Mapping the heterogeneity of natural and semi-natural landscapes

Ali, Amjad; Bie, C.A.J.M. de; Skidmore, Andrew K.; Scarrott, Rory; Lymberakis, Petros

doi:10.1016/j.jag.2013.06.007

Cited by 20 publications

(23 citation statements)

References 59 publications

(96 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Phenological patterns extracted from continuous Earthobservation-based NDVI have been widely used to map LULCC over long time periods, given the lack of higher spatial and spectral resolution data before the MODIS era (Ali et al, 2014;de Bie et al, 2012). These studies show that vegetation periodicity is highly variable for a given land cover type and that long-term averages of phenological predictors are more reliable for mapping LULCC.…”

Section: Discussionmentioning

confidence: 99%

Continuous and consistent land use/cover change estimates using socio-ecological data

et al. 2017

View full text Add to dashboard Cite

Abstract.A growing body of research shows the importance of land use/cover change (LULCC) on modifying the Earth system. Land surface models are used to stimulate land-atmosphere dynamics at the macroscale, but model bias and uncertainty remain that need to be addressed before the importance of LULCC is fully realized. In this study, we propose a method of improving LULCC estimates for land surface modeling exercises. The method is driven by projectable socio-ecological geospatial predictors available seamlessly across sub-Saharan Africa and yielded continuous (annual) estimates of LULCC at 5 km × 5 km spatial resolution. The method was developed with 2252 sample area frames of 5 km × 5 km consisting of the proportion of several land cover types in Kenya over multiple years. Forty-three socio-ecological predictors were evaluated for model development. Machine learning was used for data reduction, and simple (functional) relationships defined by generalized additive models were constructed on a subset of the highest-ranked predictors (p ≤ 10) to estimate LULCC. The predictors explained 62 and 65 % of the variance in the proportion of agriculture and natural vegetation, respectively, but were less successful at estimating more descriptive land cover types. In each case, population density on an annual basis was the highest-ranked predictor. The approach was compared to a commonly used remote sensing classification procedure, given the wide use of such techniques for macroscale LULCC detection, and outperformed it for each land cover type. The approach was used to demonstrate significant trends in expanding (declining) agricultural (natural vegetation) land cover in Kenya from 1983 to 2012, with the largest increases (declines) occurring in densely populated high agricultural production zones. Future work should address the improvement (development) of existing (new) geospatial predictors and issues of model scalability and transferability.Published by Copernicus Publications on behalf of the European Geosciences Union.

show abstract

Section: Discussionmentioning

confidence: 99%

Continuous and consistent land use/cover change estimates using socio-ecological data

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Por un lado, la clasificación (ALI et al, 2013;ATZBERGER;EILERS, 2011;BARRAZA et al, 2013;GANDINI et al, 2012;GONZÁLEZ LOYARTE et al, 2010;GANDINI, 2013b;PARUELO et al, 2001;PARUELO, 2008;VÁZQUEZ et al, 2013). IVITS et al (2013) articulando estas metodologías obtuvieron una clasificación de unidades funcionales de ecosistemas de Europa mucho más robusta que la subdivisión original en eco-zonas, por lo que consideramos que ambas metodologías o enfoques deben ser complementarias para lograr una mayor comprensión acerca de la heterogeneidad espacial, funcional y temporal de los paisajes estudiados.…”

Section: Coberturaunclassified

“…En particular, el NDVI (Normalized Difference Vegetation Index) es uno de los más utilizados ya que es un estimador lineal de la radiación fotosintéticamente activa interceptada por la vegetación y su mapeo permite describir los patrones regionales de productividad primaria neta y los diferentes atributos del comportamiento fenológico a partir de la curva anual de crecimiento (ALCARAZ-SEGURA et al, 2009;JÖNSSON, 2009;EKLUNDH, 2002EKLUNDH, , 2004GANDINI, 2013b;PARUELO et al, 1997;PARUELO et al, 2001;VÁZQUEZ et al, 2013). De esta manera, el NDVI se ha empleado en la caracterización de unidades funcionales de ecosistemas (ATZBERGER; EILERS, 2011;GANDINI et al, 2012;IVITS et al, 2013;GANDINI, 2013b;PARUELO et al, 2001), en el mapeo de la heterogeneidad de paisajes (ALI et al, 2013;BARRAZA et al, 2013) y su relación con variables climáticas (MELENDEZ-PASTOR et al, 2010;VAN LEEUWEN et al, 2013;YANG et al, 2012).…”

Section: Introductionunclassified

Characterization of land surface phenology of land cover types on the Flooding Pampa (Argentina): an approach using NDVI time series

Gandini

2016

Ambiência

View full text Add to dashboard Cite

ResumenEn la Pampa Deprimida los trabajos que analizan la dinámica y el funcionamiento de la vegetación se realizan en una escala de mayor detalle y en tiempos cortos. La teledetección ha facilitado el estudio de la dinámica y comportamiento fenológico de la vegetación a partir de índices espectrales específicos, capaces de abarcar diferentes escalas espacio-temporales. Los objetivos de este trabajo son analizar y caracterizar la dinámica fenológica de las coberturas vegetales más representativas de la Pampa Deprimida mediante el análisis de series temporales de NDVI del sensor MODIS. Se utilizó una serie temporal de 253 imágenes de NDVI de las estaciones de crecimiento 2000-2011 las cuales fueron ajustadas en TIMESAT. Este ajuste permitió extraer un conjunto de atributos fenológicos de importancia ecológica: inicio y fin de la estación de crecimiento, duración de la estación de crecimiento, valor y momento del máximo NDVI y la integral anual de la curva de NDVI. Las curvas anuales de crecimiento típicas para cada una de las coberturas presentaron patrones similares, con el inicio de las estaciones de crecimiento en agosto y el final entre mayo y junio, el momento de mayor actividad fotosintética se dio en marzo. Una característica común fue la disminución del NDVI a comienzos de enero, relacionado con las sequías estivales que se producen en la región. En la matriz del paisaje se observó una caída significativa del NDVI acumulado en el período 2000-2011, lo que podría indicar una sobre-utilización por parte del ganado. La metodología aquí empleada complementa las características

show abstract

“…Yet, handling decametric spatial resolution imagery at global scale is a daunting task, and observations are not available with high temporal frequency for the past. This has led some effort to derive information on spatial heterogeneity directly from coarse spatial resolution by exploiting the wealth of information that such data has in the temporal (de Bie, Nguyen, Ali, Scarrott, & Skidmore, 2012;Ali, de Bie, Skidmore, Scarrott, & Lymberakis, 2014) or angular (Chen, Menges, & Leblanc, 2005;Pinty, Widlowski, Gobron, Verstraete, & Diner, 2002) domain.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: A demonstration for agricultural monitoring applications

Duveiller

López-Lozano

Cescatti

2015

Remote Sensing of Environment

View full text Add to dashboard Cite

MODIS has been providing daily imagery for retrieving land surface properties with a spatial resolution 250 m since the year 2000. In many places, this pixel size is closer to that of individual landscape elements, such as managed forest stands or crop fields, than those of time series more typically used in vegetation analyses (with pixel sizes ranging from about 1 km to 8 km). With such spatial resolution, combined with its increasingly long archive, MODIS data offers great potential for vegetation monitoring applications in general, and crop growth monitoring in particular. However, due to its whiskbroom design, the observation geometry of the MODIS instrument, combined with the spatial uncertainty in the registration of the images, can result in different (albeit neighbouring) physical areas being mapped onto the same pixel depending on the view zenith angle (which varies from day to the next). Rather than considering this as an inconvenience, a method is here proposed to exploit this peculiarity to identify pixels corresponding to a homogeneous plant cover, in order to retrieve surface specific time series of satellite products. This method is based on quantifying the temporal signal-to-noise ratio (SNR, hereafter) of the daily MODIS NDVI time series, defined as the variance of smoothed temporal signal over the variance of the residues. If consecutive observations of the same pixel (which have thus sampled the spatial vicinity of that pixel) provide similar NDVI values, the resulting temporal signal is relatively stable and the SNR is high. Such stability can indicate that the signal comes from a spatially homogeneous surface, such as a single large field covered by the same crop with similar agro-management practices. On the contrary, a noisy time series indicates instead a transition zone between different land uses or between fields with different management practices. SNR maps therefore serve as a proxy for sub-pixel homogeneity from which time series originating from a specific land cover or land use can be retrieved. This approach is demonstrated over 12 contrasting agricultural landscapes across the globe from which clearly distinctive crop specific signals are extracted. Exploiting the full MODIS archive to derive surface specific information in this way should open new avenues for regional to global agricultural monitoring applications. Expanding this method to derive satellite products for specific land cover classes could also be useful for many other applications linked to dynamics of land cover and land use change.

show abstract

Mapping the heterogeneity of natural and semi-natural landscapes

Cited by 20 publications

References 59 publications

Continuous and consistent land use/cover change estimates using socio-ecological data

Continuous and consistent land use/cover change estimates using socio-ecological data

Characterization of land surface phenology of land cover types on the Flooding Pampa (Argentina): an approach using NDVI time series

Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: A demonstration for agricultural monitoring applications

Contact Info

Product

Resources

About