Recovery of Vegetation Canopy after Severe Fire in 2000 at the Black Hills National Forest,South Dakota,USA

Forest fires often result in varying degrees of canopy loss in forested landscapes. The subsequent trajectory of vegetation canopy recovery is important for ecosystem processes because the canopy controls photosynthesis and evapotranspiration. The loss and recovery of a canopy is often measured by leaf area index （LAD and other vegetation indices that are related to canopy photosynthetic capacity. In this study we used time series imagery from the Moderate Resolution Imaging Spectroradiometer （MODIS） sensor onboard the Terra satellite over the period of 2000-2009 to track the recovery of the vegetation canopy after fire. The Black Hills National Forest, South Dakota, USA experienced an extensive wildfire starting on August 24, 2000 that burned a total area of 33 785 ha, most of which was ponderosa pine forest. The MODIS data show that canopy photosynthetic capacity, as measured by IL,AI, recovered within 3 years （2001-2003）. This recovery was attributed to rapid emergence of understory grass species after the fire event. Satellite-based Normalized Difference Vegetation Index （NDVI） and Enhanced Vegetation Index （EVI） at the burned sites also recovered within 3 years （2001-2003）. Rapid recovery of LAI, NDVI, and EVI at the burned sites makes it difficult to use these variables for identifying and mapping burned sites several years after the fire event. However, the Land Surface Water Index （LSWI）, calculated as a normalized ratio between near infrared and shortwave infrared bands （band 2 and band 6 （1628 1652 nm） in MODIS sensor）, was able to identify and track the burned sites over the entire period of 2000 2009. This fmding opens a window of opportunity to identify and map disturbances using imagery from those sensors with both NIR and SWIR bands, including Landsat 5 TM （dating back to 1984）; furthermore, a longer record of disturbance and recovery helps to improve our understanding of disturbance regimes, simulations of forest succession, and the carbon cycle.

Mapping rice cropping systems using Landsat-derived Renormalized Index of Normalized Difference Vegetation Index （RNDVI） in the Poyang Lake Region, China

Mapping rice cropping systems with optical imagery in multiple cropping regions is challenging due to cloud contamination and data availability; development of a phenology-based algorithm with a reduced data demand is essential. In this study, the Landsat-derived Renorma- lized Index of Normalized Difference Vegetation Index （RNDVI） was proposed based on two temporal windows in which the NDVI values of single and early （or late） rice display inverse changes, and then applied to discriminate rice cropping systems. The Poyang Lake Region （PLR）, characterized by a typical cropping system of single cropping rice （SCR, or single rice） and double cropping rice （DCR, including early rice and late rice）, was selected as a testing area. The results showed that NDVI data derived from Landsat time-series at eight to sixteen days captures the temporal development of paddy rice. There are two key phenological stages during the overlapping growth period in which the NDVI values of SCR and DCR change inversely, namely the ripening phase of early rice and the growing phase of single rice as well as the ripening stage of single rice and the growing stage of late rice. NDVI derived from scenes in two temporal windows, specifically early August and early October, was used to construct the RNDVI for discriminating rice cropping systems in the polder area of the PLR, China. Comparison with ground truth data indicates high classification accuracy. The RNDVI approach highlights the inverse variations of NDVI values due to the difference of rice growth between two temporal windows. This makes the discrimination of rice cropping systems straightforward as it only needs to distinguish whether the candidate rice typeis in the period of growth （RNDVI 〈 0） or senescence （RNDVI 〉 0）.