Spatio-Temporal Changes in Vegetation in the Last Two Decades (2001–2020) in the Beijing–Tianjin–Hebei Region

In terrestrial ecosystems, vegetation is sensitive to climate change and human activities. Its spatial-temporal changes also affect the ecological and social environment. In this paper, we considered the Beijing–Tianjin–Hebei region to study the spatio-temporal vegetation patterns. The detailed analysis of a moderate-resolution imaging spectroradiometer (MODIS) data were carried out through the Google Earth Engine (GEE) platform. Our results show a slow and tortuous upward trend in the average leaf area index (LAI) in the study region for the periods 2001–2020. Specifically, Beijing had the highest LAI value, with an average of 1.64 over twenty years, followed by Hebei (1.30) and Tianjin (1.04). Among different vegetation types, forests had the highest normalized difference vegetation index (NDVI) with the range of 0.62–0.78, followed by shrubland (0.58–0.75), grassland (0.34–0.66), and cropland (0.38–0.54) over the years. Spatially, compared to the whole study area, index value in the northwestern part of the Beijing–Tianjin–Hebei region increased greatly in many areas, such as northwest Beijing, Chengde, and Zhangjiakou, indicating a significant ecological optimization. Meanwhile, there was ecological degradation in the middle and southeast regions, from Tangshan southeastward to Handan, crossing Tianjin, Langfang, the east part of Baoding, Shijiazhuang, and the west part of Cangzhou. Air temperature and precipitation were positively and significantly correlated with net primary production (NPP) and precipitation stood out as a key driver. Additionally, an intensification of the urbanization rate will negatively impact the vegetation NPP, with the shrubland and forest being affected most relative to the cropland

ENHANCING CONSERVATION WITH HIGH RESOLUTION PRODUCTIVITY DATASETS FOR THE CONTERMINOUS UNITED STATES

Human driven alteration of the earth’s terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earth’s terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS). In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production across the CONUS domain. The main results of this work are three publically available datasets: 1) 30 m Landsat NDVI; 2) 250 m MODIS based GPP and NPP; and 3) 30 m Landsat based GPP and NPP. My goal is that these products prove useful for the wider scientific, conservation, and land management communities as we continue to strive for better conservation and management practices

Mapping the status of the North American beaver invasion in the Tierra del Fuego archipelago

Quantifying the presence and environmental impact of invasive species is the starting point for research on management and nature conservation. North American beavers (Castor canadensis) were introduced to Argentina from Canada in 1946, and the species has been identified as a major agent of environmental change in the Tierra del Fuego archipelago in the Anthropocene. We studied the invasion status (distribution and density) of beavers through analyses of the dam densities in the Tierra del Fuego landscapes. We identified beaver dams with a GIS using visual interpretation of high-resolution aerial imagery from Microsoft Bing, Google Earth and HERE and related them to natural environmental gradients. These factors comprised geographic (vegetation zones and distance to streams), climatic (temperature, precipitation, evapotranspiration and net primary productivity) and topographic (elevation and slope) data. The datasets (dams and factors) were combined, and the data from the different zonation classes were subsequently compared using ANOVAs and Tukey’s mean comparison tests. Deviations from the mean density (x mean density—x total mean density) were calculated to visualize the deviations for the studied factors. The datasets were also evaluated using principal component analyses (PCA). Our results showed a total of 206,203 beaver dams (100,951 in Argentina and 105,252 in Chile) in the study area (73,000 km2). The main island of Tierra del Fuego presented a greater degree of invasion (73.6% of the total study area) than the rest of the archipelago, especially in areas covered by mixed-evergreen and deciduous forests. The studied geographic, climatic and topographic factors showed positive trends (higher beaver preference) with beaver spread, which were all significant (p <0.05) when compared across the landscape. Although beavers are flexible in their habitat use, our empirical records showed that they had marked preferences and were positively influenced by the most productive forests. Here, we describe a scientific panorama that identified the drivers of species invasion based on satellite data and the available ecological datasets. The identification of such drivers could be useful for developing new tools for management and/or control strategies of the beavers in the Tierra del Fuego archipelago.Fil: Huertas Herrera, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Lencinas, María Vanessa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Toro Manríquez, Mónica del Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Miller, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentin

A Case Study Examining the Relationship Between Human Appropriation of Net Primary Productivity and Landscape Patterns in the U.S. Great Lakes Basin

Human appropriation of net primary productivity (HANPP) has been proposed as a measure of human pressures on biodiversity; it represents the proportion of energy flow that was historically available to wildlife food webs but has been appropriated for human use, primarily through the harvesting of primary production. This study examined the spatial relationship between HANPP of managed terrestrial landscapes and two abiotic proxy metrics for biodiversity–landscape diversity and local connectedness. Our objectives were 1) to quantify patterns of HANPP in forestlands and croplands, comparing the extraction of NPP in a recent decade against the potential natural vegetation that largely existed on the US side of the Great Lakes prior to European settlement; and 2) to assess spatial patterns of HANPP in comparison to landscape diversity and local connectedness at the county scale across the region. Our analysis considered above and below-ground compartments of NPP and focused on the percent of potential NPP being appropriated (%HANPP0). The mean areaweighted %HANPP0 across our study region was 45%, with the lowest %HANPP0 occurring in counties with >50% forest cover. We observed a significant (p<0.001) but weak, negative relationship between %HANPP0 and county means of landscape diversity (r=-0.53, r2=0.28) and a significant (p<0.001), moderate, negative relationship between %HANPP0 and local connectedness (r =-0.61, r2=0.36). Our findings are comparable to global estimate of HANPP on croplands and forestlands, and support previous research indicating HANPP negatively impacts biodiversity. We concluded the calculation of HANPP could be used as an additional tool for conservation professionals during regional-scale landuse planning or conservation decision-making, particularly in mixed-use landscapes that exhibit potential to support biodiversity based on abiotic proxy measures and have high amounts of primary production harvest.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/145425/1/Barton_Erin_Thesis.pd

Monitoring effects of land cover change on biophysical drivers in rangelands using albedo

This paper explores the relationship between land cover change and albedo, recognized as a regulating ecosystems service. Trends and relationships between land cover change and surface albedo were quantified to characterise catchment water and carbon fluxes, through respectively evapotranspiration (ET) and net primary production (NPP). Moderate resolution imaging spectroradiometer (MODIS) and Landsat satellite data were used to describe trends at catchment and land cover change trajectory level. Peak season albedo was computed to reduce seasonal effects. Different trends were found depending on catchment land management practices, and satellite data used. Although not statistically significant, albedo, NPP, ET and normalised difference vegetation index (NDVI) were all correlated with rainfall. In both catchments, NPP, ET and NDVI showed a weak negative trend, while albedo showed a weak positive trend. Modelled land cover change was used to calculate future carbon storage and water use, with a decrease in catchment carbon storage and water use computed. Grassland, a dominant dormant land cover class, was targeted for land cover change by woody encroachment and afforestation, causing a decrease in albedo, while urbanisation and cultivation caused an increase in albedo. Land cover map error of fragmented transition classes and the mixed pixel effect, affected results, suggesting use of higher-resolution imagery for NPP and ET and albedo as a proxy for land cover

Utilizing Satellite Fusion Methods to Assess Vegetation Phenology in a Semi-Arid Ecosystem

Dryland ecosystems cover over 40% of the Earth’s surface, and are highly heterogeneous systems dependent upon rainfall and temperature. Climate change and anthropogenic activities have caused considerable shifts in vegetation and fire regimes, leading to desertification, habitat loss, and the spread of invasive species. Modern public satellite imagery is unable to detect fine temporal and spatial changes that occur in drylands. These ecosystems can have rapid phenological changes, and the heterogeneity of the ground cover is unable to be identified at course pixel sizes (e.g. 250 m). We develop a system that uses data from multiple satellites to model finer data to detect phenology in a semi-arid ecosystem, a dryland ecosystem type. The first study in this thesis uses recent developments in readily available satellite imagery, coupled with new systems for large-scale data analysis. Google Earth Engine is used with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to create high resolution imagery from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). The 250 m daily MODIS data are downscaled using the 16-day, 30 m Landsat imagery resulting in daily, 30 m data. The downscaled images are used to observe vegetation phenology over the semi-arid region of the Morley Nelson Snake River Birds of Prey National Conservation Area in Southwestern Idaho, USA. We found the fused satellite imagery has a high accuracy, with R2 ranging from 0.73 to 0.99, when comparing fusion products to the true Landsat imagery. From these data, we observed the phenology of native and invasive vegetation, which can help scientists develop models and classifications of this ecosystem. The second study in this thesis builds upon the fused satellite imagery to understand pre-and post-fire vegetation response in the same ecosystem. We investigate the phenology of five areas that burned in 2012 by using the fusion imagery (daily) to derive the normalized difference vegetation index (NDVI, a measure of vegetation greenness) in areas dominated by grass (n=4) and shrub (n=1). The five areas also had a range of historical burns before 2012, and overall we investigated the phenology of these areas over a decade. This proof of concept resulted in observations of the relationship between the timing of fire and the vegetation greenness recovery. For example, we found that early and late season fires take the longest amount of time for vegetation greenness to recover, and that the number of historical fires has little impact in the vegetation greenness response if it has already burned once, and is a grass-dominated region. The greenness dynamics of the shrub-dominated study site provides insight into the potential to monitor post-fire invasion by nonnative grasses. Ultimately the systems developed in this thesis can be used to monitor semi-arid ecosystems over long-time periods at high spatial and temporal resolution

North American boreal forests are a large carbon source due to wildfires from 1986 to 2016

Wildfires are a major disturbance to forest carbon (C) balance through both immediate combustion emissions and post-fire ecosystem dynamics. Here we used a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to simulate C budget in Alaska and Canada during 1986-2016, as impacted by fire disturbances. We extracted the data of difference Normalized Burn Ratio (dNBR) for fires from Landsat TM/ETM imagery and estimated the proportion of vegetation and soil C combustion. We observed that the region was a C source of 2.74 Pg C during the 31-year period. The observed C loss, 57.1 Tg C year(-1), was attributed to fire emissions, overwhelming the net ecosystem production (1.9 Tg C year(-1)) in the region. Our simulated direct emissions for Alaska and Canada are within the range of field measurements and other model estimates. As burn severity increased, combustion emission tended to switch from vegetation origin towards soil origin. When dNBR is below 300, fires increase soil temperature and decrease soil moisture and thus, enhance soil respiration. However, the post-fire soil respiration decreases for moderate or high burn severity. The proportion of post-fire soil emission in total emissions increased with burn severity. Net nitrogen mineralization gradually recovered after fire, enhancing net primary production. Net ecosystem production recovered fast under higher burn severities. The impact of fire disturbance on the C balance of northern ecosystems and the associated uncertainties can be better characterized with long-term, prior-, during- and post-disturbance data across the geospatial spectrum. Our findings suggest that the regional source of carbon to the atmosphere will persist if the observed forest wildfire occurrence and severity continues into the future.Peer reviewe

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Regime de queima em Goiás, Brasil, e em Moçambique entre 2010 e 2019: frequência, recorrência e classes de cobertura mais afetadas

Nos últimos anos, o mundo tem experienciado eventos extremos&nbsp;relacionados à ocorrência do fogo, que vêm causando uma série de&nbsp;danos às populações e ecossistemas. Em 2020 incêndios assolaram&nbsp;Austrália, Brasil, Estados Unidos, entre outras nações. Isso dá à temática&nbsp;dos incêndios florestais relevância e urgência globais e torna necessários&nbsp;a maior compreensão e o monitoramento desses eventos. O presente&nbsp;trabalho buscou identificar semelhanças e diferenças no regime de fogo,&nbsp;mais especificamente na frequência e recorrência, entre Moçambique&nbsp;e no Estado de Goiás, Brasil, entre 2010 e 2019. Ambos os recortes&nbsp;estão localizados na mesma zona bioclimática, onde estão presentes as&nbsp;savanas. Estas, consideradas ecossistemas com maior resiliência ao fogo,&nbsp;não estão imunes às consequências de incêndios intensos e frequentes.&nbsp;Logo, monitorar tais eventos nesses ecossistemas é importante,&nbsp;principalmente para identificar características que possam nortear a&nbsp;tomada de decisões. As etapas metodológicas para o desenvolvimento&nbsp;da presente pesquisa envolveram organização de base de dados e uso&nbsp;de plataformas de processamento geoespacial baseado em nuvem, o&nbsp;que resultou em produtos de caracterização dos eventos de queima. Em&nbsp;ambos os recortes estudados, o fogo ocorre anualmente em extensões&nbsp;consideráveis, principalmente no caso de Moçambique, cujo percentual&nbsp;de área queimada anualmente é maior que o de Goiás. Tal dinâmica&nbsp;pode estar relacionada a especificidades de cada região. Os presentes&nbsp;resultados possibilitam melhor compreensão de como se dá a ocorrência&nbsp;de incêndios e queimadas em diferentes savanas e podem motivar outras&nbsp;pesquisas a respeito, com vistas a maiores esclarecimentos.Over the last few years, the world has experienced extreme&nbsp;events related to the occurrence of fire, which has caused a&nbsp;great deal of damage to people and ecosystems. In 2020 fires&nbsp;raged in Australia, Brazil, the United States, and other nations.&nbsp;Thus, the forest fire issue becomes a matter of global relevance&nbsp;and urgency and requires a better understanding and monitoring&nbsp;of these events. This study sought to identify similarities and&nbsp;differences between the fire regime, specifically the frequency&nbsp;and recurrence, in Mozambique and the state of Goiás, Brazil,&nbsp;between 2010 and 2019. Both focuses are located in the same&nbsp;bioclimatic zone, where savannas are present. Savannas,&nbsp;considered the most fire-resilient ecosystems, are not immune&nbsp;to the consequences of intense and frequent fires. Therefore,&nbsp;monitoring such events in these ecosystems is important,&nbsp;especially to identify characteristics that can guide decisionmaking.&nbsp;The methodological steps for developing this study&nbsp;involved database organization and using cloud-based geospatial&nbsp;processing platforms, which resulted in fire event characterization&nbsp;products. In both of the studied focuses, fire occurs annually&nbsp;in significant extensions, especially in Mozambique, where the&nbsp;burnt area percentage is higher than in Goiás. Such dynamics&nbsp;may be related to each region’s specificities. These results allow for a better understanding of how fires and burning occur in&nbsp;different savannas. and may motivate further research aimed at&nbsp;further clarification

A bi-directional strategy to detect land use function change using time-series Landsat imagery on Google Earth Engine:A case study of Huangshui River Basin in China

Constructed land, cropland, and ecological land are undergoing intense competition in many rapidly-developing regions. One of the major reasons to cause frequent land use (LU) conversions is the policy dynamics. The detection of such conversions is thus a prerequisite to understanding urban dynamics and how policies shape landscapes. This paper presents a bi-directional strategy to detect the LU change of the Huangshui River Basin of China from 1987 to 2018 using time-series Landsat imagery. We first initialized classification and optimization of remote sensing images using the Random Forest algorithm; We then detected bi-directional spatio-temporal changes based on the distribution probability of land-cover types. Our results reveal complicated dynamics underlying the net increase in urban and built-up land (UB) and the net decrease in cropland. In this area, due to the implementation of ecological compensation projects such as ecological migration and mine restoration, we found that on average 5.52 km2 of UB was converted into ecological land (forest, grassland and shrubland) every year, even though UB has expanded 3.6 times in the last 30 years with multiple conversions for cropland and ecological land. Meanwhile, 60% of lost cropland was converted to shrubland and grassland, and 40% was converted to UB. The accuracy of LU classification increases by 6.03% from 88.17%, and kappa coefficient increases by 2.41% from 85.16, compared to the existing initial results and uni-directional detection method. This study highlights the importance of the use of an effective remote sensing-based strategy for monitoring high-frequency LU changes in watershed areas with complicated human-nature interactions.</p