Continuous monitoring of forest change dynamics with satellite time series

Decuyper, Mathieu; Chávez, Roberto O.; Lohbeck, Madelon; Lastra, José A.; Tsendbazar, Nandin‐Erdene; Hackländer, Julia; Herold, Martin; Vågen, Tor-Gunnar

doi:10.1016/j.rse.2021.112829

Cited by 56 publications

(50 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is very challenging to repeatedly produce up-to-date and accurate maps and obtain up-to-date and accurate information, especially at large scales, for many important applications and monitoring systems due to time, effort, and cost. As larger volumes of geospatial data become available, an ever-increasing number of techniques for analyzing them have increased the number and scope of monitoring applications (e.g., global water mapping [32], forest and deforest monitoring [33], and global climate change research [34]). Downloading, analyzing, and managing a multi-decadal time series of satellite imagery over large areas is not practical using desktop computing resources [35].…”

Section: Scope and Intended Audiencementioning

confidence: 99%

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

View full text Add to dashboard Cite

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

show abstract

Section: Scope and Intended Audiencementioning

confidence: 99%

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The KDEs define each anomaly value within the frequency distribution of the observed values at a given DOY. An anomaly is any value outside the 90% probability distribution of the referenced frequency distribution (RDF) (Decuyper et al, 2022 ). The curve is therefore specific to any pixel and provides a kind of fingerprint made up mainly of the specific species combination and the forest density, acting as a baseline.…”

Section: Methodsmentioning

confidence: 99%

Distinguishing Abrupt and Gradual Forest Disturbances With MODIS-Based Phenological Anomaly Series

Gnilke

Sanders

2022

Front. Plant Sci.

View full text Add to dashboard Cite

Capturing forest disturbances over time is increasingly important to determine the ecosystem's capacity to recover as well as aiding a timely response of foresters. With changes due to climate change increasing the frequencies, a better understanding of forest disturbances and their role in historical development is needed to, on the one hand, develop forest management approaches promoting ecosystem resilience and, on the other hand, provide quick and spatially explicit information to foresters. A large, publicly available satellite imagery spanning more than two decades for large areas of the Earth's surface at varying spatial and temporal resolutions represents a vast, free data source for this. The challenge is 2-fold: (1) obtaining reliable information on forest condition and development from satellite data requires not only quantification of forest loss but rather a differentiated assessment of the extent and severity of forest degradation; (2) standardized and efficient processing routines both are needed to bridge the gap between remote-sensing signals and conventional forest condition parameters to enable forest managers for the operational use of the data. Here, we investigated abiotic and biotic disturbances based on a set of ground validated occurrences in various forest areas across Germany to build disturbance response chronologies and examine event-specific patterns. The proposed workflow is based on the R-package “npphen” for non-parametric vegetation phenology reconstruction and anomaly detection using MODIS EVI time series data. Results show the potential to detect distinct disturbance responses in forest ecosystems and reveal event-specific characteristics. Difficulties still exist for the determination of, e.g., scattered wind throw, due to its subpixel resolution, especially in highly fragmented landscapes and small forest patches. However, the demonstrated method shows potential for operational use as a semi-automatic system to augment terrestrial monitoring in the forestry sector. Combining the more robust EVI and the assessment of the phenological series at a pixel-by-pixel level allows for a changing species cover without false classification as forest loss.

show abstract

“…( 5)-( 7), Kauth & Thomas, 1976). The GEE code developed for this study was a modification of the original by Decuyper et al (2022).…”

Section: Landsat-8 Oli Data and Spectral Indicesmentioning

confidence: 99%