Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models

No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau

Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth\u27s “third pole,” is a unique region for studying the long‐term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low‐level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982–2014), the GIMMS NDVI data set (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001–2014), the Satellite Pour l\u27Observation de la Terre Vegetation (SPOT‐VEG) NDVI data set (1999–2013), and the Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS) NDVI data set (1998–2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green‐up” dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground‐based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO\u3csub\u3e2\u3c/sub\u3e flux tower data

Abstract Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO\u3csub\u3e2\u3c/sub\u3e flux tower data

Abstract Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security

Climate, land use and vegetation trends: Implication of land use change and climate change on northwestern drylands of Ethiopia

Land use / land cover (LULC) change assessment is getting more consideration by global environmental change studies as land use change is exposing dryland environments for transitions and higher rates of resource depletion. The semiarid regions of northwestern Ethiopia are not different as land use transition is the major problem of the region. However, there is no satisfactory study to quantify the change process of the region up to now. Hence, spatiotemporal change analysis is vital for understanding and identification of major threats and solicit solutions for sustainable management of the ecosystem. LULC change studies focus on understanding the patterns, processes and dynamics of land use transitions and driving forces of change. The change processes in dryland ecosystems can be either seasonal, gradual or abrupt changes of random or systematic change processes that result in a pattern or permanent transition in land use. Identification of these processes of change and their type supports adoption of monitoring options and indicate possible measures to be taken to safeguard this dynamic ecosystem. This study examines the spatiotemporal patterns of LULC change, temporal trends in climate variables and the insights of the communities on change patterns of ecosystems. Landsat imagery, MODIS NDVI, CRU temperature, TAMSAT rainfall and socio-ecological field data were used in order to identify change processes. LULC transformation was monitored using support vector machine (SVM) algorithm. A cross-tabulation matrix assessment was implemented in order to assess the total change of land use categories based on net change and swap change. In addition, the pattern of change was identified based on expected gain and loss under a random process of gain and loss, respectively. Breaks For Additive Seasonal and Trend (BFAST) analysis was employed for determining the time, direction and magnitude of seasonal, abrupt and trend changes within the time series datasets. In addition, Man Kendall test statistic and Sen’s slope estimator were used for assessing long term trends on detrended time series data components. Distributed lag (DL) model was also adopted in order to determine the time lag response of vegetation to the current and past rainfall distribution. Over the study period of 1972- 2014, there is a significant change in LULC as evidenced by a significant increase in size of cropland of about 53% and a net loss of over 61% of woodland area. The period 2000-2014 has shown a sharp increase of cropland and a sharp decline of woodland areas. Proximate causes include agricultural expansion and excessive wood harvesting; and underlying causes of demographic factor, economic factors and policy contributed the most to an overuse of existing natural resources. In both the observed and expected proportion of random process of change and of systematic changes, woodland has shown the highest loss compared to other land use types. The observed transition and expected transition under random process of gain of woodland to cropland is 1.7%, implies that cropland systematically gains to replace woodland. The comparison of the difference between observed and expected loss under random process of loss also showed that when woodland loses cropland systematically replaces it. The assessment of magnitude and time of breakpoints on climate data and NDVI showed different results. Accordingly, NDVI analysis demonstrated the existence of breakpoints that are statistically significant on the seasonal and long term trends. There is a positive trend, but no breakpoints on the long term precipitation data during the study period. The maximum temperature also showed a positive trend with two breakpoints which are not statistically significant. On the other hand, there is no seasonal and trend breakpoints in minimum temperature, though there is an overall positive trend along the study period. The Man-Kendall test statistic for long term average Tmin and Tmax showed significant variation where as there is no significant trend within the long term rainfall distribution. The lag regression between NDVI and precipitation indicated a lag of up to forty days. This proves that the vegetation growth in this area is not primarily determined by the current precipitation rather with the previous forty days rainfall. The combined analysis showed declining vegetation productivity and a loss of vegetation cover that contributed for an easy movement of dust clouds during the dry period of the year. This affects the land condition of the region, resulting in long term degradation of the environmen

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

Exploring short-term climate change effects on rangelands and broad-leaved forests by free satellite data in Aosta Valley (Northwest Italy)

Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer

Climatic impacts of vegetation dynamics in Eastern Africa

The climate system responds to changes in the structure and physiology of vegetation. These changes can be induced by seasonal growing cycles, anthropogenic land cover changes (LCCs), and precipitation extremes. The extent to which vegetation changes impact the climate depends on the type of ecosystem, the season, and the intensity of perturbations from LCCs and precipitation extremes. Under the growing impacts of climate change and human modification of natural vegetation cover, understanding and monitoring the underlying biogeophysical processes through which vegetation affects the climate are central to the development and implementation of effective land use plans and mitigation measures. In Eastern Africa (EA) the vegetation is characterized by multiple growing cycles and affected by agricultural expansion as well as recurrent and severe drought events. Nonetheless, the degrees to which vegetation changes affect the surface energy budget and land surface temperature (LST) remain uncertain. Moreover, the relative contributions of various biogeophysical mechanisms to land surface warming or cooling across biomes, seasons, and scales (regional to local) are unknown. The objective of this thesis was to analyze and quantify the climatic impacts of land changes induced by vegetation seasonal dynamics, agricultural expansion, and precipitation extremes in EA. In particular, this thesis investigated these impacts across biomes and spatio-temporal scales. To address this objective, satellite observation and meteorological data were utilized along with empirical models, observation-based metrics, and statistical methods. The results showed that rainfall–vegetation interaction had a strong impact on LST seasonality across ecoregions and rainfall modality patterns. Furthermore, seasonal LST dynamics were largely controlled by evapotranspiration (ET) changes that offset the albedo impact on the surface radiation balance. Forest loss disturbed the LST dynamics and increased local LST consistently and notably during dry seasons, whereas during the wet season its impact was limited because of strong rainfall–vegetation interaction. Moreover, drought events affected LST anomalies; however, the impact of droughts on temperature anomalies was highly regulated by vegetation greening. In addition, the conversion of forest to cropland generated the highest net warming (1.3 K) compared with other conversion types (savanna, shrubland, grassland, and cropland). Warming from the reduction of ET and surface roughness was up to ~10 times stronger than the cooling effect from albedo increases (−0.12 K). Furthermore, large scale analysis revealed a comparable warming magnitude during bushland-to-cropland conversion associated with the dominant impact of latent heat (LE) flux reduction, which outweighed the albedo effect by up to ~5 times. A similar mechanism dominated the surface feedback during precipitation extremes; where LE flux anomalies dominated the energy exchange causing the strongest LST anomaly in grassland, followed by savanna. By contrast, the impact was negligible in forest ecosystems. In conclusion, the results of this thesis clarify the mechanics and magnitude of the impacts of vegetation dynamics on LST across biomes and seasons. These results are crucial for guiding land use planning and climate change mitigation efforts in EA. The methods and results of this thesis can assist in the development of ecosystem-based mitigation strategies that are tailored to EA biomes. Moreover, they can be used for assessing the performance of climate models and observation-based global scale studies that focus on the biogeophysical impacts of LCCs. Keywords: LST seasonality; Land cover change; Bushland (Acacia-Commiphora); Biophysical effects; Precipitation extremes; Satellite observation.Ilmastojärjestelmä reagoi kasvillisuuden rakenteen ja fysiologian muutoksiin. Muutokset voivat johtua kasvukauden vaiheesta, ihmistoiminnan vaikutuksesta maanpeitteeseen ja sään ääri-ilmiöistä. Se missä määrin kasvillisuuden muutokset vaikuttavat ilmastoon riippuu ekosysteemistä ja vuodenajasta sekä maanpeitemuutosten ja sään ääri-ilmiöiden voimakkuudesta. Ilmastonmuutoksen ja maanpeitteen muokkaamisen vaikutusten voimistuessa on keskeistä ymmärtää ja seurata biogeofysikaalisia prosesseja, joiden kautta kasvillisuus vaikuttaa ilmastoon. Tällä tiedolla on keskeinen rooli tehokkaiden maankäyttösuunnitelmien kehittämisessä ja toteuttamisessa sekä ilmastonmuutoksen hillinnässä. Itä-Afrikassa kasvillisuudella on ominaisesti useita kasvukausia ja siihen vaikuttavat maatalouden laajentuminen sekä toistuvat ja vakavat kuivuusjaksot. Siitä huolimatta kasvillisuuden muutosten vaikutus energiataseeseen ja maanpinnan lämpötilaan on edelleen epävarmaa. Lisäksi eri biogeofysikaalisten mekanismien suhteellista vaikutusta maanpinnan lämpenemiseen tai jäähtymiseen eri biomien, vuodenaikojen ja mittakaavojen (alueellinen ja paikallinen) välillä ei tunneta. Tämän tutkielman tavoitteena oli analysoida ja kvantifioida kasvillisuuden vuodenaikaisvaihtelun, maatalouden laajentumisen ja sademäärän ääri-ilmiöiden aiheuttamien muutosten ilmastovaikutuksia Itä-Afrikassa. Erityisesti tutkielmassa tarkasteltiin vaikutuksia eri biomien ja mittakaavojen välillä. Tutkielmassa hyödynnettiin satelliittihavaintoja ja meteorologisia tietoja sekä empiirisiä malleja, havaintopohjaisia indeksejä ja tilastollisia menetelmiä. Tulokset osoittivat, että sademäärän ja kasvillisuuden vuorovaikutuksella oli voimakas vaikutus maanpinnan lämpötilan vuodenaikaisvaihteluun kasvillisuustyyppien ja sademoodien välillä. Maanpinnan lämpötilaa säätelivät suurelta osin evapotranspiraation muutokset, jotka kompensoivat albedon vaikutuksia pinnan säteilytasapainoon. Metsän häviäminen häiritsi maanpinnan lämpötilan dynamiikkaa ja lisäsi sitä paikallisesti, etenkin kuivina vuodenaikoina, kun taas sadekauden aikana sen vaikutus oli vähäinen sateen ja kasvillisuuden voimakkaan vuorovaikutuksen vuoksi. Lisäksi kuivuus vaikutti lämpötilan poikkeavuuksiin; kuivuuden vaikutusta sääteli kuitenkin voimakkaasti kasvillisuuden vihertyminen. Metsän muuntaminen viljelysmaaksi aiheutti suurimman nettolämmityksen (1.3 K) verrattuna muihin muutostyyppeihin (savanni, pensaikko, ruohostomaat ja viljelymaat). Evapotranspiraation vähenemisestä ja pinnan epätasaisuudesta aiheutuva lämpeneminen oli jopa noin 10 kertaa voimakkaampi kuin albedon jäähdytysvaikutus (−0.12 K). Lisäksi pensaikon muuntaminen viljelysmaaksi aiheutti vastaavan lämpenemisen. Lämpeneminen liittyi latentin lämpövuon merkityksen vähentymiseen, joka ylitti albedovaikutuksen jopa noin viisinkertaisesti. Samanlainen mekanismi hallitsi sademäärän ääripäiden aikana, jolloin latentin lämpövuon poikkeavuudet hallitsivat energianvaihtoa aiheuttaen voimakkaimman maanpinnan lämpötilan poikkeavuuden ruohostomailla ja savanneilla. Sitä vastoin metsissä vaikutus oli vähäinen. Yhteenvetona voidaan todeta, että tutkielman tulokset selventävät kasvillisuuden dynamiikan vaikutusten mekanismeja ja suuruutta maanpinnan lämpötilaan biomien ja vuodenaikojen välillä. Tulokset ovat tärkeitä Itä-Afrikan maankäytön suunnittelun ja ilmastonmuutoksen hillitsemistoimien ohjaamisessa. Tutkielman menetelmät ja tulokset voivat auttaa kehittämään Itä-Afrikan biomeille räätälöityjä ekosysteemipohjaisia lieventämisstrategioita. Lisäksi niitä voidaan käyttää arvioimaan ilmastomalleja ja havaintopohjaisia globaalin mittakaavan tutkimuksia, jotka keskittyvät maanpeitemuutosten biogeofysikaalisiin vaikutuksiin. Avainsanat: Maanpinnan lämpötilan vuodenaikaisvaihtelu; Maanpeitteen muutos; Pensaikko (AcaciaCommiphora); Biofysikaaliset vaikutukset; Sademäärä; Satelliittikaukokartoitus