Landscape Processes Overview

Principles

Definition

Landscape processes is the academic concept and research field that studies the dynamic interactions, flows, and transformations of energy, matter, and organisms across heterogeneous spatial configurations of the Earth's surface. This concept investigates the complex interplay of physical, biological, and human-induced factors that shape landscape patterns and drive environmental change over various scales, providing a foundational understanding for ecological analysis, environmental management, and sustainable land use planning.

Ontological type

Core Processes

Theoretical Frameworks

Human–Environment Interactions

Key developments

Key Figures

Spatial Structure Paradigm era

Pierre Legendre [1], associated with University of Colorado Boulder [2] and Lund University [3], was a central figure in the Spatial Structure Paradigm era. His 1992 paper Partialling out the Spatial Component of Ecological Variation [4] and his 1993 paper Spatial Autocorrelation: Trouble or New Paradigm? [5] reframed ecological data by separating spatial structure from environmental variation and by treating spatial autocorrelation as a core analytic concept, catalyzing landscape-scale inference. These advances promoted patch-based metrics, early GIS tools, and scale-aware analyses that linked landscape structure to hydrology, erosion, and species distributions in this era. Legendre's work during this era helped establish riparian corridors and land–water interfaces as empirical domains for applying structure–process linkages.

Cross-scale Connectivity era

Lenore Fahrig [1] is an influential ecologist associated with Université du Québec à Montréal [3] and Carleton University [4] during the Cross-scale Connectivity era. Her key contributions in this era include the examination of habitat fragmentation on biodiversity, as demonstrated by the 2003 paper Effects of Habitat Fragmentation on Biodiversity [7], which showed how fragmentation alters dispersal and community structure and underscored connectivity as a central mechanism for biodiversity outcomes. Limin Yang [2] is linked to Nanjing Agricultural University [5] and the University of Hong Kong [6] during this period. Yang's contributions include advancing land cover change assessment through the 2015 NLCD completion paper Completion of the 2011 National Land Cover Database for the Conterminous United States - Representing a Decade of Land Cover Change Information [8], a resource that supports cross-scale connectivity analyses and promotes reproducible landscape assessment across disciplines.

Global Remote-sensing Analytics era

The Global Remote-sensing Analytics era (2021–2024) features representative authors such as Matthew Hansen for Landsat-based, high-resolution global land-cover change and urban expansion monitoring, and Xiaoxiang Zhu for scalable, time-series land-cover mapping and deep-learning workflows on near-global satellite data. Hansen’s Global Forest Change dataset and analyses quantify fragmentation and deforestation drivers at continental scales, while near-global digital elevation models contribute terrain-aware baselines for sediment transfer and landscape connectivity analyses across large extents. Zhu’s work demonstrates automated, reproducible pipelines, transfer learning, and Google Earth Engine-enabled workflows that operationalize global change analyses for resilience planning and policy-relevant scenario testing. Additional contributors such as Graeme Foody have advanced accuracy assessment and uncertainty quantification for machine-learning-derived landscape products, strengthening credibility and enabling cross-scale comparisons in this era.