Correction: Tilly, N. et al. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 2015, 7, 11449-11480

نویسندگان

  • Nora Tilly
  • Helge Aasen
  • Georg Bareth
چکیده

After publication of the research paper [1] an error during the data analysis process was recognized. In Table 4 [1] the units for fresh and dry biomass are stated as being g/m2. However, the values actually refer to the sampling area (0.2 m by 0.2 m), hence each value should have been multiplied by 25 to extrapolate it to g/m2. Unfortunately, this step was missed out. All analyses were re-executed based on the correct values, and the corresponding tables and figures are presented in the same order as in the paper in the following Tables 1–3, Figure 1–3. Thus, the stated sensitivity thresholds for the saturation of the NDVI and RGBVI must also be corrected to be about 185 g/m2 and 1375 g/m2 for dry and fresh biomass, respectively. In comparison to the originally stated values the R2 and d values for all models did not change and hence, the overall statements of the study are correct. For the linear BRMs, the value ranges were extended through the multiplication by 25 and thus, the SEE and RMSE differ. In contrast, the log-transformation for the exponential BRMs converted the factor to a constant summand, which is added to each value (ln(25) – 3.22). Consequently, the absolute difference between the biomass values and hence, the SEE and RMSE, do not differ. We apologize for any inconvenience this has caused.

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

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner wa...

متن کامل

Remote sensing technology for mapping and monitoring vegetation cover (Case study: Semirom-Isfahan, Iran)

To determine the suitable indices for vegetation cover and production assessment based on the remote sensing data, simultaneous digital data with field data belonging to the spring rangeland of the Semirom-Isfahan province were analyzed. During two years of monitoring the annual, grass, forb, and shrub vegetation cover and the total production data from 86 were collected. The Global Positioning...

متن کامل

Remote sensing technology for mapping and monitoring vegetation cover (Case study: Semirom-Isfahan, Iran)

To determine the suitable indices for vegetation cover and production assessment based on the remote sensing data, simultaneous digital data with field data belonging to the spring rangeland of the Semirom-Isfahan province were analyzed. During two years of monitoring the annual, grass, forb, and shrub vegetation cover and the total production data from 86 were collected. The Global Positioning...

متن کامل

Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging

Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs). The superhigh resolution, multi-temporal (1 cm/pixel) CSMs were der...

متن کامل

Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Ou...

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:
  • Remote Sensing

دوره 7  شماره 

صفحات  -

تاریخ انتشار 2015