49 research outputs found
Ecosystem aboveground structures of an open-canopy black spruce forest in interior Alaska for ecosystem modeling
Recent warming trends in the Arctic and boreal regions far exceed the global average, and this trend is likely to continue in the future. To establish accurate benchmarks for models and represent the current status of carbon stocks and flows, it is essential to provide detailed descriptions of forest ecosystem structures, including understory plant communities alongside carbon flux data. In this data paper, we present a synthesis data set of aboveground forest structural characteristics. Field campaigns were performed from 2010 to 2018 at the Poker Flat Research Range of the Geophysical Institute of the University of Alaska, Fairbanks, located on the outskirts of Fairbanks, AK, USA. The forest is a sparse evergreen needle-leaf forest. The field campaigns include tree census surveys in the years 2010 and 2014, direct measurement of tree aboveground biomass and their vertical profiles in 2012, tree leaf area index (LAI) measurement using a non-destructive method in 2018, and biomass and LAI survey of forest floor vegetation through direct sampling in 2018. The ecosystem structure data sets described in this paper are helpful for the calibration of the forest stand representations in the ecosystem and land surface models and for the validation of the simulated forest growth and stand structures. The data are available for download from the Japanese National Institute of Polar Research Arctic Data archive System, listed under Kobayashi et al. 2023
Growth trends and site productivity in boreal forests under management and environmental change: insights from long-term surveys and experiments in Sweden
Under a changing climate, current tree and stand growth information is indispensable to the carbon sink strength of boreal forests. Important questions regarding tree growth are to what extent have management and environmental change influenced it, and how it might respond in the future. In this thesis, results from five studies (Papers I-V) covering growth trends, site productivity, heterogeneity in managed forests and potentials for carbon storage in forests and harvested wood products via differing management strategies are presented. The studies were based on observations from national forest inventories and long-term experiments in Sweden. The annual height growth of Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) had increased, especially after the millennium shift, while the basal area growth remains stable during the last 40 years (Papers I-II). A positive response on height growth with increasing temperature was observed. The results generally imply a changing growing condition and stand composition. In Paper III, yield capacity of conifers was analysed and compared with existing functions. The results showed that there is a bias in site productivity estimates and the new functions give better prediction of the yield capacity in Sweden. In Paper IV, the variability in stand composition was modelled as indices of heterogeneity to calibrate the relationship between basal area and leaf area index in managed stands of Norway spruce and Scots pine. The results obtained show that the stand structural heterogeneity effects here are of such a magnitude that they cannot be neglected in the implementation of hybrid growth models, especially those based on light interception and light-use efficiency. In the long-term, the net climate benefits in Swedish forests may be maximized through active forest management with high harvest levels and efficient product utilization, compared to increasing carbon storage in standing forests through land set-asides for nature conservation (Paper V). In conclusion, this thesis offers support for the development of evidence-based policy recommendations for site-adapted and sustainable management of Swedish forests in a changing climate
Remote Sensing Monitoring of Land Surface Temperature (LST)
This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research
Detecting and mapping forest nutrient deficiencies: eucalyptus variety (Eucalyptus grandis x and Eucalyptus urophylla) trees in KwaZulu-Natal, South Africa.
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Abstract available in PDF
Mesoscale Wind and Temperature Changes over Peatlands of the Hudson Bay Lowlands: impacts on the Surface Energy Balance and Net CO2 Exchange
Although northern peatlands in general are currently a net carbon sink, there is still considerable uncertainty in the long-term combined response of plant productivity and ecosystem respiration to global warming and moisture changes. The Hudson Bay Lowlands (HBL) region of Canada is the second largest peat-accumulating complex in the world that is strongly influenced by the cold air masses originating off the Hudson Bay. Recent warming has caused observed changes in the sea ice dynamics and energy budget of the Hudson Bay, yet it is presently unknown how these climatic changes in the Bay will impact the surface energy and carbon balance of the adjacent HBL. As a globally significant peatland carbon pool, the HBL will play an important role in future climate warming and permafrost carbon feedback. The primary aim of this study is to improve our understanding of the warming-induced changes in the advective influence of the Hudson Bay and its linkage to the changes in the surface energy and carbon balance over peatlands of the HBL. I use a combined model and assimilated climate dataset to investigate the mesoscale wind and temperature changes in the HBL and their impacts on the surface energy balance (1979–2018). Furthermore, I employ a satellite data-driven light-use efficiency model, calibrated and validated with eddy covariance tower measurements at a fen and bog to examine the response of net ecosystem CO2 exchange to climatic changes (2000–2019). The results reveal that differential rates of warming between offshore and onshore winds have produced significant changes in the advective role of the Hudson Bay as evident in the increased frequency and strength of onshore winds. Also, the results show contrasting net CO2 exchange between the fen and bog sites. The anomalies in gross primary production and ecosystem respiration were associated with strong trends in temperature and moisture, and the Hudson Bay had a more pronounced advective influence on peatland respiration than photosynthesis
Advances in Remote Sensing-based Disaster Monitoring and Assessment
Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones
Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.
With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties.
The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0
Remote Sensing of Plant Biodiversity
At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally.
This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing
instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution.
The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity.
Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely.
Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON).
This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing
Toward a better understanding of changes in Northern vegetation using long-term remote sensing data
Cascading consequences of recent changes in the physical environment of northern lands associated with rapid warming have affected a broad range of ecosystem processes, particularly, changes in structure, composition, and functioning of vegetation. Incomplete understanding of underlying processes driving such changes is the primary motivation for this research. We report here the results of three studies that use long-term remote sensing data to advance our knowledge of spatiotemporal changes in growing season, greenness and productivity of northern vegetation. First, we improve the remote sensing-based detection of growing season by fusing vegetation greenness, snow and soil freeze/thaw condition. The satellite record reveals extensive lengthening trends of growing season and enhanced annual total greenness during the last three decades. Regionally varying seasonal responses are linked to local climate constraints and their relaxation. Second, we incorporate available land surface histories including disturbances and human land management practices to understand changes in remotely sensed vegetation greenness. This investigation indicates that multiple drivers including natural (wildfire) and anthropogenic (harvesting) disturbances, changing climate and agricultural activities govern the large-scale greening trends in northern lands. The timing and type of disturbances are important to fully comprehend spatially uneven vegetation changes in the boreal and temperate regions. In the final part, we question how photosynthetic seasonality evolved into its current state, and what role climatic constraints and their variability played in this process and ultimately in the carbon cycle. We take the ‘laws of minimum’ as a basis and introduce a new framework where the timing of peak photosynthetic activity (DOYPmax) acts as a proxy for plants adaptive state to climatic constraints on their growth. The result shows a widespread warming-induced advance in DOYPmax with an increase of total gross primary productivity across northern lands, which leads to an earlier phase shift in land-atmosphere carbon fluxes and an increase in their amplitude. The research presented in this dissertation suggests that understanding past, present and likely future changes in northern vegetation requires a multitude of approaches that consider linked climatic, social and ecological drivers and processes