Dataset Information

Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery.

ABSTRACT: This study aimed to evaluate changes in the aboveground net primary productivity (ANPP) of grasslands in the Pampa biome by using experimental plots and changes in the spectral responses of similar vegetation communities obtained by remote sensing and to compare both datasets with meteorological variations to validate the transition scales of the datasets. Two different geographic scales were considered in this study. At the local scale, an analysis of the climate and its direct influences on grassland ANPP was performed using data from a long-term experiment. At the regional scale, the influences of climate on the grassland reflectance patterns were determined using vegetation sensor imagery data. Overall, the monthly variations of vegetation canopy growth analysed using environmental changes (air temperature, total rainfall and total evapotranspiration) were similar. The results from the ANPP data and the NDVI data showed the that variations in grassland growth were similar and independent of the analysis scale, which indicated that local data and the relationships of local data with climate can be considered at the regional scale in the Pampa biome by using remote sensing.

SUBMITTER: Scotta FC

PROVIDER: S-EPMC4541953 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery.

Scottá Fernando C FC da Fonseca Eliana L EL

Sensors (Basel, Switzerland) 20150721 7

This study aimed to evaluate changes in the aboveground net primary productivity (ANPP) of grasslands in the Pampa biome by using experimental plots and changes in the spectral responses of similar vegetation communities obtained by remote sensing and to compare both datasets with meteorological variations to validate the transition scales of the datasets. Two different geographic scales were considered in this study. At the local scale, an analysis of the climate and its direct influences on gr ...[more]

PMID: 26197320

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Project description:Understanding how land-use changes affect different facets of plant biodiversity in seminatural European grasslands is of particular importance for biodiversity conservation. As conclusions of previous experimental or synchronic observational studies did not converge toward a general agreement, assessing the recent trends in vegetation change in various grassland systems using a diachronic approach is needed. In this resurvey study, we investigated the recent changes in grassland vegetation of the French Jura Mountains, a region with a long tradition of pastoralism. We compared the floristic composition of 150 grassland plots recorded between 1990 and 2000 with new relevés made in 2012 on the same plots. We considered taxonomic, phylogenetic and functional diversity as well as ecological characteristics of the plant communities derived from ecological indicator values and life strategies of the species. PCA of the floristic composition revealed a significant general trend linked to the sampling year. Wilcoxon paired tests showed that contemporary communities were generally more dominated by grass species and presented a higher tolerance to defoliation, a higher pastoral value, and a higher nutrient indicator value. Comparisons revealed a decrease in phylogenetic and functional diversity. By contrast, local species richness has slightly increased. The intensity of change in species composition, measured by Hellinger distance between pairs of relevés, was dependent on neither the time lag between the two surveys, the author of the first relevé nor its location or elevation. The most important changes were observed in grasslands that previously presented low pastoral value, low grass cover, low tolerance to defoliation, and high proportion of stress-tolerant species. This trend was likely linked to the intensification of grassland management reported in the region, with a parallel increase in mowing frequency, grazing pressure, and fertilization level. More restrictive specifications should be applied to agricultural practices to avoid overexploitation of mountain species-rich grasslands and its negative consequences on their biodiversity and resilience.

Project description:Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: FM~LV∗LV_Den∗NDVI had a determination of coefficient R 2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables- FM~LV∗LV_Den . In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.

Dataset Information

Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery.

Publications

Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery.

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