Including Vegetation Indices Research Articles

Comparison of different methods for corn LAI estimation over northeastern China

Leaf area index (LAI) is a crucial variable in all kinds of ecosystem, climate and crop yield models, describing the fluxes of energy, mass and momentum between the surface and the planetary boundary layer. To accurately determine the corn LAI, several methods of LAI estimation have been evaluated in this investigation, including vegetation indices, principal component analysis (PCA), the neural network method (NN), the look-up table (LUT) inversion from PROSAIL model and the Hybrid model. Comparisons were conducted based on field-measured corn canopy hyperspectral reflectance and LAI data over northeastern China. In order to fairly compare the LAI estimation performance of different methods, the ground-measured data were separated into two sets (modeling data and validation data), except the LUT and hybrid methods of PROSAIL-based. The results indicated that the PCA method delivered the best performance for corn LAI estimation (with maximum R2=0.814 and minimum RMSE=0.501) in this study. The hybrid model and EVI provided moderate results. Comparatively, the LUT and NN methods were less successful and NDVI provided the worst corn LAI estimation performance in this study. The PCA method shows great potential for performing well on corn LAI estimation from hyperspectral information. PCA can avoid the reflectance saturation defect of dense canopy in a certain extent, can utilize hyperspectral reflectance data much more effectively than other methods, and is not limited by the band numbers, it can also reduce noise and provide an great correlation with LAI from the hyper- bands or the multi- bands reflectance.

Water surface variations monitoring and flood hazard analysis in Dongting Lake area using long-term Terra/MODIS data time series

Dongting Lake is the second largest freshwater lake in China, and its water surface area varied very significantly during last decade. Remote sensing technology has more advantages in macro monitoring of lake water surface area than the traditional methods. In the paper, an integrated threshold method of water body extraction based on MODIS data is given, which synthesizes several factors, including vegetation index—NDVI, spectrum characters of water body, cloud and shadow, and the SRTM digital elevation information. With this method and 356 scenes MODIS 8-Day composite (MOD09Q1) image, water surface area of Dongting Lake was dynamically monitored from 2000 to 2009. The result shows that during 1 year, the water area variation in Dongting Lake area had a typical seasonal (monsoon) behavior, and during last decade, the water area decreased gradually and obviously. Based on variation monitoring, yearly max-submersion time index has been suggested to analyze flood hazard in study area. With the support of ArcGIS software, authors estimated the yearly submersion time of the Dongting Lake for each year separately and average submersion time from 2000 to 2009. The result shows 67.46% of study area is being with high flood hazard.

Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur

Summary 1. Statistical modelling of habitat suitability is an important tool for planning conservation interventions, particularly for areas where species distribution data are expensive or hard to collect. Sometimes however the predictor variables typically used in habitat suitability modelling are themselves difficult to obtain or not meaningful at the geographical extent of the study, as is the case for the Alaotran gentle lemur Hapalemur alaotrensis, a critically endangered lemur confined to the marshes of Lake Alaotra in Madagascar. 2. We developed a habitat suitability model where all predictor variables, including vegetation indices and image texture measures at different scales (as surrogates for habitat structure), were derived from Landsat7 satellite imagery. Using relatively few presence records, the maximum entropy (Maxent) approach and AUC were used to assess the performance of candidate predictor variables, for studying the effect of scale, model selection and mapping suitable habitat. 3. This study demonstrated the utility of satellite imagery as a single source of predictor variables for a Maxent habitat suitability model at the landscape level, within a restricted geographical extent and with a fine grain, in a case where predictor variables typically used at the macro‐scale level (e.g. climatic and topographic) were not applicable. 4. In the case of H. alaotrensis, the methodology generated a habitat suitability map to inform conservation management in Lake Alaotra and a replicable protocol to allow rapid updates to habitat suitability maps in the future. The exploration of candidate predictor variables allowed the identification of scales that appear ecologically relevant for the species. 5. Synthesis and applications. This study presents a cost‐effective combination of maximum entropy habitat suitability modelling and satellite imagery, where all predictor variables are derived solely from Landsat7 images. With a habitat modelling method like Maxent that shows good performance with few presence samples and Landsat images now freely available, the methodology can play an important role in rapid assessments of the status of species at the landscape level in data‐poor regions, when typical macro‐scale environmental predictors are of little use or difficult to obtain.

A Review on Remote Sensing of Weeds in Agriculture

In the effort of developing precision agriculture tools, remote sensing has been commonly considered as an effective technique for weed patch delineation, where weed infestations are detected based on variations in the plant canopy spectral response. Because the canopy spectral response is important for weed detection, discussions on the irradiative interaction of light in plant canopies and the effect of variable soil background on the canopy spectral response are presented in this review. Also, a presentation of the current techniques for removal of soil effects, including vegetation indices and spectral mixture analysis, shows that these techniques have not been adequately developed for use in remote sensing-based weed detection applications. Given the nature of light interaction in a plant canopy, this review proposes that the spectral response of a plant canopy depends on both the species and the biomass density. Remote detection of weeds from ground-, aircraft-, and satellite-based platforms has been accomplished on a wide scale, yet the use of these weed detection methods to make variable-rate herbicide applications has not occurred as often. By judging success based on variable-rate herbicide applications rather than precise weed localization, some of the current problems in weed sensing may be skirted.

Vegetation Spectral Reflectance Along a North-South Vegetation Gradient in Northern Australia

This research demonstrates that frequently used vegetation indices should not be applied comprehensively across subtropical, semi-arid and arid zones. Zonal, species specific band combinations are considered more sensitive to changing conditions. In three areas within a subtropical to arid climatic gradient, ground radiometer readings were undertaken to establish basic spectral data for the quantification of green vegetation cover in Northern Territory, Australia. The density of woody green vegetation cover was 32% in the subtropical zone, 28% in the semi-arid zone and 9% in the arid zone. Dry season reflectance data on different woody species, dead and alive herbaceous cover, plant litter and soil (cover types) were obtained on the equivalent of Multispectral Scanner (MSS) wavebands. Relationships between ratio and linear band combinations (including vegetation indices) and percentage cover types were examined by regression analysis to determine what the band combinations were in fact predicting along the gradient. Results showed that completely different band combinations were necessary to predict green vegetation cover in subtropical as opposed to semi-arid and arid areas. Commonly applied indices like the NDVI (Normalized Difference Vegetation Index) were negatively associated with soil variables and not related to green vegetation cover. Further tests where projected foliage cover was artificially increased from 20-100% resulted in a suite of different spectral response curves for each climatic zone. These were categorized as: (a) plants which totally darken or (b) partially darken the soil background in all wavebands and (c) plants which are more highly reflective in the near-infrared than the background soil. The subtropical species divided evenly among the three response curve types but the semi-arid and arid species mostly darkened or partially darkened the local soil response. The need for different band combinations to predict green vegetation cover through the climatic zones results from the unique spectral response patterns of climatically variable plant species in relation to their soil background. This work has value in environmental and range monitoring in Northern Territory and other subcontinents spanning similar climatic belts.