Q. Yu | Search USGS

USGS2010

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water-quality standards. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near-infrared (NIR)-Green Angle Index (NGAI) and normalized water absorption depth (DH), at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral-derived NDVI. The IKONOS-based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the DH. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High-resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. Interpretation of biophysical parameters derived from high-resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments. ?? 2010 Blackwell Publishing Ltd.

Freshwater Biology↗

USGS2006

Litterfall production along successional and altitudinal gradients of subtropical monsoon evergreen broadleaved forests in Guangdong, China

Evaluation of litterfall production is important for understanding nutrient cycling, forest growth, successional pathways, and interactions with environmental variables in forest ecosystems. Litterfall was intensively studied during the period of 1982-2001 in two subtropical monsoon vegetation gradients in the Dinghushan Biosphere Reserve, Guangdong Province, China. The two gradients include: (1) a successional gradient composed of pine forest (PF), mixed pine and broadleaved forest (MF) and monsoon evergreen broadleaved forest (BF), and (2) an altitudinal gradient composed of Baiyunci ravine rain forest (BRF), Qingyunci ravine rain forest (QRF), BF and mountainous evergreen broadleaved forest (MMF). Mean annual litterfall production was 356, 861 and 849 g m-2 for PF, MF and BF of the successional gradient, and 1016, 1061, 849 and 489 g m-2 for BRF, QRF, BF and MMF of the altitudinal gradient, respectively. As expected, mean annual litterfall of the pioneer forest PF was the lowest, but rapidly increased over the observation period while those in other forests were relatively stable, confirming that forest litterfall production is closely related to successional stages and growth patterns. Leaf proportions of total litterfall in PF, MF, BF, BRF, QRF and MMF were 76.4%, 68.4%, 56.8%, 55.7%, 57.6% and 69.2%, respectively, which were consistent with the results from studies in other evergreen broadleaved forests. Our analysis on litterfall monthly distributions indicated that litterfall production was much higher during the period of April to September compared to other months for all studied forest types. Although there were significant impacts of some climate variables (maximum and effective temperatures) on litterfall production in some of the studied forests, the mechanisms of how climate factors (temperature and rainfall) interactively affect litterfall await further study. ?? 2006 Springer Science+Business Media B.V.

Plant Ecology↗