274 research outputs found
Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system
A globally integrated carbon observation and analysis system is needed to
improve the fundamental understanding of the global carbon cycle, to improve
our ability to project future changes, and to verify the effectiveness of
policies aiming to reduce greenhouse gas emissions and increase carbon
sequestration. Building an integrated carbon observation system requires
transformational advances from the existing sparse, exploratory framework
towards a dense, robust, and sustained system in all components:
anthropogenic emissions, the atmosphere, the ocean, and the terrestrial
biosphere. The paper is addressed to scientists, policymakers, and funding
agencies who need to have a global picture of the current state of the
(diverse) carbon observations. We identify the current state of carbon
observations, and the needs and notional requirements for a global integrated
carbon observation system that can be built in the next decade. A key
conclusion is the substantial expansion of the ground-based observation
networks required to reach the high spatial resolution for CO<sub>2</sub> and
CH<sub>4</sub> fluxes, and for carbon stocks for addressing policy-relevant
objectives, and attributing flux changes to underlying processes in each
region. In order to establish flux and stock diagnostics over areas such as
the southern oceans, tropical forests, and the Arctic, in situ observations
will have to be complemented with remote-sensing measurements. Remote sensing
offers the advantage of dense spatial coverage and frequent revisit. A key
challenge is to bring remote-sensing measurements to a level of long-term
consistency and accuracy so that they can be efficiently combined in models
to reduce uncertainties, in synergy with ground-based data. Bringing tight
observational constraints on fossil fuel and land use change emissions will
be the biggest challenge for deployment of a policy-relevant integrated
carbon observation system. This will require in situ and remotely sensed data
at much higher resolution and density than currently achieved for natural
fluxes, although over a small land area (cities, industrial sites, power
plants), as well as the inclusion of fossil fuel CO<sub>2</sub> proxy measurements
such as radiocarbon in CO<sub>2</sub> and carbon-fuel combustion tracers.
Additionally, a policy-relevant carbon monitoring system should also provide
mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up
(surface-based) flux estimates across the range of spatial and temporal
scales relevant to mitigation policies. In addition, uncertainties for each
observation data-stream should be assessed. The success of the system will
rely on long-term commitments to monitoring, on improved international
collaboration to fill gaps in the current observations, on sustained efforts
to improve access to the different data streams and make databases
interoperable, and on the calibration of each component of the system to
agreed-upon international scales
Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review
Bark beetle outbreaks can result in a devastating impact on forest ecosystem
processes, biodiversity, forest structure and function, and economies. Accurate
and timely detection of bark beetle infestations is crucial to mitigate further
damage, develop proactive forest management activities, and minimize economic
losses. Incorporating remote sensing (RS) data with machine learning (ML) (or
deep learning (DL)) can provide a great alternative to the current approaches
that rely on aerial surveys and field surveys, which are impractical over vast
geographical regions. This paper provides a comprehensive review of past and
current advances in the early detection of bark beetle-induced tree mortality
from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We
parse recent literature according to bark beetle species & attack phases, host
trees, study regions, imagery platforms & sensors, spectral/spatial/temporal
resolutions, spectral signatures, spectral vegetation indices (SVIs), ML
approaches, learning schemes, task categories, models, algorithms,
classes/clusters, features, and DL networks & architectures. This review
focuses on challenging early detection, discussing current challenges and
potential solutions. Our literature survey suggests that the performance of
current ML methods is limited (less than 80%) and depends on various factors,
including imagery sensors & resolutions, acquisition dates, and employed
features & algorithms/networks. A more promising result from DL networks and
then the random forest (RF) algorithm highlighted the potential to detect
subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral
regions.Comment: Under review, 33 pages, 5 figures, 8 Table
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