Patterns in the Landscape – Analyses of Cause and Effect

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For two decades, USGS scientists with the Land Cover Trends team have used satellite data to study landscape change across the United States. Increasingly, research is focused on understanding why change occurs. Insights into the underlying causes of shifts in land use and land cover (LULC) will allow managers and stakeholders to make more informed decisions about how to respond to future environmental and climatic conditions.

USGS investigations into the causes of land change are called ‘driving forces’ studies.  Driving forces are different processes--economic, demographic, technological, institutional, cultural, environmental, and climatic--that transform or maintain LULC.

The Land Cover Trends team is now undertaking research that quantifies the complex spatial and temporal relationships between land change and its driving forces in order to develop more accurate land-cover assessments and forecasts.

The project goal is to develop and implement a systematic approach for identifying causes of land change at regional and national scales. A key data resource for this effort is the new generation of annual land use/land change (LULC) maps produced by the USGS Land Change Monitoring, Assessment, and Projection (LCMAP) project for 1985-2014. The availability of annual data will allow USGS researchers to link land change rates and patterns to driving forces with unprecedented precision. The Land Cover Trends team will leverage this detail to isolate abiotic influences, explore land change sensitivity to extreme weather events and sustained climate changes, and investigate effects across a wide range of explanatory parameters.

Method development will commence at the regional scale. Our initial focus has been centered in the Pacific Northwest. This focus provides the opportunity to test the efficacy of the methodology prior to a national-scale rollout, and to tailor outreach products to regional resource managers, researchers, and local governments for assessment and utilization.

Relevance: 

The benefits of identifying the rates and causes of LULC change are far-reaching.  More informed understanding of drivers will allow insights into the environmental impacts of past change and more precise estimates of future responses to specific processes and climate scenarios. The use of scenario-based LULC projections enables an assessment of the interactions among climate, land-use, policy, and socioeconomic driving forces. This information can facilitate land-use planning and restoration efforts.

Diagram of forest change

Diagram illustrating potential changes to forest land cover.(Credit: Christopher Soulard, USGS. Public domain.)

 

Who benefits from our science?

The long-range goal of the Land Cover Trends project is to deliver information about the driving forces of land cover change that will be applicable for a wide range of scientific, land management, planning, and environmental conservation purposes.  

Resource managers. non-governmental organizations, and private stakeholders are increasingly interested in knowing how altered environmental conditions will affect priorities such as preserving floral and faunal species of concern, mitigating the effects of development projects, sustaining agricultural productivity in changing landscapes, and planning for protection of communities in fire-prone areas. Understanding the factors that lead to land use change provides an opportunity to identify, isolate, and manage risk by highlighting areas of particular vulnerability or resistance to change under different future scenarios.

Land change driver workflow diagram

Conceptual diagram illustrating methods for analyzing land change rates and causes. Results will be applied to project future land changes. (Credit: Christopher Soulard, USGS. Public domain.)

 

Task 1

A necessary foundation for the study of the causes of land change is a thorough and comprehensive assessment of landscape variations over time. Land Cover Trends research will determine the proximate causes of land change through analysis of past and present landscape trajectories extracted from LCMAP annual datasets. These investigations will provide insights into the timing of LULC changes or events, distinguish natural from anthropogenic processes, and monitor short-lived alterations and long-term trends. Results will serve as direct inputs into subsequent analyses.

The evaluation of LCMAP land change products will involve determining the most effective conversion of LCMAP-generated annual maps and statistics into analysis-ready data. To learn how data and corresponding land change statistics can be appropriately used, the Land Cover Trends team will compare LCMAP maps and statistics to an array of verification datasets, including satellite imagery, LCMAP validation points, and independent LULC data and change statistics.

 

Task 2

A comprehensive understanding of the rates and patterns of land change reported by LCMAP will facilitate insight into the underlying causes of such change. The primary objective of the Land Cover Trends project is to develop a quantitative approach to estimate the major drivers of land change in the United States from 1985-2014, such as water use and availability, weather events, climatic conditions, population pressure, public policy, economics, and technological advances. A related goal is the consideration of how drivers interact to affect regional variability of land-use change characteristics.

To advance research into LULC change drivers beyond qualitative or anecdotal observations of individual forcing agents, the Land Cover Trends team will develop and apply a comprehensive, quantitative approach that can be flexibly applied across biome, regional, and national scales. The effort will incorporate information and data applicable to smaller-scale influences, such as local economic, development, and land preservation policies; invasive species or pest infestations; weather or climate variability; and land management practices. This scalable framework will allow USGS researchers to isolate abiotic influences, explore climate sensitivity to both long term trends and extreme weather events, and investigate merged variables in multiple regression analyses.