Percentage Of Trees Research Articles

A productive friction: Leveraging misalignments between local ecological knowledge and remotely sensed imagery for forest conservation planning

AbstractEarth's forests are continually monitored by both the satellite record and the lived experiences of nearly 2 billion forest‐proximate peoples. Generally, the satellite record summarizes production estimates, such as percent tree cover, at regular, relatively coarse scales. Conversely, local perceptions tend to capture changes at irregular and very fine scales. While the utility of both of these sources of information has been widely demonstrated in isolation, little work has explored in what contexts they are expected to correlate or deviate, or how they might be quantitatively integrated. Here, we collect gridded information on community perceived and remotely sensed mangrove cover change across 719 0.5‐km grids in Pemba Island, Tanzania. We reveal variation in the association between these two data sources across different wards (shehia) and explore the reasons for this variation using interviews and direct observation. We find that shehia with the greatest alignment between perceived and remotely sensed mangrove change tended to have little planting or natural regeneration of mangrove propagules and large areas of complete cover loss. Alternatively, in shehia with the lowest alignment, we find high levels of natural and/or human‐assisted mangrove recolonization and selective harvesting of individual trees and branches. These findings indicate that the alignment between local knowledge and satellite observations of mangrove cover change systematically increases with the scale of change in this system. Finally, we demonstrate a practical workflow for quantitatively leveraging these misalignments by optimizing across both data sources to identify restoration priority areas.

Abstract C032: Take Charge during Treatment: A randomized exercise trial for breast cancer patients

Abstract Introduction. Breast cancer (BC) is the most frequently diagnosed cancer with over 4 million survivors living in the United States. Recent treatment (Tx) advances such as in targeted (e.g. anti-HER2), systemic (e.g. anthracyclines), or combined therapies have supported the increased survival rates but continue to present adverse effects. Cardiotoxicity, which may be induced during or after Tx, is the second highest cause of mortality in BC survivors and also negatively impacts quality of life (QoL). Black/African American (B/AA) patients have higher rates of this effect along with a higher incidence of aggressive BC types. Limited research has targeted vascular function as an area of focus in the cardio-oncology field. Methods. The Discovery and Elimination of Cardio-Oncology Disparities Toward Equity in the Heartland Center is examining how anti-cancer treatments impact vascular function, the potential mitigating effects of exercise and how socioenvironmental factors moderate these effects. The Take Charge during Treatment randomized trial within this Center is testing the feasibility and acceptability of an exercise during treatment program for B/AA and non-Hispanic White (NHW) women newly diagnosed with BC. The study also examines and compares the effects of exercise on VO2 peak and QoL in participants. The 16-week intervention promotes adherence to the American College of Sports Medicine exercise guidelines for cancer survivors and is guided by a certified exercise trainer. To evaluate the intervention, patient's complete surveys including the FACT-B for QoL, a VO2 peak assessment, physical assessments, and anthropometry measurements at baseline (before BC Tx), after the 16-week program, and at a 12-month timepoint. Socioenvironmental factors are examined using participant addresses. Results. To date, 1330 patients were assessed for eligibility, 60 consented, and 49 enrolled and randomized. Herein, we present data on 45 of the 49 (35 NHW, 10 B/AA). The mean age of participants is 52 years (SD=12), most were diagnosed with Stage I or II BC, and they have varied education and income levels. Baseline (pre-treatment) differences are noted by racial group for QoL (Total score – 88 (SD=9) for NHW vs 74 (SD=29) for B/AA; Physical function – 25 (SD=3) for NHW vs 22 (SD=5) for B/AA; Functional – 21 (SD=4) for NHW vs 18 (SD=8) for B/AA), VO2 peak (22 (SD=5) for NHW vs 17 (SD=6) for B/AA), and obesity (NHW – 30%, B/AA – 70%). Cognitive function, pain interference, social isolation and perceived stress were similar across groups. B/AA women were more likely to report living in a highly redlined area (50%) compared to NHW women (3%), with lower percent tree canopy (13% vs 24%) and lower perceived social cohesion (15 vs 19). Reports of discrimination and resilience were similar across groups. Conclusions. Preliminary baseline results suggest that B/AA women enter BC Tx more vulnerable to developing QoL challenges and cardiotoxicity. Neighborhood environmental factors will be further examined to understand the association with adverse outcomes. Citation Format: Estefania Alonso, Melinda Stolley, Kirsten Beyer, Alison Kriegel, Shane Phillips, Matt Durand, Michael E. Widlansky, Amanda L. Kong, Kent Hoskins, Andreas Beyer. Take Charge during Treatment: A randomized exercise trial for breast cancer patients [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C032.

Long-term riparian forest loss around streams, lakes, and wetlands in ecologically diverse managed and unmanaged landscapes

Riparian forests are at risk from the cumulative effects of human and natural disturbance. We assessed the magnitude and rate of forest loss adjacent to freshwater features in the province of British Columbia, Canada, from 2000 to 2021. We used Global Forest Change data to quantify percent tree cover lost annually across six ecologically and topographically diverse ecosections containing 361,370 streams, lakes, and wetlands. We stratified the aquatic features into large and small size classes within and outside parks. We hypothesized that cumulative forest loss would be greater outside of parks as a consequence of human-caused and natural disturbance. Also, we hypothesized greater rates of forest loss around small aquatic features that generally receive less protection outside of parks. Loss of riparian forest was consistently greater outside of parks (median losses of 3.20% versus 1.29%), and adjacent to small streams (median = 3.31%) and wetlands regardless of size (median = 3.10%). Rates of cumulative riparian forest loss were nonlinear, increasing substantially in 2013. Though the annual riparian forest losses were small, the cumulative losses from 2000 to 2021 were substantial. We anticipate the continued loss of riparian forest as climate changes and natural disturbances intensify. We recommend further investigation of the implications of these observed trends for ecosystem values that are found within or adjacent to riparian forest across managed and unmanaged landscapes.

Abstract P115: Urban Greenspace and All-Cause, Cardiovascular Disease-Specific, and Breast Cancer-Specific Mortality Among Older Women With Breast Cancer in the United States: Regional Variations Within the Surveillance, Epidemiology, and End Results and Medicare Linked Database

Background: Cardiovascular disease (CVD) and cancer are leading causes of morbidity and mortality in the US. Cancer survivors are at increased risk for adverse CVD outcomes due to multiple factors, including cardiotoxicity. Research suggests that greenspace contributes to better health outcomes, including CVD and breast cancer (BC). Objective: To explore relationships between urban greenspace and all-cause (ACM), CVD-specific (CVDSM), and BC-specific (BCSM) mortality and explore regional differences among older women diagnosed with BC in the US SEER-Medicare database. Methods: The 2011 national landcover database was linked to the 2010 census tracts and the 2010-2017 SEER-Medicare BC cohort. Women were included if they were 66+ years at diagnosis, had invasive BC, enrolled in Medicare Part A and Part B for 12 months before diagnosis, and had a census tract match for greenspace. Independent variable ‘greenspace’ was census tract percent tree canopy quartiles. Outcomes were all-cause, CVD, and BC-specific survival. Censoring was determined by the end of the study date, 12/31/2018. Covariates included age and comorbidity at BC diagnosis, race/ethnicity, dual enrollment in both Medicare and Medicaid eligibility, neighborhood social vulnerability, and tumor stage. Results: Regional variations in association between greenspace and BC survival were observed (Table 1). Greenspace is protective for ACM and BCSM in California, Washington, New Jersey, and Michigan, while no significant associations were observed in Georgia or Louisiana. For CVDSM, greenspace is protective in CA, while no significant differences were observed in Washington, Louisiana, and Michigan. An inverse relationship is observed for CVDSM in Georgia and New Jersey. Discussion: Our findings warrant more studies to investigate why greenspace is protective in some areas (i.e., CA, NJ, MI) or hazardous in other areas (i.e., GA). There is a need to identify critical factors and pathways contributing to these greenspace-survival differences.

The Economic Effects of Stormwater Best Management Practices (BMPs) on Housing Sale Prices in Washington, D.C.

This study aims to investigate the economic effects of stormwater best management practices (BMPs) on housing sale prices in Washington, D.C., BMPs play a significant role in mitigating multiple threats, such as water pollution, soil erosion, and property damage. While studies on the economic value of BMPs were limited, literature addresses that housing sale prices can be affected by nearby stormwater BMPs. This study addresses the following research questions: Do stormwater BMPs positively impact housing sale prices? How do proximity and number of structural BMPs affect the housing sale prices? We used the hedonic pricing method by applying multiple linear regression models to determine whether a set of independent variables significantly improved the models. Our primary findings indicate that BMPs have positive, negative, or no effects on housing sale prices. The proximity of BMPs inside of parks increased housing sale prices in all buffers. In contrast, the proximity of BMPs outside of parks and impervious roads decreased housing sale prices in all buffers. Percent tree canopy coverage negatively linked to a 50 m buffer and had no relationship with other buffers on housing sale prices. This study implies that BMPs impact housing prices and can be improved by landscape architects, policymakers, and stakeholders.

Testing temporal transferability of remote sensing models for large area monitoring

Applying remote sensing models outside the temporal range of their training data, referred to as temporal model transfer, has become common practice for large area monitoring projects that extrapolate models for hindcasting or forecasting to time periods lacking reference data. However, the development of appropriate validation methods for temporal transfer has lagged behind its rapid adoption. Breaking temporal transfer's assumption of temporal consistency in both remote sensing and reference data and their relationship to each other could lead to biased pixel-level predictions and small area estimators, compromising the operational validity of large area monitoring products. Few studies using temporal transfer have evaluated its effects on model accuracy at the pixel/plot level, and the propensity for biased small area estimators and trends resulting from temporal transfer remains unexplored. We present a framework for evaluating temporal transferability by combining temporal cross-validation with a multiscale map assessment to aid in identifying where and when biased model predictions could scale to small area estimates and affect predicted trends.This validation framework is demonstrated in a case study of annual percent tree canopy cover mapping in Michigan. We tested and compared temporal transferability of random forest models of canopy cover derived from 2010 to 2016 systematic dot-grid photo-interpretations at Forest Inventory and Analysis plots with Landsat spectral indices fit with the LandTrendr temporal segmentation algorithm serving as the primary predictor variables. The temporal cross-validation error (mean RMSE = 13.9% cover) was higher than the common validation approach of considering all time periods of testing data together (RMSE = 13.6% cover) and lower than models trained and tested within the same year (mean RMSE = 14.2% cover). However, the bias of model predictions and small area estimators for individual years was higher with temporal transfer models than when applying models within the same year as their training data. We also evaluated how training models using different temporal subsets and with and without LandTrendr fitting affected predictions of Michigan's 1984–2020 predicted annual mean cover. The mean cover from LandTrendr-based models followed expected and consistent trends and had less difference between models trained with different temporal subsets (max difference = 5.8% cover). While those from Landsat had high interannual variations and greater difference between temporal models (max difference = 11.2% cover). The results of this case study demonstrate that evaluation of temporal transferability is necessary for establishing the operational validity of large area monitoring products, even when using time series algorithms that improve temporal consistency.

The effects of 2-D and 3-D urban landscape metrics on mean radiant temperature in hot-arid Phoenix and Tempe, Arizona, USA

The composition and configuration of the built environment affect intra-urban heat variability and human thermal exposure. We investigated how 2-D and 3-D building and vegetation characteristics, represented by various landscape metrics, affect the mean radiant temperature (MRT) distribution in Phoenix and Tempe, Arizona, USA, to determine which urban form characteristics are most important for minimizing thermal exposure. For a hot-dry summer day with low wind speed and a maximum air temperature of 43.3 °C, the relationship between 1-m resolution MRT data and seventeen 2-D and 3-D landscape metrics were analyzed at the Census block (micro) and urban (macro) scale. The landscape metrics were calculated with FragStat from a Digital Surface Model, a 3-D point cloud obtained from high-resolution (0.5-m) USGS LiDAR data, and a 1-m resolution land use/land cover map classified from the 2015 National Agriculture Imagery Program (NAIP) data. Hourly MRT for June 27, 2012, was simulated using the SOlar LongWave Environmental Irradiance Geometry (SOLWEIG) model. After testing for autocorrelation in hourly MRT using Moran's I, the relationship between landscape metrics and MRT outcomes was analyzed using correlation coefficients and multiple linear regression. The best predictive power was achieved using 2-D and 3-D metrics together, with an explanation MRT of 86 % (8:00 h) to 96 % (12:00 h). The five most important factors were 3-D vegetation height, 2-D percent tree surface cover, 3-D building height, 2-D building edge density, and the 3-D vegetation height coefficient of variation. Results show that MRT is driven by the composition and configuration of 2-D and 3-D urban features. The horizontal arrangement impacts MRT through varying land cover, and the vertical extent influences shade patterns, with more complex urban forms providing more shade and lowering MRT. Findings advance our understanding of how urban design can reduce thermal exposure on hot days using passive cooling strategies that rely on changes in the configuration and composition of landscapes.

Fire, Rain and CO2: Potential Drivers of Tropical Savanna Vegetation Change, with Implications for Carbon Crediting

A global trend of increasing tree cover in savannas has been observed and ascribed to a range of possible causes, including CO2 levels, changing rainfall and fire frequency. We tested these explanations in the Australian tropical savanna, taking 96 savanna ‘cool burning’ projects from Australia’s emissions offset scheme as case studies. We obtained readings of tree cover and explanatory variables from published remote sensing or spatial data sources. These were analysed using time-series linear regression to obtain coefficients for the influence of severe fire occurrence, annual rainfall and prior percentage tree cover. Although statistically significant coefficients for the key variables were found in only half (severe fire) or one quarter (rainfall) of the individual project models, when comparing all the model coefficients across the rainfall gradient, ecologically coherent explanations emerge. No residual trend was observed, suggesting rising CO2 levels have not influenced tree cover over the study period. Our approach models tree cover change by separating ecological drivers from human-controlled factors such as fire management. This is an essential design feature of national emissions inventories and emissions offsets programs, where crediting must be additional to the expected baseline, and arise from human activity.

Mapping landscape ecological patterns using numeric and categorical maps.

The reciprocal relationships between ecological process and landscape pattern are fundamental to landscape ecology. Landscape ecologists traditionally use raster maps portraying classified features such as land use or land cover categories, and metrics suggested by the patch-corridor-matrix conceptual model of pattern. Less attention has been given to the landscape gradient conceptual model and raster maps portraying numeric features such as greenness or percent vegetation cover. We introduce the open-source tool GraySpatCon to calculate and map a variety of landscape pattern metrics from both conceptual models using either categorical or numeric maps. The 51 metrics, drawn mostly from the landscape ecology and image processing literatures, are calculated from the frequencies of input pixel values and/or the pixel value adjacencies in an analysis region. GraySpatCon conducts either a moving window analysis which produces a continuous map of a pattern metric, or a global analysis which produces a single metric value. We describe an implementation in the GuidosToolbox desktop application which allows novice users to interactively explore GraySpatCon functionality. In the R desktop environment, we demonstrate several metrics using an example map of percent tree cover and illustrate a multi-scale moving window analysis to identify scale domains. Comparisons of computational efficiency indicate a substantial GraySpatCon advantage over related software in the R environment.

Association of Redlining and Natural Environment with Depressive Symptoms in Women in the Sister Study.

Improving mental health is recognized as an important factor for achieving global development goals. Despite strong evidence that neighborhood greenery promotes better mental health, there are environmental justice concerns over the distribution of neighborhood greenery. Underlying these concerns are present-day consequences of historical discriminatory financial investment practices, such as redlining which was established by the U.S. Federal Home Owners' Loan Corporation (HOLC) in the 1930s. The impacts of redlining on environmental and health disparities have been researched extensively. However, the influences of redlining on the associations between neighborhood environment and health outcomes have not been fully assessed. The aim of this study was to examine whether associations between residential tree cover and depressive symptoms vary across areas subject to HOLC practices. Depressive symptoms were defined by the 10-item Center for Epidemiologic Studies Depression Scale collected during the period 2008-2012 for 3,555 women in the Sister Study cohort residing in cities subject to HOLC practices across the United States. HOLC rating maps were obtained from the Mapping Inequality Project, University of Richmond, with neighborhoods graded as A (best for financial investment, green), B (still desirable, blue), C (declining, yellow), and D (hazardous, red-known as redlined). Tree cover within and from residences was estimated using 2011 U.S. Forest Service Percent Tree Canopy Cover. Mixed model using climate zone as the random effect was applied to evaluate the associations with adjustments for potential covariates. Analyses were stratified by HOLC grade. Tree cover was significantly higher in neighborhoods with better HOLC grades. A 10% increase in tree cover was associated with reduced odds of depressive symptoms for the full study population, with adjusted odds ratios (AORs) of 0.93 [95% confidence interval (CI): 0.88, 0.99], and 0.91 (0.85, 0.97) for and buffer, respectively. Across HOLC grades, the strongest associations were observed in redlined neighborhoods, with respective AORs of 0.72 (95% CI: 0.52, 0.99) and 0.63 (95% CI: 0.45, 0.90) for and buffer. Findings support a remediation strategy focused on neighborhood greenery that would address multiple public health priorities, including mental health and environmental justice. https://doi.org/10.1289/EHP12212.

Epigenetic architecture of Pseudotaxus chienii: Revealing the synergistic effects of climate and soil variables.

Whether conifers can withstand environmental changes especially temperature fluctuations has been controversial. Epigenetic analysis may provide new perspectives for solving the issue. Pseudotaxus chienii is an endangered gymnosperm species endemic to China. In this study, we have examined the genetic and epigenetic variations in its natural populations aiming to disentangle the synergistic effects of climate and soil on its population (epi)genetic differentiation by using amplified fragment length polymorphism (AFLP) and methylation-sensitive AFLP (MSAP) techniques. We identified 23 AFLP and 26, 7, and 5 MSAP outliers in P. chienii. Twenty-one of the putative adaptive AFLP loci were found associated with climate and/or soil variables including precipitation, temperature, K, Fe, Zn, and Cu, whereas 21, 7, and 4 MSAP outliers were significantly related to precipitation of wettest month (Bio13), precipitation driest of month (Bio14), percent tree cover (PTC), and soil Fe, Mn, and Cu compositions. Total precipitation and precipitation in the driest seasons were the most influential factors for genetic and epigenetic variation, respectively. In addition, a high full-methylation level and a strong correlation between genetic and epigenetic variation were detected in P. chienii. Climate is found of greater importance than soil in shaping adaptive (epi)genetic differentiation, and the synergistic effects of climate and climate-soil variables were also observed. The identified climate and soil variables should be considered when applying ex situ conservation.

Carbon Storage Estimation of Quercus aquifolioides Based on GEDI Spaceborne LiDAR Data and Landsat 9 Images in Shangri-La

The assessment of forest carbon storage plays a crucial role in forest management and ecosystem exploration, enabling the evaluation of forest quality, resources, carbon cycle and management. The Global Ecosystem Dynamics Investigation (GEDI) satellite provides a means to accurately measure these various forest vertical structure parameters by penetrating the forest canopy. However, the distribution of the footprint along the orbit track is heterogeneous and discontinuous, preventing the acquisition of spatially distributed carbon storage formation at the county level. Consequently, this study integrated GEDI and Landsat 9 data to estimate Quercus aquifolioides carbon storage in Shangri-La. By applying the Kriging interpolation to previously pretreated footprints, surface information from the GEDI L2B footprints was obtained. At the same time, Landsat 9 vegetation indices and band reflectance were extracted to analyze the correlation with the carbon storage of Quercus aquifolioides samples. Then, three methods (support vector machine, bagging, and random forest) were used to create a carbon storage estimation model for Shangri-La. The research results showed that (1) among the models for the selection of GEDI footprint parameters based on semi-variance, the optimal model of the digital_elevation_model was the spherical model, while the best model of percentage tree cover from the MODIS data (modis_treecover) and the foliage height diversity index (fhd_normal) was the exponential model. (2) Analyzing the thirty-three extracted independent variable factors correlated with the carbon storage of Quercus aquifolioides showed that the top five variables with the highest correlation were digital_elevation_model, modis_treecover, fhd_normal, DEM, and band 1 (B1). (3) After variable selection, the R2 = 0.82 and RMSE = 11.92 t/hm2 values of the Quercus aquifolioides carbon storage estimation model established via random forest were obtained, and its evaluation precision was superior to that of the support vector machine method and bagging regression. The carbon storage of Quercus aquifolioides was primarily in the range of 8.22~94.63 t/hm2, and the mean value was 42.44 t/hm2, while the total carbon storage was about 5,374,137.62 t. The findings from this paper illustrated the feasibility of obtaining carbon storage data on a county scale by combining GEDI LiDAR data with Landsat 9 optical data. The results also suggested a new perspective for combining GEDI L2B data with other remote sensing images to estimate other forest structure parameters.

Modelling tree canopy cover and evaluating the driving factors based on remotely sensed data and machine learning

Quantifying urban tree cover is important to ensure sustainable urban ecosystem. This study calculates urban percent tree cover (PTC) for Bursa city, Turkey from Sentinel-2 data and evaluates the driving factors of PTC using an Artificial Neural Network-Multi Layer Perception (ANN-MLP) approach. For the PTC calculation, a Regression Tree (RT) analysis was performed using several vegetation indices (NDVI, LAI, fCOVER, MSAVI2, and MCARI) to improve accuracy. Socio-economic, topographic, and biophysical variables were incorporated into the ANN-MLP approach to evaluate the factors that drive urban PTC. A PTC prediction map was generated with an accuracy of 0.95 and a coefficient of determination of 0.87. The ANN-MLP training process yielded a correlation coefficient value of 0.71 and an R-square of 0.82 was achieved between the predicted ANN-MLP and observed tree cover maps. A priority tree cover map was generated considering statistical relationships between the factors and the ANN-MLP prediction map in addition to visual interpretations at the urban scale. Results demonstrate that, unlike other urban forms, PTC has a statistically negative relationship with the gross dwelling density (R2 =0.31). Topographic variables including slope and DEM were positively correlated with PTC with the R2 value of 0.80 and 0.72 respectively. The integration of remote sensing data with vegetation indices and driving factors yielded accurate prediction for identifying and evaluating the variability in the urban PTC.

Varying effects of tree cover on relationships between satellite-observed vegetation greenup date and spring temperature across Eurasian boreal forests

Earth observation satellites have facilitated the quantification of how vegetation phenology responds to climate warming on large scales. However, satellite image pixels may contain a mixture of multiple vegetation types or species with diverse phenological responses to climate variability. It is unclear how these mixed pixels affect the statistical relationships between satellite-derived vegetation phenology and climate factors. Here, we aim to investigate the impacts of percent tree cover (PTC), a measure of mixed pixel, on the statistical relationships between satellite-derived vegetation greenup date (GUD) and spring air temperature across Eurasian boreal forests at a 0.05° spatial resolution. We estimated GUD using Moderate Resolution Imaging Spectroradiometer (MODIS) time series data. The responses of GUD to interannual variation in spring temperature (April to May) during 2001–2020 were characterized by correlation coefficient (RTAM) and sensitivity (STAM). We then evaluated the local impacts of PTC on spatial variations in RTAM and STAM using partial correlation analysis through spatial moving windows. Our results indicate that, for most areas, forests with higher PTC were associated with stronger RTAM and STAM. Moreover, PTC had stronger local impacts on RTAM and STAM than mean annual temperature and temperature seasonality for 37.3% and 27.4% of the moving windows, respectively. These impacts were spatially varying and different among forest types. Specifically, deciduous broadleaf forests and deciduous needleleaf forests tend to have a higher proportion of these impacts compared to other forest types. Our findings demonstrate the nonnegligible effects of PTC on the statistical responses of GUD to temperature variability at coarse spatial resolution (0.05°) across Eurasian boreal forests.

Satisfaction with urban trees associates with tree canopy cover and tree visibility around the home

Many world cities want to expand the number of urban trees. How this expansion occurs should consider what people expect from trees based on how they experience and perceive these trees. Therefore, we need a better understanding of how people perceptually respond to urban tree abundance. This research examined whether people’s satisfaction with urban trees and satisfaction with the management of those trees were related to objective measures of greenery such as the Normalized Difference Vegetation Index (NDVI), percent tree canopy cover, and the Viewshed Greenness Visibility Index (VGVI) for trees. We used a demographically and geographically representative survey of 223 residents in Toronto, Canada, and calculated NDVI, canopy cover, and VGVI at three neighbourhood sizes. We analysed the data using generalized linear regression. We found that canopy cover and VGVI had a positive association with satisfaction with urban trees. The associations were comparatively stronger at larger neighbourhood scales than at smaller scales. There were no statistically significant associations with NDVI or satisfaction with the management of urban trees.

RUGOSE SPIRALING WHITEFLY INFESTATION ON COCONUT: THREATS AND REMEDY

Rugose spiraling whitefly (RSW) Aleurodicus rugioperculagus (Hemiptera: Aleurodidae) is an invasive pest causing severe damage to coconut plant, having higher percent of infestation (70.2% to 165.4%). The nymph and adults suck cell sap from the underside of the leaves and secret honeydew that develop sooty mold fungi and interfere the growth of the plants. It was first identified in Bangladesh in 2019 on coconut plants in Jashore.Then it has spread to almost all other districts in Bangladesh. Highest coconut tree infestation was observed in southwestern part (97.5%) and lowest (68.0%) was in central part of the country and there was no significant difference among locations. In the coastal regions, the highest percent (97.0%) of infested coconut tree, infested fronds (96.2%) and leaflet area (84.0%) was recorded in Bagerhat district while the lowest percent infested tree (68.0%), fronds per tree (67.2%) and leaflet area (48.0%) was in Cox’s Bazar. The abundance of RSW increases as the number of leaves increased. Higher percent tree infestation (85.7%) was noticed in native while it was lower(81.2%) in dwarf coconut plants although no significant difference was observed.Low abundances of adult RSW were also found in dwarf coconut plants than that of native ones. The highest incidence of whitefly of coconut was observed in summer months of April-May andthe lowest was in June-July. Biological control is an effective measure where Encarsia guadeloupae, lady bird beetleand lacewings are common predators and entomopathogenic fungi Isaria fumosorosea reduces the incidenceof RSW from 77.8 to 75.1%. Various cultural and mechanical practices also significantly reduce the pest incidence, infestation, abundance of egg, nymph and adult by 50.0%, 49.9%, 49.7%, 50.1% and 49.8% respectively. Different systemic neonicotinoid insecticides also showed successful result in controlling rugose spiraling whitefly

Effects of Climate Change on the Distribution of Threatened Fishing Bat Myotis pilosus in China.

Climate change and biodiversity loss are two severe challenges that the world is facing. Studying the distribution shifts of species in response to climate change could provide insights into long-term conservation and biodiversity maintenance. Myotis pilosus is the only known fishing bat in East Asia, whereas its population has been decreasing in recent years and it is listed as a "Vulnerable" species. To assess the impact of climate change on the distribution of M. pilosus, we obtained 33 M. pilosus occurrence records within China where they are mainly distributed, and extracted 30 environmental variables. MaxEnt was applied to assess the habitat suitability, recognize the important environmental variables, predict future distribution changes, and identify the potential future climate refugia. The prediction result based on eleven dominant environmental variables was excellent. The Jackknife test showed that the "minimum temperature of coldest month", "precipitation of wettest quarter", "percent tree cover", and "precipitation of driest month" were the main factors affecting the distribution of M. pilosus. The current suitable areas were predicted to be mainly located in southwest and southeast China with a total area of about 160.54 × 104 km2, accounting for 16.72% of China's land area. Based on the CCSM4, it was predicted that the future (2050 and 2070) suitable areas of M. pilosus will expand and shift to high latitudes and altitudes with global warming, but the area of moderately and highly suitable habitats will be small. Considering the dispersal capacity of M. pilosus, the area of colonized suitable habitats in 2050 and 2070 was predicted to be only ca. 94 × 104 km2 and 155 × 104 km2, respectively. The central and southern parts of Hainan, southern Guangdong, central Guizhou, and southern Beijing were identified as potential climate refugia and could be considered as priority conservation areas for M. pilosus. Thus, we suggest long-term monitoring of the priority conservation areas, especially the areas at high latitudes and altitudes. These results contribute to our knowledge of the possible spatial distribution pattern of M. pilosus under current and future climate scenarios, which is important for the population protection and habitat management of this special piscivorous bat species.

Examination of the amount of GEDI data required to characterize central Africa tropical forest aboveground biomass at REDD+ project scale in Mai Ndombe province

The Global Ecosystem Dynamics Investigation (GEDI) is the first spaceborne LiDAR designed to improve quantification of vegetation structure and forest aboveground biomass (AGB) including in the tropics where forest AGB inventory data are limited. GEDI is a sampling instrument on the International Space Station (ISS) and does not provide data on a regular, systematic basis. Reducing Emissions from Deforestation and Degradation and enhancement of carbon stocks (REDD+) projects require forest AGB inventories to quantify avoided carbon emissions achieved by conserving forest biomass. Although there is high confidence that GEDI can retrieve measurements that allow estimation of AGB at scale, less is known about how well its operational deployment performs for measurement of AGB to support REDD+ projects. This includes an understanding of the appropriate time period required to collect sufficient GEDI observations for reliable forest AGB assessment. This paper describes the first study to examine the amount of GEDI data needed to characterize tropical forest AGB at REDD+ project scale. In tropical Africa, the average REDD+ project size documented by the Center for International Forestry Research is equivalent to a square area of approximately 50 × 50 km (250,000 ha). Recently available good quality GEDI footprint-level AGB product data acquired over a 31 month period over Mai Ndombe province in the west of the Democratic Republic of the Congo were considered. A global 30 m percent tree cover product, updated with contemporary mapped forest cover loss, was used to map the intact forest across the province. Fifteen 50 × 50 km test sites, representing example REDD+ project areas with >80% forest cover and good quality AGB forest footprint data distributed across each site, were selected. The sites were selected from five AGB stratum defined from the GEDI data, and with three sites selected per stratum that had low, medium and high semivariogram sill values that reflect increasing within-site AGB spatial variation. The overall mean GEDI AGB (OMGA) was derived from all the good quality forest GEDI footprint AGB values acquired over the 31 months of GEDI operation at each site. The expected minimum number of GEDI orbits (norbitsp) required to characterize the OMGA to within p = ±5%, ±10%, and ±20% was derived by considering different combinations of GEDI orbits randomly selected from the 31 months of GEDI data. The expected minimum number of days (ndaysp) required to characterize the AGB over each site was derived by multiplying the site norbitsp values with a scalar coefficient of 13.03 days. The scalar coefficient was found by counting the temporal intervals between successive GEDI orbits containing good quality forest AGB data and is equivalent to the average number of days required to obtain a GEDI orbit containing good quality forest AGB data at 50 × 50 km scale. Among the 15 sites, observation periods ranging from 65 to 221 days (0.18 – 0.61 years), 143 – 534 days (0.39 – 1.46 years), and 390 – 742 days (1.07 – 2.03 years) were required to characterize the AGB to within ±20%, ±10%, and ±5% of the site OMGA, respectively. The Intergovernmental Panel on Climate Change (IPCC) recommended accuracy requirement for forest AGB estimates is 10%. Thus, to meet this accuracy requirement the findings of this study indicate that at least 534 days (1.46 years) would be required for REDD+ site monitoring using GEDI in Mai Ndombe province. In other central African tropical forest localities these observations periods may be different depending on the forest AGB and spatial variation, cloud cover, ephemeral surface water presence, and GEDI AGB retrieval sensitivity to the forest conditions.

The Impacts of Redlining on Urban Heat in New York’s Capital Districts

The impact of green space, or lack thereof, on the surface temperature of cities has long been studied through the Urban Heat Island effect. However, the extent to which the historic discriminatory housing policy of redlining influences the temperatures of different neighborhoods in the same city is still being revealed. Here, the researchers investigated how Home Owners’ Loan Corporation (HOLC) maps correspond to land surface temperatures through uneven distribution of tree canopy and impervious surfaces in New York’s Capital District. Using HOLC maps for Albany, Troy, and Schenectady, the researchers calculated percent tree cover and imperviousness for each of the map’s graded blocks, and calculated the land surface temperature for each block using Landsat 8 imagery. Results showed that neighborhoods rated “hazardous” were generally hotter, more impervious, and less tree-covered than those rated “best” in Albany and Schenectady, but not in Troy. The difference between blocks rated “hazardous” and blocks rated “best” was +2.67°C in Albany, +4.18° in Schenectady, and -1.18° in Troy. These results are an example of how discriminatory housing policies are tied to a disproportional distribution of urban heat. Affected communities, like those in the Capital District, should be identified to increase resilience to the environmental, physiological, and social impacts of climate change.
 Keywords: Urban Heat Island Effect, Redlining, New York, Housing Policy, Green Space, Urban Tree Cover.

First assessment of optical and microwave remotely sensed vegetation proxies in monitoring aboveground carbon in tropical Asia

Tropical Asia has shown a strong greening trend in recent years. However, detailed knowledge about changes in carbon stocks remain uncertain as only few studies have used remotely sensed vegetation products for monitoring spatial and temporal changes of aboveground live biomass carbon (AGC) in that region. This study aims at evaluating optical- and microwave-based vegetation proxies (i.e., LAI, percent tree cover (PTC), L−/X-/C-band vegetation optical depths (VOD)) for understanding the spatio-temporal variations of tropical Asian AGC between 2013 and 2019.Our results indicated that the spatial distributions of L-VOD and PTC were highly spatially correlated with four benchmark AGC maps used for comparison (R > 0.79 and R > 0.75, respectively). By employing L-VOD as the reference data in assessing AGC dynamics, the X-/C-VOD showed advantages in capturing AGC changes for low-medium (20–40 Mg C/ha) carbon density vegetation, respectively, while other vegetation proxies showed limited capabilities. All proxies presented limitations in tracking AGC dynamics at high AGC density (> 60 Mg C/ha). Tropical Asian AGC stocks estimated using the L-VOD product indicated that tropical Asia accumulated carbon in biomass at a rate of +44+39+53 Tg C/yr between 2013 and 2019. This small sink is dominated by non-forest biomes (65.9%). The non-forest regions in southern India, southwest China, and southern Vietnam and southwest China showed a continuous AGC increase while forests in northern Laos, Malaysia, and central Indonesia experienced continuous decreases between 2013 and 2019 caused by deforestation.