Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data

2003 ◽  
Vol 29 (2) ◽  
pp. 241-258 ◽  
Author(s):  
Richard Fernandes ◽  
Chris Butson ◽  
Sylvain Leblanc ◽  
Rasim Latifovic
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Accuracy Assessment ◽  
Area Index ◽  
Landsat 7 ◽  
Landsat 5 Tm

scholarly journals A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

2018 ◽  
Vol 10 (5) ◽  
pp. 763 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco García-Haro ◽  
Lorenzo Busetto ◽  
Luigi Ranghetti ◽  
Beatriz Martínez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Polar System ◽  
Critical Comparison ◽  
Landsat 7 ◽  
Sentinel 2

scholarly journals Estimativa de Albedo e Índice de Área Foliar na Amazônia (Albedo Estimate and Leaf Area Index in Amazonia)

2011 ◽  
Vol 4 (1) ◽  
pp. 22 ◽  
Author(s):  
Ailton Marcolino Liberato
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Scientific Literature ◽  
Thematic Mapper ◽  
Amazon Forest ◽  
Geometric Correction ◽  
Landsat Images ◽  
Area Index ◽  
Landsat 5 Tm

Propôs-se, neste trabalho, estimar dados de albedo e Indice de Área Foliar (IAF) à superfície terrestre usando-se o sensor Thematic Mapper (TM) do satélite Landsat 5 e compará-lo com valores disponíveis na literatura científica. A região de estudo esta localizada no estado de Rondônia. Para a realização do estudo obtiveram-se quatro imagens orbitais do satélite Landsat 5 – TM, na órbita 231 e ponto 67, nas datas 13/07/2005, 13/05, 30/06 e 16/07 do ano de 2006, a que correspondem os dias Juliano 194, 133, 181 e 197, respectivamente. As correções geométricas para as imagens foram realizadas e geradas as cartas de albedo e IAF. O algoritmo SEBAL estimou satisfatoriamente os valores de albedo e IAF de superfícies sobre áreas de floresta (exceto para IAF) e pastagem.Palavras-chave: sensoriamento remoto, vegetacao, Floresta da Amazonia. Albedo Estimate and Leaf Area Index in Amazonia ABSTRACTThis study objectives the assessment of albedo and Leaf Area Index (LAI) data at surface using  images from Thematic Mapper (TM) sensor onboard Landsat 5 satellite, and  compare the results with values available in the scientific literature. The study area is located in the State of Rondônia. To carry out the study four orbital TM - Landsat images were obtained in the path 231 and row  67, for the dates of 07/13/2005, 06/30 and 07/16 of  2006 year, which correspond to the days 194, 181 and 197, respectively. The geometric correction for images was performed and maps of albedo and IAF were generated. The algorithm SEBAL estimated, satisfactorily, the values of albedo and IAF on the surface pasture and forest (except for LAI).Keywords: remote sensing, vegetation, Amazon Forest.

scholarly journals Monitoring leaf area index at watershed level through NDVI from Landsat-7/ETM+ data

2004 ◽  
Vol 61 (3) ◽  
pp. 243-252 ◽  
Author(s):  
Alexandre Cândido Xavier ◽  
Carlos Alberto Vettorazzi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Hydrological Cycle ◽  
Riparian Forest ◽  
Empirical Relationships ◽  
Area Index ◽  
Landsat 7 ◽  
The Mean ◽  
The Relationship

Leaf area index (LAI) is an important parameter of the vegetation canopy, and is used, for instance, to estimate evapotranspiration, an important component of the hydrological cycle. This work analyzed the relationship between LAI, measured in field, and NDVI from four dates (derived from Landsat-7/ETM+ data), and with such vegetation index, to generate and analyze LAI maps of the study area for the diverse dates. LAI data were collected monthly in the field with LAI-2000 equipment in stands of sugar cane, pasture, corn, eucalypt, and riparian forest. The relationships between LAI and NDVI were adjusted by a potential model; 57% to 72% of the NDVI variance were explained by the LAI. LAI maps generated by empirical relationships between LAI and NDVI showed reasonable precision (standard error of LAI estimate ranged from 0.42 to 0.87 m² m-2). The mean LAI value of each monthly LAI map was shown to be related to the total precipitation in the three previous months.

Application of a forest reflectance model in estimating leaf area index of Scots pine stands using Landsat-7 ETM reflectance data

2003 ◽  
Vol 29 (3) ◽  
pp. 314-323 ◽  
Author(s):  
Miina Rautiainen ◽  
Pauline Stenberg ◽  
Tiit Nilson ◽  
Andres Kuusk ◽  
Heikki Smolander
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Scots Pine ◽  
Area Index ◽  
Reflectance Model ◽  
Landsat 7 ◽  
Pine Stands ◽  
Reflectance Data ◽  
Scots Pine Stands

scholarly journals Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests

2021 ◽  
Vol 13 (16) ◽  
pp. 3325
Author(s):  
Jing Liu ◽  
Longhui Li ◽  
Markku Akerblom ◽  
Tiejun Wang ◽  
Andrew Skidmore ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Hemispherical Photography ◽  
Individual Tree ◽  
Area Index ◽  
Terrestrial Lidar ◽  
Software Programs

The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R2 > 0.8, RMSD < 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R2 = 0.88 > 0.72, 0.49; RMSE = 0.45 < 0.7, 0.94; nRMSE = 15.7% < 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP.

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