Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions.Peer Reviewe

Urban−rural gradients reveal joint control of elevated CO₂ and temperature on extended photosynthetic seasons

Photosynthetic phenology has large effects on the land-atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO₂) concentrations, which reflect expected future environmental conditions. Using more than 880 urban-rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO₂, we investigated the combined impacts of elevated CO₂ and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (−5.6 ± 0.7 d), peaked earlier (−4.9  ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO₂ and temperature, whereas the delayed end of season was mainly attributed to CO₂ enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change

characterizing responses of land surface phenology to urbanization, climate change, and extreme weather events using remote sensing and Bayesian models

Land surface phenology (LSP) is the intra-annual rhythm of vegetation dynamic from dormancy to activity and back to dormancy over the landscape. Shifts of LSP have cascading effects on food production, carbon sequestration, water consumption, biodiversity, and public health. There are three major knowledge gaps in understanding the impacts of urbanization, climate change, and extreme weather events on LSP. (1) Previous studies mainly focused on investigating the effects of urbanization on the spatial patterns of LSP by comparing the phenological metrics between urban center and the surrounding rural regions. However, it remains unclear how urbanization-induced land cover conversions and climate change jointly influence the temporal variations of LSP. (2) Conventional methods usually model key phenological transition dates (e.g. discrete timing of spring bud-break and fall senescence) based on aggregated climate variables (e.g. mean temperature, growing-degree days), ignoring the fact that LSP is a dynamic and continuous process which responds to daily weather conditions continuously. (3) Current projection of LSP shifts under future environmental changes relies heavily on species-level observations and degree-day models. It is challenging to produce a set of future LSP metrics in a temporally consistent manner at regional-to-continental scales. To fill these knowledge gaps, this three-part dissertation built a data-model synthesis framework by integrating remotely sensed data and climate data into a state-of-the-art Bayesian model. First, I established a framework to separate the temporal shifts of phenology driven by climate change from those caused by urbanization. Second, I developed and evaluated a Bayesian Hierarchical Space-Time model for Land Surface Phenology (BHST-LSP) to synthesize remotely sensed vegetation greenness with climate covariates at a daily scale from 1981 to 2014 across the entire conterminous United States. Finally, I used the BHST-LSP to project vegetation phenology from 2020 to 2099 under two climate change scenarios. This dissertation contributed in-depth understanding of the LSP and its environmental cues in both natural and human dominated ecosystems. Results from this dissertation provide strong evidences for adoption of climate change mitigation policies and immediate management measures to prevent severe adverse impacts of global warming and urbanization from disrupting vital ecosystem services and functions.Doctor of Philosoph

Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions

Climate Change and Environmental Sustainability-Volume 4

Anthropogenic activities are significant drivers of climate change and environmental degradation. Such activities are particularly influential in the context of the land system that is an important medium connecting earth surface, atmospheric dynamics, ecological systems, and human activities. Assessment of land use land cover changes and associated environmental, economic, and social consequences is essential to provide references for enhancing climate resilience and improving environmental sustainability. On the one hand, this book touches on various environmental topics, including soil erosion, crop yield, bioclimatic variation, carbon emission, natural vegetation dynamics, ecosystem and biodiversity degradation, and habitat quality caused by both climate change and earth surface modifications. On the other hand, it explores a series of socioeconomic facts, such as education equity, population migration, economic growth, sustainable development, and urban structure transformation, along with urbanization. The results of this book are of significance in terms of revealing the impact of land use land cover changes and generating policy recommendations for land management. More broadly, this book is important for understanding the interrelationships among life on land, good health and wellbeing, quality education, climate actions, economic growth, sustainable cities and communities, and responsible consumption and production according to the United Nations Sustainable Development Goals. We expect the book to benefit decision makers, practitioners, and researchers in different fields, such as climate governance, crop science and agricultural engineering, forest ecosystem, land management, urban planning and design, urban governance, and institutional operation.Prof. Bao-Jie He acknowledges the Project NO. 2021CDJQY-004 supported by the Fundamental Research Funds for the Central Universities and the Project NO. 2022ZA01 supported by the State Key Laboratory of Subtropical Building Science, South China University of Technology, China. We appreciate the assistance of Mr. Lifeng Xiong, Mr. Wei Wang, Ms. Xueke Chen, and Ms. Anxian Chen at School of Architecture and Urban Planning, Chongqing University, China

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Effects of tropospheric ozone pollution on net primary productivity and carbon storage in terrestrial ecosystems of China

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): D22S09, doi:10.1029/2007JD008521.We investigated the potential effects of elevated ozone (O3) along with climate variability, increasing CO2, and land use change on net primary productivity (NPP) and carbon storage in China's terrestrial ecosystems for the period 1961–2000 with a process-based Dynamic Land Ecosystem Model (DLEM) forced by the gridded data of historical tropospheric O3 and other environmental factors. The simulated results showed that elevated O3 could result in a mean 4.5% reduction in NPP and 0.9% reduction in total carbon storage nationwide from 1961 to 2000. The reduction of carbon storage varied from 0.1 Tg C to 312 Tg C (a decreased rate ranging from 0.2% to 6.9%) among plant functional types. The effects of tropospheric O3 on NPP were strongest in east-central China. Significant reductions in NPP occurred in northeastern and central China where a large proportion of cropland is distributed. The O3 effects on carbon fluxes and storage are dependent upon other environmental factors. Therefore direct and indirect effects of O3, as well as interactive effects with other environmental factors, should be taken into account in order to accurately assess the regional carbon budget in China. The results showed that the adverse influences of increasing O3 concentration across China on NPP could be an important disturbance factor on carbon storage in the near future, and the improvement of air quality in China could enhance the capability of China's terrestrial ecosystems to sequester more atmospheric CO2. Our estimation of O3 impacts on NPP and carbon storage in China, however, must be used with caution because of the limitation of historical tropospheric O3 data and other uncertainties associated with model parameters and field experiments.This research is funded by NASA Interdisciplinary Science Program (NNG04GM39C)

Rural vegetation characteristics and biodiversity conservation strategies in the Yangtze river delta urban agglomeration

The integrated green development of the Yangtze River Delta is a long-term major strategy. The rural ecological status is the basis for the healthy development of the Yangtze River Delta urban agglomeration. This study focuses on the expansion of demand for rural vegetation biodiversity conservation and ecosystem service functions in the Yangtze River Delta urban agglomeration. A total of 256 plant communities in 28 villages were investigated, and the composition of family, genera and species and life forms were analyzed respectively; through clustering, 47 community types and 8 vegetation types were divided, and the composition and distribution characteristics of plant community were analyzed; and then it analyzed the species biodiversity and compared the characteristics of rural transformation in the Yangtze River Delta with the vegetation characteristics of urban areas and natural areas. Finally, specific countermeasures for biodiversity conservation and reconstruction of the Yangtze River Delta rural vegetation was proposed in terms of species protection, habitat maintenance, plant community conservation, and ecological aesthetics guidance

Exacerbated grassland degradation and desertification in Central Asia during 2000-2014.

Grassland degradation and desertification is a complex process, including both state conversion (e.g., grasslands to deserts) and gradual within-state change (e.g., greenness dynamics). Existing studies hardly separated the two components and analyzed it as a whole based on time series vegetation index data, which cannot provide a clear and comprehensive picture for grassland degradation and desertification. Here we propose an integrated assessment strategy, by considering both state conversion and within-state change of grasslands, to investigate grassland degradation and desertification process in Central Asia. First, annual maps of grasslands and sparsely vegetated land were generated to track the state conversions between them. The results showed increasing grasslands were converted to sparsely vegetated lands from 2000 to 2014, with the desertification region concentrating in the latitude range of 43-48° N. A frequency analysis of grassland vs. sparsely vegetated land classification in the last 15 yr allowed a recognition of persistent desert zone (PDZ), persistent grassland zone (PGZ), and transitional zone (TZ). The TZ was identified in southern Kazakhstan as one hotspot that was unstable and vulnerable to desertification. Furthermore, the trend analysis of Enhanced Vegetation Index during thermal growing season (EVITGS ) was investigated in individual zones using linear regression and Mann-Kendall approaches. An overall degradation across the area was found; moreover, the second desertification hotspot was identified in northern Kazakhstan with significant decreasing in EVITGS , which was located in PGZ. Finally, attribution analyses of grassland degradation and desertification were conducted by considering precipitation, temperature, and three different drought indices. We found persistent droughts were the main factor for grassland degradation and desertification in Central Asia. Considering both state conversion and gradual within-state change processes, this study provided reference information for identification of desertification hotspots to support further grassland degradation and desertification treatment, and the method could be useful to be extended to other regions