Monitoring vegetation phenology using MODIS

Zhang, Xiaoyang; Friedl, M. A.; Schaaf, Crystal B.; Strahler, Alan H.; Hodges, J.C.F.; Gao, Feng; Reed, Barbara M.; Huete, Alfredo

doi:10.1016/s0034-4257(02)00135-9

Cited by 2,099 publications

(1,631 citation statements)

References 17 publications

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“…While remote sensing for large-area land cover change mapping has become common in many types of landscapes (Zhang et al, 2003;Linderman et al, 2005;Hansen et al, 2013), mapping urban expansion globally has remained incredibly difficult: urban areas are rare, their features are small, they are heterogeneous in both material composition and configuration, and the form, rates, and spectral-temporal signatures of new land development are highly variable across locations (Jensen, 1999;Makov et al, 2005;Potere et al, 2009;Schneider et al, 2009;Schneider, 2012). In addition, cost and data availability generally necessitate the use of coarse or moderate resolution data for continental scale mapping, thereby compounding the issue of land cover 'mixing' due to the large pixel size.…”

Section: Introductionmentioning

confidence: 99%

“…Many methods that take advantage of multi-season and multi-year observations employ time-series spectral profiles, as the large number of data points provides better discrimination of signal from noise, and makes it possible to link vegetation phenology to the spectral trajectory (Kennedy et al, 2014). Curve-fitting (Zhang et al, 2003;Kennedy et al, 2007), harmonics analysis (Cihar et al, 2001;Jakubauskas et al, 2001;Bradley et al, 2007;Geerken, 2009), and wavelet transformation (Sakamoto et al, 2005;Martinez et al, 2009) have all been used to monitor vegetation dynamics and extract phenological markers (e.g. date of greenup, senescence) in forested or agricultural landscapes.…”

Section: Introductionmentioning

confidence: 99%

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Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

153

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Abstract. Urbanization is one of the most important components of global environmentalchange, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a new methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify ca 2010 urban extent; and (3) mapping newly built areas (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250 m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East-Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socicoeconomic/political and ecological/climatic conditions with good accuracy (70-91% overall accuracy by country, 69-89% by biome). The 250 m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of ii the error in the change detection is related to human decision making or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as provide a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

153

View full text Add to dashboard Cite

show abstract

“…Asáreas com maior cobertura vegetal apresentaram também os maiores desvios e erros padrões da média (Fig. 2), devido aos seguintes fatores: i) variação estacional da redução das folhas resultado da sazonalidade do regime de chuvas, conforme descrito por Sakamoto et al (2006), e observado na Floresta Estacional; e ii) pelaÁrea de Plantio, apresentando altos desvios, por possuírem intensa produção foliar e colheitas em um período curto, corroborando com dados de Zhang et al (2003).…”

Section: Resultados E Discussão Assinaturas Temporais Ndvi-modisunclassified

Modelagem de espectros temporais NDVI-MODIS, no período de 2000 a 2008, na bacia do rio Paracatu, Brasil

Santana¹,

Júnior²,

Pimentel³

et al. 2010

Rev. Bras. Geof.

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ABSTRACT. The (b) tratamento de ruídos; (c) modelagem matemática das assinaturas temporais NDVI; e (d) análise das relações estatísticas entre NDVI e abertura de dossel. A regressão não linear, modelagem ondulatória, para dispersão do NDVI pelas séries temporais, obteve significância de seus parâmetros (R 2 e p) e erros toleráveis. Com isso pôde-se identificar as fisionomias a serem delimitadas, e simular séries temporais futuras, pelo uso da terra, coberturas vegetais e tamanho daárea de cada fisionomia. O padrão de distribuição dos dados de NDVI apresentou significativa relação com dados de campo de abertura de dossel, podendo ser um preciso indicador da mudança do uso da terra entre as séries temporais.Palavras-chave: regressão não linear, modelos ondulatórios, Rio São Francisco.

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“…A systematic under-estimation was visible as regression slopes <1.0 in all bands except NIR (Figure 1c), and is likely to be caused by three factors. First, for some observation geometry configurations, F1 and F2 (Equation (1)) are highly uncorrelated, which make the inversion of their respective parameters poorly constrained [8]. Second, the MODIS BRDF parameters (MOD43A1 Collection 6) are fitted from the observed data during a 16-day period, whereas MYD09GA directional reflectance is an instantaneous product [9].…”

Section: Variability Of the Mcd43a1 Productmentioning

confidence: 99%

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

et al. 2017

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Measurements of solar radiation reflected from Earth's surface are the basis for calculating albedo, vegetation indices, and other terrestrial attributes. However, the "bi-directional" geometry of illumination and viewing (i.e., the Bi-directional Reflectance Distribution Function (BRDF)) impacts reflectance and all variables derived or estimated based on these data. The recently released MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) dataset enables retrieval of directional reflectance at arbitrary solar and viewing angles, potentially increasing precision and comparability of data collected under different illumination and observation geometries. We quantified the ability of MCD43A1 Collection 6 for retrieving directional reflectance and compared the daily Collection 6 retrievals to those of MCD43A1 Collection 5, which are retrieved on an eight-day basis. Correcting MODIS-based estimates of surface reflectance from the illumination and viewing geometry of the Terra satellite (MOD09GA) to that of the MODIS Aqua (MYD09GA) overpass, as well as MCD43A4 Collection 6 and Landsat-5 TM images show that the BRDF correction of MCD43A1 Collection 6 results in greater consistency among datasets, with higher R 2 (0.63-0.955), regression slopes closer to unity (0.718-0.955), lower root mean squared difference (RMSD) (0.422-3.142), and lower mean absolute error (MAE) (0.282-1.735) compared to the Collection 5 data. Smaller levels of noise (observed as high-frequency variability within the time series) in MCD43A1 Collection 6 in comparison to Collection 5 corroborates the improvement of BRDF parameters time series. These results corroborates that the daily MCD43A1 Collection 6 product represents the anisotropy of surface features and results in more precise directional reflectance derivation at any solar and viewing geometry than did the previous Collection 5.

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Monitoring vegetation phenology using MODIS

Cited by 2,099 publications

References 17 publications

Detecting change in urban areas at continental scales with MODIS data

Detecting change in urban areas at continental scales with MODIS data

Modelagem de espectros temporais NDVI-MODIS, no período de 2000 a 2008, na bacia do rio Paracatu, Brasil

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

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