling, it produces information at the level of the fundamental ecological unit: the individual. On the contrary, remote sensing produces information in a continuum space whose unit is usually the plot, containing dozens of trees of different species. The National Ecological Observatory Network (NEON) provides data collected both from the field and 1m2 resolution remote sensing. Using these data we developed a method to scale up functional traits measured on 160 trees to the full spatial extent of NEON sites (millions of crowns). The first step of our pipeline involved the use of image segmentation methods to infer crown geometry. Then, we built models to infer Leaf Mass Area (LMA), nitrogen, carbon, and phosphorus content. We developed a naive multiple instance regression method based on an ensemble of Partial Least Squares Generalized Linear regressions (pls-GLR). Results were applied both at the pixel scale (1 m2) and aggregated to the crown scale for comparison. Finally, we applied the models to two NEON sites, Ordway Swisher Biological Station (OSBS) and Talladega (TALL) to estimate the location and leaf traits of approximately 5 million trees. Nitrogen, LMA and phosphorus models had R2 values ranging from 0.50 to 0.75 on held out test data, comparable with what has been observed in studies conducted at the plot scale. The ensemble resulted in better predictability than single pls-GLM models. Models for carbon performed poorly compared to other traits (R2 of 0.22). Crown delineation produced the highest uncertainty in the pipeline, generally due to over-segmentation of crowns. The intersection over union between segmented and ground-truth crowns ranged between 0.24% and 0.78%. Despite this uncertainty, crown scale predictions performed significantly better than pixel predictions, resulting in improvement of R2 on test data of between 0.07 to 0.20 points. We used the resulting models to produce individual trait data for ~2.5 million individual crowns for both OSBS and TALL, covering a total extent of ~360 km2. In these “derived-data” LMA, %N, and %P were highly correlated as expected. Chemical traits were highly correlated to some physical traits like LAI, and to elevation. Structural traits like crown area and height were correlated to chemical traits only in OSBS." @default.
• W2915375198 created "2019-03-02" @default.
• W2915375198 creator A5001850958 @default.
• W2915375198 creator A5026770889 @default.
• W2915375198 creator A5028916813 @default.
• W2915375198 creator A5071762860 @default.
• W2915375198 creator A5090854014 @default.
• W2915375198 date "2019-02-21" @default.
• W2915375198 modified "2023-09-23" @default.
• W2915375198 title "Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale" @default.
• W2915375198 cites W1086254176 @default.
• W2915375198 cites W1541726269 @default.
• W2915375198 cites W164087953 @default.
• W2915375198 cites W1683383071 @default.
• W2915375198 cites W195732057 @default.
• W2915375198 cites W1977970167 @default.
• W2915375198 cites W1979795508 @default.
• W2915375198 cites W1984216839 @default.
• W2915375198 cites W1984792953 @default.
• W2915375198 cites W1985291094 @default.
• W2915375198 cites W1985712136 @default.
• W2915375198 cites W1993895874 @default.
• W2915375198 cites W2032413422 @default.
• W2915375198 cites W2033803508 @default.
• W2915375198 cites W2039768055 @default.
• W2915375198 cites W2052021470 @default.
• W2915375198 cites W2059697555 @default.
• W2915375198 cites W2068845501 @default.
• W2915375198 cites W2070315926 @default.
• W2915375198 cites W2075435443 @default.
• W2915375198 cites W2080677313 @default.
• W2915375198 cites W2094887943 @default.
• W2915375198 cites W2101364071 @default.
• W2915375198 cites W2102094072 @default.
• W2915375198 cites W2107625277 @default.
• W2915375198 cites W2117329063 @default.
• W2915375198 cites W2118295263 @default.
• W2915375198 cites W2121748258 @default.
• W2915375198 cites W2123127253 @default.
• W2915375198 cites W2124569934 @default.
• W2915375198 cites W2127473051 @default.
• W2915375198 cites W2144042033 @default.
• W2915375198 cites W2145040893 @default.
• W2915375198 cites W2146751368 @default.
• W2915375198 cites W2151549592 @default.
• W2915375198 cites W2153147674 @default.
• W2915375198 cites W2156555857 @default.
• W2915375198 cites W2162161769 @default.
• W2915375198 cites W2165044919 @default.
• W2915375198 cites W2166079210 @default.
• W2915375198 cites W2169353679 @default.
• W2915375198 cites W2169678197 @default.
• W2915375198 cites W2170408123 @default.
• W2915375198 cites W2179141461 @default.
• W2915375198 cites W2182852097 @default.
• W2915375198 cites W2195651936 @default.
• W2915375198 cites W2218052335 @default.
• W2915375198 cites W2266690409 @default.
• W2915375198 cites W2279188631 @default.
• W2915375198 cites W2280611282 @default.
• W2915375198 cites W2315994349 @default.
• W2915375198 cites W2342299051 @default.
• W2915375198 cites W2344880441 @default.
• W2915375198 cites W2345760618 @default.
• W2915375198 cites W2460271296 @default.
• W2915375198 cites W2460403273 @default.
• W2915375198 cites W2473991816 @default.
• W2915375198 cites W2474463858 @default.
• W2915375198 cites W2487661149 @default.
• W2915375198 cites W2521632916 @default.
• W2915375198 cites W2561648625 @default.
• W2915375198 cites W2582323655 @default.
• W2915375198 cites W2587254133 @default.
• W2915375198 cites W2587880456 @default.
• W2915375198 cites W2589601645 @default.
• W2915375198 cites W2616764377 @default.
• W2915375198 cites W2792227970 @default.
• W2915375198 cites W2794870874 @default.
• W2915375198 cites W2801514148 @default.
• W2915375198 cites W2810426430 @default.
• W2915375198 cites W2890663740 @default.
• W2915375198 cites W2895308688 @default.
• W2915375198 cites W2901846858 @default.
• W2915375198 cites W2904039075 @default.
• W2915375198 cites W2912067133 @default.
• W2915375198 cites W2962766617 @default.
• W2915375198 cites W2969711797 @default.
• W2915375198 cites W2983606184 @default.
• W2915375198 cites W3101126838 @default.
• W2915375198 hasPublicationYear "2019" @default.
• W2915375198 type Work @default.
• W2915375198 sameAs 2915375198 @default.
• W2915375198 citedByCount "0" @default.
• W2915375198 crossrefType "posted-content" @default.
• W2915375198 hasAuthorship W2915375198A5001850958 @default.
• W2915375198 hasAuthorship W2915375198A5026770889 @default.
• W2915375198 hasAuthorship W2915375198A5028916813 @default.
• W2915375198 hasAuthorship W2915375198A5071762860 @default.
• W2915375198 hasAuthorship W2915375198A5090854014 @default.

• next