Forest leaf area index determination: A multiyear satellite- independent method based on within-stand normalized difference vegetation index spatial variability

نویسندگان

  • G. le Maire
  • C. François
  • K. Soudani
  • H. Davi
  • V. Le Dantec
  • B. Saugier
  • E. Dufrêne
چکیده

[1] The Leaf Area Index (LAI) and its spatial distribution are key features to describe the forest ecophysiological processes. A stable and reproducible relationship is obtained between the LAI and the standard deviation sNDVI of the pixel-based satellite-derived normalized difference vegetation indices (NDVI) of forest stands. In situ measurements of LAI have been performed with the LAI-2000 Plant Canopy Analyser over 8 years in the managed Fontainebleau forest (France) on about 31 stands each year, including oak, beech, and mixed oak-beech stands. Simultaneous satellite images have been acquired, atmospherically and geometrically corrected, and included into a geographic information system to get the mean NDVI and the sNDVI for each stand. A total of six different satellites with a 20to 30-m spatial resolution have been considered over the eight studied years: SPOT1, SPOT2, SPOT4, LANDSAT ETM+, IKONOS, and HYPERION. The mean LAI of a stand is linked to the sNDVI with a unique relationship that appears to be mostly yearand satellite-independent, because the sNDVI is nearly insensitive to additive or proportional shifts on NDVI. The theoretical bases of the sNDVI-LAI relationship are investigated. The results show the combined importance of the shape of the within-stand LAI distribution (following a Weibull probability density function) and the shape of the within-stand LAI-NDVI curves (showing a saturation). The root mean square error of the predicted LAI over the 259 samples is 1.14 m/m when all years and satellites are considered, using the following equation: LAI = 2.45 ln(sNDVI) 5.58 (r = 0.63).

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Analysis of a multiyear global vegetation leaf area index data set

[1] The analysis of a global data set of monthly leaf area index (LAI), derived from satellite observations of normalized difference vegetation index (NDVI) for the period July 1981 to September 1994, is discussed in this paper. Validation of this retroactive, coarse resolution (8 km) global multiyear data set is a challenging task because repetitive ground measurements from all representative ...

متن کامل

Evaluation of remote sensing indicators in drought monitoring using machine learning algorithms (Case study: Marivan city)

Remote sensing indices are used to analyze the Spatio-temporal distribution of drought conditions and to identify the severity of drought. This study, using various drought indices generated from Madis and TRMM satellite data extracted from Google Earth Engine (GEE) platform. Drought conditions in Marivan city from February to November for the years 2001 to 2017 were analyzed based on spatial a...

متن کامل

Determination of spring onset and growing season leaf development using satellite measurements

An integrated approach to retrieve microwave emissivity difference vegetation index (EDVI) over land regions has been developed from combined multi-platform/multi-sensor satellite measurements, including SSM/I measurements. A possible relationship of the remotely sensed EDVI and the leaf physiology of canopy is explored at the Harvard Forest site for two growing seasons. This study finds that t...

متن کامل

Analysis of LAI in Iran based on MODIS satellite data

This study was performed to evaluate the extent of leaf area in Iran from (2002) to (2016) using Remote sensing. For this purpose, we extracted data collection and leaf area index for the Iranian territory from MODIS website. The database was established with programming in MATLAB software to perform mathematical and Statistical calculations repeated. After the analysis of the data in this soft...

متن کامل

Leaf Area Index (LAI) Estimation in Boreal Mixedwood Forest of Ontario, Canada Using Light Detection and Ranging (LiDAR) and WorldView-2 Imagery

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the pr...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009