scholarly journals Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

2021 ◽  
Vol 13 (17) ◽  
pp. 3352
Author(s):  
Tawanda W. Gara ◽  
Parinaz Rahimzadeh-Bajgiran ◽  
Roshanak Darvishzadeh
Keyword(s):  
Remote Sensing ◽  
Forest Ecosystems ◽  
Leaf Traits ◽  
Spatial And Temporal Variation ◽  
Future Research ◽  
Leaf Mass Per Area ◽  
Ecosystem Structure ◽  
Advantages And Disadvantages ◽  
Leaf Mass ◽  
Quantitative Remote Sensing

Quantitative remote sensing of leaf traits offers an opportunity to track biodiversity changes from space. Augmenting field measurement of leaf traits with remote sensing provides a pathway for monitoring essential biodiversity variables (EBVs) over space and time. Detailed information on key leaf traits such as leaf mass per area (LMA) is critical for understanding ecosystem structure and functioning, and subsequently the provision of ecosystem services. Although studies on remote sensing of LMA and related constituents have been conducted for over three decades, a comprehensive review of remote sensing of LMA—a key driver of leaf and canopy reflectance—has been lacking. This paper reviews the current state and potential approaches, in addition to the challenges associated with LMA estimation/retrieval in forest ecosystems. The physiology and environmental factors that influence the spatial and temporal variation of LMA are presented. The scope of scaling LMA using remote sensing systems at various scales, i.e., near ground (in situ), airborne, and spaceborne platforms is reviewed and discussed. The review explores the advantages and disadvantages of LMA modelling techniques from these platforms. Finally, the research gaps and perspectives for future research are presented. Our review reveals that although progress has been made, scaling LMA to regional and global scales remains a challenge. In addition to seasonal tracking, three-dimensional modeling of LMA is still in its infancy. Over the past decade, the remote sensing scientific community has made efforts to separate LMA constituents in physical modelling at the leaf level. However, upscaling these leaf models to canopy level in forest ecosystems remains untested. We identified future opportunities involving the synergy of multiple sensors, and investigated the utility of hybrid models, particularly at the canopy and landscape levels.

scholarly journals A Review of Remote Sensing Image Dehazing

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3926
Author(s):  
Juping Liu ◽  
Shiju Wang ◽  
Xin Wang ◽  
Mingye Ju ◽  
Dengyin Zhang
Keyword(s):  
Remote Sensing ◽  
Data Driven ◽  
Future Research ◽  
Research Focus ◽  
Image Dehazing ◽  
Surface Information ◽  
Haze Removal ◽  
Advantages And Disadvantages ◽  
Extra Information ◽  
Removal Technique

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated.

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests

2011 ◽  
Vol 21 (1) ◽  
pp. 85-98 ◽  
Author(s):  
Gregory P. Asner ◽  
Roberta E. Martin ◽  
Raul Tupayachi ◽  
Ruth Emerson ◽  
Paola Martinez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tropical Forests ◽  
Leaf Mass Per Area ◽  
Leaf Mass

scholarly journals Estimation of Chlorophyll Fluorescence at Different Scales: A Review

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3000 ◽  
Author(s):  
Zhuoya Ni ◽  
Qifeng Lu ◽  
Hongyuan Huo ◽  
Huili Zhang
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Multiple Scales ◽  
Future Research ◽  
Passive Remote Sensing ◽  
Advantages And Disadvantages ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Key Issues ◽  
Non Destructive

Measuring chlorophyll fluorescence is a direct and non-destructive way to monitor vegetation. In this paper, the fluorescence retrieval methods from multiple scales, ranging from near the ground to the use of space-borne sensors, are analyzed and summarized in detail. At the leaf-scale, the chlorophyll fluorescence is measured using active and passive technology. Active remote sensing technology uses a fluorimeter to measure the chlorophyll fluorescence, and passive remote sensing technology mainly depends on the sun-induced chlorophyll fluorescence filling in the Fraunhofer lines or oxygen absorptions bands. Based on these retrieval principles, many retrieval methods have been developed, including the radiance-based methods and the reflectance-based methods near the ground, as well as physically and statistically-based methods that make use of satellite data. The advantages and disadvantages of different approaches for sun-induced chlorophyll fluorescence retrieval are compared and the key issues of the current sun-induced chlorophyll fluorescence retrieval algorithms are discussed. Finally, conclusions and key problems are proposed for the future research.

Relationships among leaf traits of Australian arid zone plants: alternative modes of thermal protection

2012 ◽  
Vol 60 (6) ◽  
pp. 471 ◽  
Author(s):  
Ellen M. Curtis ◽  
Andrea Leigh ◽  
Scott Rayburg
Keyword(s):  
Water Content ◽  
Structural Damage ◽  
Near Infrared ◽  
Thermal Protection ◽  
Arid Zone ◽  
Leaf Traits ◽  
Leaf Mass Per Area ◽  
Leaf Economics Spectrum ◽  
Leaf Trait ◽  
Leaf Mass

Despite the importance of leaf traits that protect against critically high leaf temperatures, relationships among such traits have not been investigated. Further, while some leaf trait relationships are well documented across biomes, little is known about such associations within a biome. This study investigated relationships between nine leaf traits that protect leaves against excessively high temperatures in 95 Australian arid zone species. Seven morphological traits were measured: leaf area, length, width, thickness, leaf mass per area, water content, and an inverse measure of pendulousness. Two spectral properties were measured: reflectance of visible and near-infrared radiation. Three key findings emerged: (1) leaf pendulousness increased with leaf size and leaf mass per area, the former relationship suggesting that pendulousness affords thermal protection when leaves are large; (2) leaf mass per area increased with thickness and decreased with water content, indicating alternative means for protection through increasing thermal mass; (3) spectral reflectance increased with leaf mass per area and thickness and decreased with water content. The consistent co-variation of thermal protective traits with leaf mass per area, a trait not usually associated with thermal protection, suggests that these traits fall along the leaf economics spectrum, with leaf longevity increasing through protection not only against structural damage but also against heat stress.

scholarly journals A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

2021 ◽  
Author(s):  
Peng Liu
Keyword(s):  
Remote Sensing ◽  
Data Fusion ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Observation Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Adversarial Networks ◽  
Active Research

In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

scholarly journals Leaf traits drive differences in biomass partitioning among major plant functional types

2015 ◽  
Author(s):  
Remko A Duursma ◽  
Daniel S Falster
Keyword(s):  
Global Climate ◽  
Empirical Studies ◽  
Leaf Traits ◽  
Biomass Partitioning ◽  
Plant Functional Types ◽  
Leaf Mass Per Area ◽  
Leaf Mass ◽  
Functional Types ◽  
Vegetation Models ◽  
Climate Space

1. The partitioning of biomass into leaves and stems is one of the most uncertain and influential components of global vegetation models (GVMs). Although GVMs typically assume that the major woody plant functional types (PFTs) differ in biomass partitioning, empirical studies have not been able to justify these differences. Here we test for differences between PFTs in partitioning of biomass between leaves and stems. 2. We use the recently published Biomass And Allometry Database (BAAD), a large database including observations for individual plants. The database covers the global climate space, allowing us to test for direct climate effects in addition to PFT. 3. The leaf mass fraction (LMF, leaf / total aboveground biomass) varied strongly between PFTs (as defined by deciduous vs. evergreen and gymnosperm vs. angiosperm). We found that LMF, once corrected for plant height, was proportional to leaf mass per area across PFTs. As a result, the PFTs did not differ in the amount of leaf area supported per unit above ground biomass. We found only weak and inconsistent effects of climate on biomass partitioning. 4. Combined, these results uncover fundamental rules in how plants are constructed and allow for systematic benchmarking of biomass partitioning routines in GVMs.

scholarly journals Phenology, seasonality and trait relationships in a New Zealand forest

2021 ◽  
Author(s):  
◽  
Sharada Paudel
Keyword(s):  
New Zealand ◽  
Leaf Traits ◽  
Leaf Longevity ◽  
Leaf Abscission ◽  
Future Research ◽  
Leaf Mass Per Area ◽  
Fruit Traits ◽  
Leaf Production ◽  
Leaf Economic Spectrum ◽  
The Relationship

<p>The phenologies of flowers, fruits and leaves can have profound implications for plant community structure and function. Despite this only a few studies have documented fruit and flower phenologies in New Zealand while there are even fewer studies on leaf production and abscission phenologies. To address this limitation, I measured phenological patterns in leaves, flowers and fruits in 12 common forest plant species in New Zealand over two years. All three phenologies showed significant and consistent seasonality with an increase in growth and reproduction around the onset of favourable climatic conditions; flowering peaked in early spring, leaf production peaked in mid-spring and fruit production peaked in mid-summer coincident with annual peaks in temperature and photoperiodicity. Leaf abscission, however, occurred in late autumn, coincident with the onset of less productive environmental conditions. I also investigated differences in leaf longevities and assessed how seasonal cycles in the timing of leaf production and leaf abscission times might interact with leaf mass per area (LMA) in determining leaf longevity. Leaf longevity was strongly associated with LMA but also with seasonal variation in climate. All 12 species produced leaves in spring and abscised leaves in autumn. Nevertheless, leaf longevity ranged from 6 months to 30 months among species, leading to several distinct leaf longevity categories (i.e. 6-7 months, 15-18 months and 27-30 months). Finally, I examined the relationship of leaf traits with flower and fruit traits and their relation to the global leaf economic spectrum (LES) that describes multivariate correlations between a combinations of key leaf traits. The results resonated with the patterns of leaf economic spectrum for New Zealand species and provided evidence for significant correlations between leaf and fruit traits, indicating that plants with long lived leaves and higher LMA produce fruits that take more time to develop, stay on the plant longer and have larger seed size. This study contributed to bridging the gap in our understanding of the relationship between vegetative and reproductive traits, it has increased our understanding of phenological patterns in New Zealand forests, and when viewed with earlier phenological studies, provides a first step towards understanding how New Zealand forest might respond to global climate change. In addition, the research illustrates how seasonality in climate can constrain the life times of leaves. In the context of global trait research culminating into the whole plant economics spectrum, this study provides clear evidence of leaf and fruit phenological and morphological trait associations. It helps to further our understanding of phenology, seasonality and plant trait relationships for some common tree species in New Zealand and presents some novel findings that provide a basis for future research.</p>

scholarly journals Effects of Increased N Deposition on Leaf Functional Traits of Four Contrasting Tree Species in Northeast China

Plants ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1231
Author(s):  
Attaullah Khan ◽  
Jingjue Sun ◽  
Nowsherwan Zarif ◽  
Kashif Khan ◽  
Muhammad Atif Jamil ◽  
...  
Keyword(s):  
Biomass Allocation ◽  
Northeast China ◽  
Leaf Traits ◽  
N Deposition ◽  
Leaf Thickness ◽  
Pinus Koraiensis ◽  
Leaf Mass Per Area ◽  
Leaf Mass ◽  
Allocation Patterns ◽  
Leaf Density

Northeast China is persistently affected by heavy nitrogen (N) deposition. Studying the induced variation in leaf traits is pivotal to develop an understanding of the adaptive plasticity of affected species. This study thus assesses effects of increased N deposition on leaf morphological and anatomical traits and their correlation among and with biomass allocation patterns. A factorial experiment was conducted utilizing seedlings of two gymnosperms (Larix gmelinii, Pinus koraiensis) and two angiosperms (Fraxinus mandshurica, Tilia amurensis). Leaf mass per area and leaf density decreased and leaf thickness increased under high N deposition but trait interrelations remained stable. In gymnosperms, leaf mass per area was correlated to both leaf thickness and area, while being correlated to leaf density only in angiosperms. Epidermis, mesophyll thickness, conduit and vascular bundle diameter increased. Despite the differences in taxonomic groups and leaf habits, the common patterns of variation suggest that a certain degree of convergence exists between the species’ reaction towards N deposition. However, stomata pore length increased in angiosperms, and decreased in gymnosperms under N deposition. Furthermore, biomass and leaf mass fraction were correlated to leaf traits in gymnosperms only, suggesting a differential coordination of leaf traits and biomass allocation patterns under high N deposition per taxonomic group.

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