Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review.

Accurate measuring, mapping, and monitoring of mangrove forests support the sustainable management of mangrove blue carbon in the Asia-Pacific. Remote sensing coupled with modeling can efficiently and accurately estimate mangrove blue carbon stocks at larger spatiotemporal extents. This study aimed to identify trends in remote sensing/modeling employed in estimating mangrove blue carbon, attributes/variations in mangrove carbon sequestration estimated using remote sensing, and to compile research gaps and opportunities, followed by providing recommendations for future research. Using a systematic literature review approach, we reviewed 105 remote sensing-based peer-reviewed articles (1990 - June 2023). Despite their high mangrove extent, there was a paucity of studies from Myanmar, Bangladesh, and Papua New Guinea. The most frequently used sensor was Sentinel-2 MSI, accounting for 14.5 % of overall usage, followed by Landsat 8 OLI (11.5 %), ALOS-2 PALSAR-2 (7.3 %), ALOS PALSAR (7.2 %), Landsat 7 ETM+ (6.1 %), Sentinel-1 (6.7 %), Landsat 5 TM (5.5 %), SRTM DEM (5.5 %), and UAV-LiDAR (4.8 %). Although parametric methods like linear regression remain the most widely used, machine learning regression models such as Random Forest (RF) and eXtreme Gradient Boost (XGB) have become popular in recent years and have shown good accuracy. Among a variety of attributes estimated, below-ground mangrove blue carbon and the valuation of carbon stock were less studied. The variation in carbon sequestration potential as a result of location, species, and forest type was widely studied. To improve the accuracy of blue carbon measurements, standardized/coordinated and innovative methodologies accompanied by credible information and actionable data should be carried out. Technical monitoring (every 2-5 years) enhanced by remote sensing can provide accurate and precise data for sustainable mangrove management while opening ventures for voluntary carbon markets to benefit the environment and local livelihood in developing countries in the Asia-Pacific region.

The status of forest carbon markets in Latin America.

Tropical rainforests of Latin America (LATAM) are one of the world's largest carbon sinks, with substantial future carbon sequestration potential and contributing a major proportion of the global supply of forest carbon credits. LATAM is poised to contribute predominantly towards high-quality forest carbon offset projects designed to reduce emissions from deforestation and forest degradation, halt biodiversity loss, and provide equitable conservation benefits to people. Thus, carbon markets, including compliance carbon markets and voluntary carbon markets continue to expand in LATAM. However, the extent of the growth and status of forest carbon markets, pricing initiatives, stakeholders, amongst others, are yet to be explored and extensively reviewed for the entire LATAM region. Against this backdrop, we reviewed a total of 299 articles, including peer-reviewed and non-scientific gray literature sources, from January 2010 to March 2023. Herein, based on the extensive literature review, we present the results and provide perspectives classified into five categories: (i) the status and recent trends of forest carbon markets (ii) the interested parties and their role in the forest carbon markets, (iii) the measurement, reporting and verification (MRV) approaches and role of remote sensing, (iv) the challenges, and (v) the benefits, opportunities, future directions and recommendations to enhance forest carbon markets in LATAM. Despite the substantial challenges, better governance structures for forest carbon markets can increase the number, quality and integrity of projects and support the carbon sequestration capacity of the rainforests of LATAM. Due to the complex and extensive nature of forest carbon projects in LATAM, emerging technologies like remote sensing can enable scale and reduce technical barriers to MRV, if properly benchmarked. The future directions and recommendations provided are intended to improve upon the existing infrastructure and governance mechanisms, and encourage further participation from the public and private sectors in forest carbon markets in LATAM.

Previsual and Early Detection of Myrtle Rust on Rose Apple Using Indices Derived from Thermal Imagery and Visible-to-Short-Infrared Spectroscopy.

Myrtle rust, caused by the fungus Austropuccinia psidii, is a serious disease, which affects many Myrtaceae species. Commercial nurseries that propagate Myrtaceae species are prone to myrtle rust and require a reliable method that allows previsual and early detection of the disease. This study uses time-series thermal imagery and visible-to-short-infrared spectroscopy measurements acquired over 10 days from 81 rose apple plants (Syzygium jambos) that were either inoculated with myrtle rust or maintained disease-free. Using these data, the objectives were to (i) quantify the accuracy of models using thermal indices and narrowband hyperspectral indices (NBHI) for previsual and early detection of myrtle rust using data from older resistant green leaves and young susceptible red leaves and (ii) identify the most important NBHI and thermal indices for disease detection. Using predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (F1 score = 1.0; accuracy = 100%) distinguish control from infected plants previsually one day before symptoms appeared (1 DBS) and for all stages after early symptoms appeared. Compared with control plants, plants with myrtle rust had lower and more variable normalized canopy temperature, which was associated with higher stomatal conductance and transpiration. Using NBHI derived from green leaves, excellent previsual classification was achieved 3 DBS, 2 DBS, and 1 DBS (F1 score range = 0.89 to 0.94). The accurate characterization of myrtle rust during previsual and early stages of disease development suggests that a robust detection methodology could be developed within a nursery setting. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning.

Advances in remote sensing combined with the emergence of sophisticated methods for large-scale data analytics from the field of data science provide new methods to model complex interactions in biological systems. Using a data-driven philosophy, insights from experts are used to corroborate the results generated through analytical models instead of leading the model design. Following such an approach, this study outlines the development and implementation of a whole-of-forest phenotyping system that incorporates spatial estimates of productivity across a large plantation forest. In large-scale plantation forestry, improving the productivity and consistency of future forests is an important but challenging goal due to the multiple interactions between biotic and abiotic factors, the long breeding cycle, and the high variability of growing conditions. Forest phenotypic expression is highly affected by the interaction of environmental conditions and forest management but the understanding of this complex dynamics is incomplete. In this study, we collected an extensive set of 2.7 million observations composed of 62 variables describing climate, forest management, tree genetics, and fine-scale terrain information extracted from environmental surfaces, management records, and remotely sensed data. Using three machine learning methods, we compared models of forest productivity and evaluate the gain and Shapley values for interpreting the influence of categorical variables on the power of these methods to predict forest productivity at a landscape level. The most accurate model identified that the most important drivers of productivity were, in order of importance, genetics, environmental conditions, leaf area index, topology, and soil properties, thus describing the complex interactions of the forest. This approach demonstrates that new methods in remote sensing and data science enable powerful, landscape-level understanding of forest productivity. The phenotyping method developed here can be used to identify superior and inferior genotypes and estimate a productivity index for individual site. This approach can improve tree breeding and deployment of the right genetics to the right site in order to increase the overall productivity across planted forests.

Preprocessing Ground-Based Visible/Near Infrared Imaging Spectroscopy Data Affected by Smile Effects.

A data set of very high-resolution visible/near infrared hyperspectral images of young Pinus contorta trees was recorded to study the effects of herbicides on this invasive species. The camera was fixed on a frame while the potted trees were moved underneath on a conveyor belt. To account for changing illumination conditions, a white reference bar was included at the edge of each image line. Conventional preprocessing of the images, i.e., dividing measured values by values from the white reference bar in the same image line, failed and resulted in bad quality spectra with oscillation patterns that are most likely due to wavelength shifts across the sensor's field of view (smile effect). An additional hyperspectral data set of a Spectralon white reference panel could be used to characterize and correct the oscillations introduced by the division, resulting in a high quality spectra that document the effects of herbicides on the reflectance characteristics of coniferous trees. While the spectra of untreated trees remained constant over time, there were clear temporal changes in the spectra of trees treated with both herbicides. One herbicide worked within days, the other one within weeks. Ground-based imaging spectroscopy with meaningful preprocessing proved to be an appropriate tool for monitoring the effects of herbicides on potted plants.

Pd(II)-Catalyzed [4 + 2] Heterocyclization Sequence for Polyheterocycle Generation.

A new Pd(II)-catalyzed cascade sequence for the formation of polyheterocycles, from simple starting materials, is reported. The sequence is applicable to both indole and pyrrole substrates, and a range of substituents are tolerated. The reaction is thought to proceed by a Pd(II)-catalyzed C-H activated Heck reaction followed by a second Pd(II)-catalyzed aza-Wacker reaction with two Cu(II)-mediated Pd(0) turnovers per sequence. The sequence can be considered a formal [4 + 2] heterocyclization.

Phenotyping Whole Forests Will Help to Track Genetic Performance.

Phenotyping is the accurate and precise physical description of organisms. Accurate and quantitative phenotyping underpins the delivery of benefits from genetic improvement programs in agriculture. In forest trees, phenotyping at an equivalent precision has been impossible because trees and forests are large, long-lived, and highly variable. These facts have restricted the delivery of genetic gains in forestry compared to other agricultural sectors. We describe a landscape-scale phenotyping platform that integrates remote sensing, spatial information systems, and genomics to facilitate the delivery of greater gains enabling forestry to catch up with other sectors. Combining remote sensing at a range of spatial and temporal scales with genomics will ultimately impact on tree breeding globally.

Palladium(II) Catalyzed C-H Functionalization Cascades for the Diastereoselective Synthesis of Polyheterocycles.

C-H activation offers huge potential in the generation of complex structures from simple starting materials. Herein we report the development of a highly diastereoselective palladium(II) catalyzed C-H functionalization cascade to produce novel, unsaturated polyheterocycles from simple diene-tethered heterocyclic starting materials. The reaction is applicable to both indole and pyrrole based substrates and tolerates a wide range of functional group substitutions around the heteroaromatic core. The polyheterocyclic products are formed as single diastereoisomers, with two new stereocenters formed in a single step.

Modelling the influence of predicted future climate change on the risk of wind damage within New Zealand's planted forests.

Wind is the major abiotic disturbance in New Zealand's planted forests, but little is known about how the risk of wind damage may be affected by future climate change. We linked a mechanistic wind damage model (ForestGALES) to an empirical growth model for radiata pine (Pinus radiata D. Don) and a process-based growth model (cenw) to predict the risk of wind damage under different future emissions scenarios and assumptions about the future wind climate. The cenw model was used to estimate site productivity for constant CO2 concentration at 1990 values and for assumed increases in CO2 concentration from current values to those expected during 2040 and 2090 under the B1 (low), A1B (mid-range) and A2 (high) emission scenarios. Stand development was modelled for different levels of site productivity, contrasting silvicultural regimes and sites across New Zealand. The risk of wind damage was predicted for each regime and emission scenario combination using the ForestGALES model. The sensitivity to changes in the intensity of the future wind climate was also examined. Results showed that increased tree growth rates under the different emissions scenarios had the greatest impact on the risk of wind damage. The increase in risk was greatest for stands growing at high stand density under the A2 emissions scenario with increased CO2 concentration. The increased productivity under this scenario resulted in increased tree height, without a corresponding increase in diameter, leading to more slender trees that were predicted to be at greater risk from wind damage. The risk of wind damage was further increased by the modest increases in the extreme wind climate that are predicted to occur. These results have implications for the development of silvicultural regimes that are resilient to climate change and also indicate that future productivity gains may be offset by greater losses from disturbances.

Linking climate suitability, spread rates and host-impact when estimating the potential costs of invasive pests.

Biosecurity agencies need robust bioeconomic tools to help inform policy and allocate scarce management resources. They need to estimate the potential for each invasive alien species (IAS) to create negative impacts, so that relative and absolute comparisons can be made. Using pine processionary moth (Thaumetopoea pityocampa sensu lato) as an example, these needs were met by combining species niche modelling, dispersal modelling, host impact and economic modelling. Within its native range (the Mediterranean Basin and adjacent areas), T. pityocampa causes significant defoliation of pines and serious urticating injuries to humans. Such severe impacts overseas have fuelled concerns about its potential impacts, should it be introduced to New Zealand. A stochastic bioeconomic model was used to estimate the impact of PPM invasion in terms of pine production value lost due to a hypothetical invasion of New Zealand by T. pityocampa. The bioeconomic model combines a semi-mechanistic niche model to develop a climate-related damage function, a climate-related forest growth model, and a stochastic spread model to estimate the present value (PV) of an invasion. Simulated invasions indicate that Thaumetopoea pityocampa could reduce New Zealand's merchantable and total pine stem volume production by 30%, reducing forest production by between NZ$1,550 M to NZ$2,560 M if left untreated. Where T. pityocampa is controlled using aerial application of an insecticide, projected losses in PV were reduced, but still significant (NZ$30 M to NZ$2,210 M). The PV estimates were more sensitive to the efficacy of the spray program than the potential rate of spread of the moth. Our novel bioeconomic method provides a refined means of estimating potential impacts of invasive alien species, taking into account climatic effects on asset values, the potential for pest impacts, and pest spread rates.

Stand variation in Pinus radiata and its relationship with allometric scaling and critical buckling height.

BACKGROUND AND AIMS: Allometric relationships and the determination of critical buckling heights have been examined for Pinus radiata in the past. However, how they relate to more mature Pinus radiata exhibiting a wide range of stem diameters, slenderness and modulus of elasticity (E) at operationally used stand densities is largely unknown. The aim of this study was to examine the relationship between Pinus radiata stand structure variables and allometric scaling and critical buckling height. METHODS: Utilizing a Pinus radiata Nelder trial with stand density and genetic breed as variables, critical buckling height was calculated whilst reduced major axis regression was used to determine scaling exponents between critical height (Hcrit), actual height (H), ground line diameter (D), slenderness (S), density-specific stiffness (E/ρ) and modulus of elasticity (E). KEY RESULTS: Critical buckling height was highly responsive to decreasing diameter and increasing slenderness. Safety factors in this study were typically considerably lower than previously reported margins in other species. As density-specific stiffness scaled negatively with diameter, the exponent of 0·55 between critical height and diameter did not meet the assumed value of 0·67 under constant density-specific stiffness. E scaled positively with stem slenderness to the power of 0·78. CONCLUSIONS: The findings suggest that within species density-specific stiffness variation may influence critical height and the scaling exponent between critical height and diameter, which is considered so important in assumptions regarding allometric relationships.

The influence of nitrogen and phosphorus supply and genotype on mesophyll conductance limitations to photosynthesis in Pinus radiata.

Mesophyll conductance, g(m), may pose significant limitations to photosynthesis and may be differentially affected by nutrition and genotype in Pinus radiata D. Don. Simultaneous measurements of gas exchange and chlorophyll fluorescence were made to determine g(m), using the constant J method (Harley, P.C., F. Loreto, G. Di Marco and T.D. Sharkey. 1992. Theoretical considerations when estimating the mesophyll conductance to CO(2) flux by analysis of the response of photosynthesis to CO(2). Plant Physiol. 98:1429-1436), in a fast- and a slow-growing clone of P. radiata grown in a greenhouse with a factorial combination of nitrogen (N) and phosphorus (P) supply. Values of g(m) increased linearly with the rate of photosynthesis at saturating irradiance and ambient CO(2) concentration, A(sat) (g(m) = 0.020A(sat), r(2) = 0.25, P < 0.001) and with stomatal conductance to CO(2) transfer, g(s) (g(m) = 1.16g(s), r(2) = 0.14, P < 0.001). Values of g(m) were greater than those of stomatal conductance, g(s), and the ratio (g(m)/g(s)) was not influenced by single or combined N and P additions or clone with a mean (+/-SE) value of 1.22 +/- 0.06. Relative limitations to mesophyll conductance, L(m) (16%) to photosynthesis, were generally greater than those imposed by stomata, L(s) (13%). The mean (+/-SE) CO(2) concentration in the intercellular air spaces (C(i)) was 53 +/- 3 mumol mol(-1) lower than that in the atmosphere (C(a)). Mean (+/-SE) CO(2) concentration in the chloroplasts (C(c)) was 48 +/- 2 mumol mol(-1) lower than C(i). Values of L(s), L(m) and CO(2) diffusion gradients posed by g(s) (C(a) - C(i)) and g(m) (C(i) - C(c)) did not significantly differ with nutrient supply or clone. Mean values of V(cmax) and J(max) calculated on a C(c) basis were 15.4% and 3.1% greater than those calculated on a C(i) basis, which translated into different slopes of the J(max)/V(cmax) relationship (C(c) basis: J(max) = 2.11V(cmax), r(2) = 0.88, P < 0.001; C(i) basis: J(max) = 2.43V(cmax), r(2) = 0.86, P < 0.001). These results will be useful for correcting estimates of V(cmax) and J(max) used to characterize the biochemical properties of photosynthesis for P. radiata.

The influence of N and P supply and genotype on carbon flux and partitioning in potted Pinus radiata plants.

Carbon (C) flux and partitioning responses of Pinus radiata (D. Don) clones to a factorial combination of nitrogen (N) and phosphorus (P) supply were estimated in small trees growing in a greenhouse over 44 weeks. Our objective was to use a C budget approach at the plant level to examine how a factorial combination of N and P additions and genotype modify gross primary production (GPP), net primary production (NPP), absolute C fluxes apportioned to aboveground net primary production (ANPP), aboveground plant respiration (APR), total belowground carbon flux (TBCF) and the partitioning of GPP to ANPP, APR and TBCF. Single N or P additions increased plant NPP and GPP similarly, but their combined effects exceeded those of their individual contributions. Nitrogen and to a lesser extent P additions enhanced carbon-use efficiency (CUE, NPP:GPP) and C partitioning to ANPP at the expense of TBCF. The fraction of GPP partitioned to APR was invariant to N or P additions. The ratio of soil respiration (FS) to TBCF was significantly greater in the low-N low-P addition treatment (61%) than in those treatments with single or combined N and P additions (49%). The slowest growing clone partitioned a significantly smaller fraction of GPP to ANPP (29%) than one of the faster-growing genotypes (33%). This research provides insight into how N and P regulate the C fluxes and partitioning in individual plants. Our results contribute to explaining clonal variation in aboveground growth rates and suggest that greater gains in CUE and partitioning to ANPP occur with addition of N rather than P supply.

The impact of defoliation on nitrogen translocation patterns in the woody invasive plant, Buddleia davidii.

The influence of defoliation on nitrogen (N) re-translocation and the source for N remobilisation by the invasive shrub, Buddleia davidii Franch. (buddleia) was determined. Eighty plants were grown over two growing seasons, and half were repeatedly defoliated by removing 66% of their leaf area. During the second season, the N supply was labelled with 15N (10 atom% enrichment), to distinguish the use of stored N (unlabelled) from N taken up by roots (labelled) for growth. Defoliation significantly decreased root (39%) and total biomass (26%). Old leaves were the main source of N for remobilisation which was accelerated and increased (by 50% in the second season) in response to defoliation. In spring, root uptake of N increased by 57% in defoliated plants. Thus, defoliation induced changes in N remobilisation and uptake as compensatory growth increased the demand for N. Continued leaf removal decreased the pool of stored N and caused a significant decline in biomass production, especially in roots (39%) and flowers (31%). This has important implications for the efficacy of defoliation as a control measure, as smaller roots suggest a reduced capacity for uptake of nutrients from the soil and reduced flower production may assist in reducing the invasive spread of the species. These findings clearly show that, although the success of B. davidii is associated, in part, with efficient remobilisation of N from storage, this advantage can be overcome by continued defoliation.

Partititioning concurrent influences of nitrogen and phosphorus supply on photosynthetic model parameters of Pinus radiata.

Responses of photosynthesis (A) to intercellular CO(2) concentration (C(i)) were measured in a fast- and a slow-growing clone of Pinus radiata D. Don cultivated in a greenhouse with a factorial combination of nitrogen and phosphorus supply. Stomatal limitations scaled with nitrogen and phosphorus supply as a fixed proportion of the light-saturated photosynthetic rate (18.5%) independent of clone. Photosynthetic rates at ambient CO(2) concentration were mainly in the V(cmax)-limited portion of the CO(2) response curve at low-nitrogen supply and at the transition between V(cmax) and J(max) at high-nitrogen supply. Nutrient limitations to photosynthesis were partitioned based on the ratio of foliage nitrogen to phosphorus expressed on a leaf area basis (N(a)/P(a)), by minimizing the mean square error of segmented linear models relating photosynthetic parameters (V(cmax), J(max), T(p)) to foliar nitrogen and phosphorus concentrations. A value of N(a)/P(a) equal to 23 (mole basis) was identified as the threshold separating nitrogen (N(a)/P(a) < or = 23) from phosphorus (N(a)/P(a) > 23) limitations independent of clones. On an area basis, there were significant positive linear relationships between the parameters, V(cmax), J(max), T(p) and N(a) and P(a), but only the relationships between T(p) and N(a) and P(a) differed significantly between clones. These findings suggest that, in genotypes with contrasting growth, the responses of V(cmax) and J(max) to nutrient limitation are equivalent. The relationships between the parameters V(cmax), J(max), T(p) and foliage nutrient concentration on a mass basis were unaffected by clone, because the slow-growing clone had a significantly greater leaf area to mass ratio than the fast-growing clone. These results may be useful in discriminating nitrogen-limited photosynthesis from phosphorus-limited photosynthesis.

Modelling environmental variation in Young's modulus for Pinus radiata and implications for determination of critical buckling height.

BACKGROUND AND AIMS: Although density-specific stiffness, E/rho, (where E is Young's modulus and rho is wood density) is often assumed constant by the elastic similarity model, and in determination of critical buckling height (H(crit)), few studies have tested this assumption within species. Here this assumption is tested for Pinus radiata growing across an environmental gradient, and theory is combined with data to develop a model of Young's modulus. METHODS: Analyses use an extensive series of environmental plots covering the range of climatic and edaphic conditions over which P. radiata is grown in New Zealand. Reduced major axis regression was used to determine scaling exponents between log-log plots of H(crit) vs. groundline diameter (D), and E/rho vs. D. Path analysis was used to identify significant direct and indirect (through stem slenderness) edaphic and climatic influences on E. KEY RESULTS: Density-specific stiffness exhibited 3-fold variation. As E/rho scaled positively with D, the exponent of 0.95 between H(crit) and D exceeded the assumed value of 0.67 under constant E/rho. The final path analysis model included mean air temperature in early autumn (T(aut)) and slenderness as significant (P < 0.05) positive direct influences on E. Tree leaf area index and T(aut) were indirectly associated with E through their significant (P < 0.05) positive direct relationship with stem slenderness. Young's modulus was most sensitive to T(aut), followed by stem slenderness then leaf area index, and the final model explained 76 % of the variance in E. CONCLUSIONS: The findings suggest that within species E/rho variation may influence H(crit) and the scaling exponent between D and H(crit) so important in assumptions regarding allometric relationships. The model presented may provide a useful means of determining variation in E, E/rho and H(crit) across environmental gradients.