RESUMEN
Snow accumulation and melt have multiple impacts on Land Surface Phenology (LSP) and greenness in Alpine grasslands. Our understanding of these impacts and their interactions with meteorological factors are still limited. In this study, we investigate this topic by analyzing LSP dynamics together with potential drivers, using satellite imagery and other data sources. LSP (start and end of season) and greenness metrics were extracted from time series of vegetation and leaf area index. As explanatory variables we used snow accumulation, snow cover melt date and meteorological factors. We tested for inter-annual co-variation of LSP and greenness metrics with seasonal snow and meteorological metrics across elevations and for four sub-regions of natural grasslands in the Swiss Alps over the period 2003-2014. We found strong positive correlations of snow cover melt date and snow accumulation with the start of season, especially at higher elevation. Autumn temperature was found to be important at the end of season below 2000 m above sea level (m asl), while autumn precipitation was relevant above 2000 m asl, indicating climatic growth limiting factors to be elevation dependent. The effects of snow and meteorological factors on greenness revealed that this metric tends to be influenced by temperatures at high elevations, and by snow melt date at low elevations. Given the high sensitivity of alpine grassland ecosystems, these results suggest that alpine grasslands may be particularly affected by future changes in seasonal snow, to varying degree depending on elevation.
RESUMEN
Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field scale using a data assimilation strategy.
RESUMEN
Avian species persistence in a forest patch is strongly related to the degree of isolation and size of a forest patch and the vegetation structure within a patch and its matrix are important predictors of bird habitat suitability. A combination of space-borne optical (Landsat), ALOS-PALSAR (radar), and airborne Light Detection and Ranging (LiDAR) data was used for assessing variation in forest structure across forest patches that had undergone different levels of forest degradation in a logged forest-agricultural landscape in Southern Laos. The efficacy of different remote sensing (RS) data sources in distinguishing forest patches that had different seizes, configurations, and vegetation structure was examined. These data were found to be sensitive to the varying levels of degradation of the different patch categories. Additionally, the role of local scale forest structure variables (characterized using the different RS data and patch area) and landscape variables (characterized by distance from different forest patches) in influencing habitat preferences of International Union for Conservation of Nature (IUCN) Red listed birds found in the study area was examined. A machine learning algorithm, MaxEnt, was used in conjunction with these data and field collected geographical locations of the avian species to identify the factors influencing habitat preference of the different bird species and their suitable habitats. Results show that distance from different forest patches played a more important role in influencing habitat suitability for the different avian species than local scale factors related to vegetation structure and health. In addition to distance from forest patches, LiDAR-derived forest structure and Landsat-derived spectral variables were important determinants of avian habitat preference. The models derived using MaxEnt were used to create an overall habitat suitability map (HSM) which mapped the most suitable habitat patches for sustaining all the avian species. This work also provides insight that retention of forest patches, including degraded and isolated forest patches in addition to large contiguous forest patches, can facilitate bird species retention within tropical agricultural landscapes. It also demonstrates the effective use of RS data in distinguishing between forests that have undergone varying levels of degradation and identifying the habitat preferences of different bird species. Practical conservation management planning endeavors can use such data for both landscape scale monitoring and habitat mapping.
