An ecosystem-based management approach (EBM) is suggested as one solution to help to tackle environmental challenges facing worldwide farming systems whilst ensuring socio-economic demands are met. Despite its usefulness, the application of this approach at the farm-scale presents several implementation problems, including the difficulty of (a) incorporating the concept of ecosystem services (ES) into agricultural land use decision-making and (b) involving the farmer in the planning process. This study aims to propose a solution to overcome these challenges by utilising a geodesign framework and EBM approach to plan and design a sustainable multifunctional agricultural landscape at the farm scale. We demonstrate how the proposed approach can be applied to plan and design multifunctional agricultural landscapes that offer improved sustainability, using a New Zealand hill country farm as a case study. A geodesign framework is employed to generate future land use and management scenarios for the study area, visualize changes, and assess the impacts of future land use on landscape multifunctionality and the provision of associated ES and economic outcomes. In this framework, collaboration with the farmer was carried out to obtain farm information and co-design the farmed landscapes. The results from our study demonstrate that farmed landscapes where multiple land use/ land cover types co-exist can provide a wide range of ES and therefore, meet both economic and environmental demands. The assessment of impacts for different land use change scenarios demonstrates that land use change towards increasing landscape diversity and complexity is a key to achieving more sustainable multifunctional farmed landscapes. The integration of EBM and geodesign, is a transdisciplinary approach that can help farmers target land use and management decisions by considering the major ES that are, and could be, provided by the landscapes in which these farm systems are situated, therefore maximising the potential for beneficial outcomes.

Land Cover

10.1016/j.ecolind.2022.109762

Cite

Citations (9)

Location-based environmental factors contributing to rainfall-triggered debris flows in the Ba river catchment, northwest Viti Levu island, Fiji

Landslides (2017)

Mark Stephens John H. Lowry Arishma R. Ram

Mass movement

Elevation (ballistics)

Debris flow

10.1007/s10346-017-0918-4

Cite

Citations (15)

Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: A case study of the Southwest Regional Gap Analysis Project (SWReGAP)

Remote Sensing of Environment (2007)

John H. Lowry R. Douglas Ramsey Kathryn A. Thomas Donald L. Schrupp Todd Sajwaj

Ecoregion

Land Cover

Cadastre

Gap analysis (conservation)

10.1016/j.rse.2006.11.008

Cite

Citations (120)

Evaluating geospatial education provision: a case study of Aotearoa New Zealand

Journal of Geography in Higher Education (2024)

Mairéad de Róiste Scott C. Pool John H. Lowry

Aotearoa

10.1080/03098265.2024.2403075

Cite

Citations (0)

An indicator-based GIS approach for assessing a community’s vulnerability to mobility-related disruptions caused by floods

International Journal of Disaster Risk Reduction (2025)

Shashini Sanjeewani Ranabahu John H. Lowry Muhammad Imran

Vulnerability

10.1016/j.ijdrr.2025.105342

Cite

Citations (0)

Comparing spatial metrics that quantify urban form

Computers Environment and Urban Systems (2013)

John H. Lowry Michael Lowry

Urban sprawl

Smart growth

Urban Policy

Salt lake

10.1016/j.compenvurbsys.2013.11.005

Cite

Citations (110)

An Ecological Framework for Evaluating Map Errors Using Fuzzy Sets

Photogrammetric Engineering & Remote Sensing (2008)

John H. Lowry R. Douglas Ramsey Lisa Langs Stoner Jessica L. Kirby Karsten Schulz

The use of fuzzy sets to assess uncertainty in land-use/cover maps provides a robust conceptual framework for examining unique characteristics of map error. By recognizing the possibility of gradations of error, fuzzy sets can be used to assess errors due to class similarity, or the sensitivity of the map legend to class boundaries. Building on the theoretical work of Gopal and Woodcock (1994), we present a practical methodology for assessing map errors using fuzzy sets. A key component of our methodology focuses on improving the decision-making process map experts assume when conducting a fuzzy set assessment of map errors. Using an ecological context to define varying levels of land-cover class similarity, we demonstrate how a decision framework guides the map experts’ decisions and provides a more meaningful assessment of map errors. Our methodology differs from traditional fuzzy set error assessment methods in that the map expert evaluates misclassifications within the error matrix (off-diagonal cells) rather than individual reference sites. Advantages to a matrixbased approach include a reduction in the time required by map experts to evaluate map errors, and a relatively simple means of conveying map error information to the map user. We conclude that establishing criteria for determining multiple set memberships in a fuzzy set error assessment is an important methodological procedure that is commonly overlooked. Our methodology, designed to explicitly identify land-cover class similarities based on ecological criteria, serves as a practical example of how to address this issue.

10.14358/pers.74.12.1509

Cite

Citations (9)

A comparison between cluster busting technique and a classification tree algorithm of a moderate resolution imaging spectrometer (MODIS) land cover map of Honduras

Geocarto International (2011)

Samuel Rivera John H. Lowry Alexander J. Hernández R. Douglas Ramsey R. Delgado Lezama

A national land cover map derived from moderate resolution imaging spectrometer (MODIS) imagery products was developed for Honduras, Central America. We compared two methods of image classification: a cluster busting (CB) classification technique and a classification and regression tree (CART) algorithm. Field data samples were used to validate the resulting classifications. Inthe classification process, we used: a Google Earth™ sampling scheme, a time series of MODIS's Enhanced Vegetation Index (EVI) and digital elevation data(shuttle radar topography mission, SRTM). The CART classification method provided a more accurate classification (Kappa coefficient, K = 74%, overall model accuracy = 79.6%) while compared to the CB classification (Kappa coefficient, K = 9%, overall model accuracy = 25.1%). The findings are useful to design more accurate MODIS classification protocols in tropical countries.

Land Cover

Cohen's kappa

Imaging spectrometer

Contextual image classification

10.1080/10106049.2011.622050

Cite

Citations (5)

Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country

The Science of The Total Environment (2021)

Duy X. Tran Diane Pearson Alan Palmer John H. Lowry David A. Gray

Land Cover

Explanatory power

Spatial heterogeneity

Ordinary least squares

10.1016/j.scitotenv.2021.152126

Cite

Citations (66)