scholarly journals Estimating individual level plant traits at scale

2019 ◽  
Author(s):  
Sergio Marconi ◽  
Sarah J. Graves ◽  
Ben. G. Weinstein ◽  
Stephanie Bohlman ◽  
Ethan P. White
Keyword(s):  
Remote Sensing ◽  
Test Data ◽  
Large Scale ◽  
Plant Traits ◽  
Scale Up ◽  
Functional Ecology ◽  
Individual Level ◽  
Structural Traits ◽  
Individual Trees ◽  
Better Than

AbstractFunctional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals 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 are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual); or (2) using remote sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: 1) image segmentation, to identify individual trees and estimate structural traits; 2) ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and 3) predictions for segmented crowns for the full remote sensing footprint at the NEON sites.The R2 values on held out test data ranged from 0.41 to 0.75 on held out test data. The ensemble approach performed better than single partial least squares models. Carbon performed poorly compared to other traits (R2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over-segmentation. The pipeline produced good estimates of DBH (R2 of 0.62 on held out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R2 on test data of between to 0.26. We used the pipeline to produce individual level trait data for ∼5 million individual crowns, covering a total extent of ∼360 km2. This large dataset allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.

scholarly journals Rethinking the Fourier-Mellin Transform: Multiple Depths in the Camera’s View

2021 ◽  
Vol 13 (5) ◽  
pp. 1000
Author(s):  
Qingwen Xu ◽  
Haofei Kuang ◽  
Laurent Kneip ◽  
Sören Schwertfeger
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Feature Detection ◽  
Mellin Transform ◽  
State Of The Art ◽  
Visual Odometry ◽  
Superior Performance ◽  
Fourier Mellin Transform ◽  
And Robotics ◽  
Better Than

Remote sensing and robotics often rely on visual odometry (VO) for localization. Many standard approaches for VO use feature detection. However, these methods will meet challenges if the environments are feature-deprived or highly repetitive. Fourier-Mellin Transform (FMT) is an alternative VO approach that has been shown to show superior performance in these scenarios and is often used in remote sensing. One limitation of FMT is that it requires an environment that is equidistant to the camera, i.e., single-depth. To extend the applications of FMT to multi-depth environments, this paper presents the extended Fourier-Mellin Transform (eFMT), which maintains the advantages of FMT with respect to feature-deprived scenarios. To show the robustness and accuracy of eFMT, we implement an eFMT-based visual odometry framework and test it in toy examples and a large-scale drone dataset. All these experiments are performed on data collected in challenging scenarios, such as, trees, wooden boards and featureless roofs. The results show that eFMT performs better than FMT in the multi-depth settings. Moreover, eFMT also outperforms state-of-the-art VO algorithms, such as ORB-SLAM3, SVO and DSO, in our experiments.

scholarly journals A general quantitative theory of forest structure and dynamics

2009 ◽  
Vol 106 (17) ◽  
pp. 7040-7045 ◽  
Author(s):  
Geoffrey B. West ◽  
Brian J. Enquist ◽  
James H. Brown
Keyword(s):  
Size Structure ◽  
Dynamic Properties ◽  
Scale Up ◽  
Spatial Arrangement ◽  
Emergent Properties ◽  
Frequency Distributions ◽  
Individual Level ◽  
Quantitative Theory ◽  
Structure And Dynamics ◽  
Individual Trees

We present the first part of a quantitative theory for the structure and dynamics of forests at demographic and resource steady state. The theory uses allometric scaling relations, based on metabolism and biomechanics, to quantify how trees use resources, fill space, and grow. These individual-level traits and properties scale up to predict emergent properties of forest stands, including size–frequency distributions, spacing relations, resource flux rates, and canopy configurations. Two insights emerge from this analysis: (i) The size structure and spatial arrangement of trees in the entire forest are emergent manifestations of the way that functionally invariant xylem elements are bundled together to conduct water and nutrients up from the trunks, through the branches, to the leaves of individual trees. (ii) Geometric and dynamic properties of trees in a forest and branches in trees scale identically, so that the entire forest can be described mathematically and behaves structurally and functionally like a scaled version of the branching networks in the largest tree. This quantitative framework uses a small number of parameters to predict numerous structural and dynamical properties of idealized forests.

scholarly journals Conformity bias in the cultural transmission of music sampling traditions

2019 ◽  
Vol 6 (9) ◽  
pp. 191149 ◽  
Author(s):  
Mason Youngblood
Keyword(s):  
Cultural Transmission ◽  
Large Scale ◽  
Scale Up ◽  
Population Level ◽  
Neutral Evolution ◽  
Individual Level ◽  
Online Databases ◽  
Approximate Bayesian ◽  
Cultural Evolutionary ◽  
Turn Over

One of the fundamental questions of cultural evolutionary research is how individual-level processes scale up to generate population-level patterns. Previous studies in music have revealed that frequency-based bias (e.g. conformity and novelty) drives large-scale cultural diversity in different ways across domains and levels of analysis. Music sampling is an ideal research model for this process because samples are known to be culturally transmitted between collaborating artists, and sampling events are reliably documented in online databases. The aim of the current study was to determine whether frequency-based bias has played a role in the cultural transmission of music sampling traditions, using a longitudinal dataset of sampling events across three decades. Firstly, we assessed whether turn-over rates of popular samples differ from those expected under neutral evolution. Next, we used agent-based simulations in an approximate Bayesian computation framework to infer what level of frequency-based bias likely generated the observed data. Despite anecdotal evidence of novelty bias, we found that sampling patterns at the population-level are most consistent with conformity bias. We conclude with a discussion of how counter-dominance signalling may reconcile individual cases of novelty bias with population-level conformity.

scholarly journals A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ben G Weinstein ◽  
Sergio Marconi ◽  
Stephanie A Bohlman ◽  
Alina Zare ◽  
Aditya Singh ◽  
...  
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Spatial Location ◽  
The United States ◽  
Sensor Data ◽  
Data Set ◽  
Individual Tree ◽  
Individual Level ◽  
Remote Sensing Techniques ◽  
Individual Trees

Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network’s Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.

scholarly journals NEON Crowns: a remote sensing derived dataset of 100 million individual tree crowns

2020 ◽  
Author(s):  
Ben. G. Weinstein ◽  
Sergio Marconi ◽  
Stephanie Bohlman ◽  
Alina Zare ◽  
Aditya Singh ◽  
...  
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Spatial Location ◽  
The United States ◽  
Sensor Data ◽  
Good Correspondence ◽  
Individual Tree ◽  
Individual Level ◽  
Tree Crowns ◽  
Individual Trees

AbstractForests provide essential biodiversity, ecosystem and economic services. Information on individual trees is important for understanding the state of forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain significant technical and computational challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source dataset of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network’s Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. Tree crowns identified using this technique correspond well with hand-labeled crowns, exhibiting both high levels of overlap and good correspondence in height estimates. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses at scales of ~10,000 ha and cross-region comparisons encompassing forest types from most of the United States.

PENGEMBANGAN MEDIA BIG BOOK UNTUK MENINGKATKAN HASIL BELAJAR PECAHAN SENILAI SISWA SD

2018 ◽  
Vol 16 (1) ◽  
pp. 67-76
Author(s):  
Disyacitta Neolia Firdana ◽  
Trimurtini Trimurtini
Keyword(s):  
Learning Outcomes ◽  
Large Scale ◽  
Fourth Grade ◽  
Learning Activities ◽  
Equivalent Fractions ◽  
Fourth Grade Students ◽  
Teacher Needs ◽  
The Media ◽  
Test Result ◽  
Better Than

This research aimed to determine the properness and effectiveness of the big book media on learning equivalent fractions of fourth grade students. The method of research is Research and Development  (R&D). This study was conducted in fourth grade of SDN Karanganyar 02 Kota Semarang. Data sources from media validation, material validation, learning outcomes, and teacher and students responses on developed media. Pre-experimental research design with one group pretest-posttest design. Big book developed consist of equivalent fractions material, students learning activities sheets with rectangle and circle shape pictures, and questions about equivalent fractions. Big book was developed based on students and teacher needs. This big book fulfill the media validity of 3,75 with very good criteria and scored 3 by material experts with good criteria. In large-scale trial, the result of students posttest have learning outcomes completness 82,14%. The result of N-gain calculation with result 0,55 indicates the criterion “medium”. The t-test result 9,6320 > 2,0484 which means the average of posttest outcomes is better than the average of pretest outcomes. Based on that data, this study has produced big book media which proper and effective as a media of learning equivalent fractions of fourth grade elementary school.

Solid Lipid Nanoparticles: A Promising Drug Delivery Technology

2009 ◽  
Vol 2 (2) ◽  
pp. 509-516
Author(s):  
S. Pragati ◽  
S. Kuldeep ◽  
S. Ashok ◽  
M. Satheesh
Keyword(s):  
Drug Delivery ◽  
Solid Lipid Nanoparticles ◽  
Large Scale ◽  
Colloidal Particles ◽  
Scale Up ◽  
Lipid Nanoparticles ◽  
Scale Production ◽  
Research Activity ◽  
New Approach ◽  
Solid Lipid

One of the situations in the treatment of disease is the delivery of efficacious medication of appropriate concentration to the site of action in a controlled and continual manner. Nanoparticle represents an important particulate carrier system, developed accordingly. Nanoparticles are solid colloidal particles ranging in size from 1 to 1000 nm and composed of macromolecular material. Nanoparticles could be polymeric or lipidic (SLNs). Industry estimates suggest that approximately 40% of lipophilic drug candidates fail due to solubility and formulation stability issues, prompting significant research activity in advanced lipophile delivery technologies. Solid lipid nanoparticle technology represents a promising new approach to lipophile drug delivery. Solid lipid nanoparticles (SLNs) are important advancement in this area. The bioacceptable and biodegradable nature of SLNs makes them less toxic as compared to polymeric nanoparticles. Supplemented with small size which prolongs the circulation time in blood, feasible scale up for large scale production and absence of burst effect makes them interesting candidates for study. In this present review this new approach is discussed in terms of their preparation, advantages, characterization and special features.

Recombinant Protein Production in Microalgae: Emerging Trends

2020 ◽  
Vol 27 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Niaz Ahmad ◽  
Muhammad Aamer Mehmood ◽  
Sana Malik
Keyword(s):  
Chloroplast Genome ◽  
Recombinant Proteins ◽  
Large Scale ◽  
Higher Plants ◽  
Scale Up ◽  
Chloroplast Transformation ◽  
Point Of View ◽  
Nuclear Transformation ◽  
Scale Production ◽  
Algal Chloroplast

: In recent years, microalgae have emerged as an alternative platform for large-scale production of recombinant proteins for different commercial applications. As a production platform, it has several advantages, including rapid growth, easily scale up and ability to grow with or without the external carbon source. Genetic transformation of several species has been established. Of these, Chlamydomonas reinhardtii has become significantly attractive for its potential to express foreign proteins inexpensively. All its three genomes – nuclear, mitochondrial and chloroplastic – have been sequenced. As a result, a wealth of information about its genetic machinery, protein expression mechanism (transcription, translation and post-translational modifications) is available. Over the years, various molecular tools have been developed for the manipulation of all these genomes. Various studies show that the transformation of the chloroplast genome has several advantages over nuclear transformation from the biopharming point of view. According to a recent survey, over 100 recombinant proteins have been expressed in algal chloroplasts. However, the expression levels achieved in the algal chloroplast genome are generally lower compared to the chloroplasts of higher plants. Work is therefore needed to make the algal chloroplast transformation commercially competitive. In this review, we discuss some examples from the algal research, which could play their role in making algal chloroplast commercially successful.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

Expanding the boundaries of learning

2021 ◽  
Vol 102 (8) ◽  
pp. 8-13
Author(s):  
Thomas Hatch
Keyword(s):  
Instructional Practices ◽  
New Hampshire ◽  
Large Scale ◽  
Educational Experiences ◽  
Scale Up ◽  
Project Based Learning

Taking advantage of the possibilities for learning outside of school requires us to build on what we know about why it is so hard to sustain and scale up unconventional educational experiences within conventional schools. To illustrate the opportunities and challenges, Thomas Hatch describes a large-scale approach to project-based learning developed in a camp in New Hampshire and incorporated in a Brooklyn school, a trip-based program in Detroit, and Singapore’s systemic embrace of learning outside school. By understanding the conditions that can sustain alternative instructional practices, educators can find places to challenge the boundaries of schooling and create visions of the possible that exceed current constraints.

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