Change Analysis Overview

Principles

Definition

Change analysis is a research methodology focused on systematically identifying, quantifying, and interpreting alterations in phenomena, systems, or data over temporal periods or across varying conditions. It investigates the patterns, drivers, and consequences of these transformations to understand dynamic processes and inform decision-making or theoretical development.

Ontological type

Core Methods

Impact Evaluation

Use Cases

Key developments

Key Figures

Sensor-aware Change Frameworks era

Igor V. Nikiforov [1] is associated with the Sensor-aware Change Frameworks era, with affiliations at Université de Lille [3] and Université de Technologie de Troyes [4]. His key contributions in this era include the Detection of Abrupt Changes: Theory and Applications [7], which introduced formal theory and methods for abrupt-change detection that underpin multivariate change analysis in SAR and multispectral time series. Michèle Basseville [2] has ties to MIT [5] and Université de Rennes [6] in this era. Her key contributions in this era, through the same Detection of Abrupt Changes: Theory and Applications [7], advanced the formal theory of abrupt-change detection and its applications, informing experimental designs and inference under limited ground truth.

Cross-sensor Multiscale Methods era

Alan Belward [1] is associated with institutions such as Boston University [3] and Jilin University [4] in this era. His key contribution from the era is the GLC2000: a new approach to global land cover mapping from Earth observation data [5], a pioneering framework that advanced consistent, scalable land-cover products across sensors and regions. Zhe Zhu [2] is affiliated with Boston University [3] and Jilin University [4] in this era. His key contribution from the era is the paper 'Continuous change detection and classification of land cover using all available Landsat data' [6], which introduced continuous change detection and classification across the Landsat archive and enabled scalable, multitemporal monitoring for regional change mapping.

Cloud-based Deep Time-series era

Warren B. Cohen [1], whose work spanned the University of Cambridge [3] and Oregon State University [4] during this era, helped shape cloud-based change monitoring. His key contributions include the Implementation of the LandTrendr Algorithm on Google Earth Engine [7], the Continuous monitoring of land disturbance based on Landsat time series [8], and How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms? [9], which collectively advanced scalable, reproducible monitoring and cross-method comparability essential to this era. Zhe Zhu [2], affiliated with Boston University [5] and Northwest A&F University [6] during this period, helped advance time-series based change analysis. His key contributions include the Continuous monitoring of land disturbance based on Landsat time series [8], Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014) [10], and Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data [11], which collectively enabled multi-sensor integration, longer temporal coverage, and more robust, policy-relevant assessments.