Physiological and Chemical Characteristics of Five Cultivars of Cynodon

The experiment was carried out at UNESP, Brazil, to evaluate five Cynodon cultivars: (Cynodon nlemfuensis Vanderyst cv “Tifton 68”, Cynodon spp. cv “Tifton 85”, Cynodon dactylon cv “Florakirk”, Cynodon nlemfuensis Vanderyst var. nlemfuensis cv “Florico” e Cynodon nlemfuensis Vanderyst var. nlemfuensis cv “Florona”). Plants were sampled once a week from 14 to 84 days of growth to study physiological characteristics and at 14-day intervals to study chemical characteristics. The following variables were studied: DM production, leaf/stem ratio, leaf weight/total dry matter ratio, relative growth rate, chlorophyll contents, leaf area, specific leaf area, LAI, and the contents of CP, NDF, and ADF. The five cultivars showed similar responses for all variables studied. The data obtained allowed us to conclude that the five cultivars would be better managed when cut or grazed at 28-42 days intervals of plant growth

Object-based change detection in the cerrado biome using landsat time series

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series