Hierarchical relationships between landscape structure and temperature in a managed forest landscape (original) (raw)
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Stochastic Environmental Research and Risk Assessment, 2012
Temporal shifts in phenology or vegetation period of plants are seen as indicators of global warming with potentially severe impacts on ecosystem functioning. In spite of increasing knowledge on drivers, it is of utmost importance to disentangle the relationship between air temperatures, phenological events, potential temporal lags (phase shifts) and time scale for certain plant species. Assessing the phase shifts as well as the scale-dependent relationship between temperature and vegetation phenology requires the development of a nonlinear temporal model. Therefore, we use wavelet analysis and present a framework for identifying scale-dependent cross-phase coupling of bivariate time series. It allows the calculation of (a) scale-dependent decompositions of time series, (b) phase shifts of seasonal components in relation to the annual cycle, and (c) interannual phase differences between seasonal phases of different time series. The model is applied to air temperature data and remote sensing phenology data of a beech forest in Germany. Our study reveals that certain seasonal changes in amplitude and phase with respect to the normal annual rhythm of temperature and beech phenology are coupled time-delayed components, which are characterized by a time shift of about one year.
Detecting climate-induced patterns using wavelet analysis
Environmental Pollution, 1994
One of the difficulties encountered in the detection of ecosystem responses to climate change is distinguishing climate-induced patterns from those created by other sources. For example, changes in the trend of stream discharge records over time may reflect a composite response of changes in the climate (i.e. precipitation and temperature), land-use (e.g. timber harvesting and grazing), and local basin characteristics. Methods which quantify and relate information of temporal and spatial patterns across scales are critical to assess climatically induced changes in the forest and stream ecosystems. A methodology utilizing wavelet analysis is introduced for the purpose of identifying and isolating inferred climatic components of the hydrologic record Trends observed in stream discharge records from eastern Oregon, USA are identified and used to illustrate the utility of a new time series technique, wavelet analysis, as a complementary approach for discerning pattern. This methodology affords an informed procedure for choosing filter dimensions for the purpose of signal decomposition. The wavelet cross-covariance is applied to precipitation and discharge records to identify the climatic component in the discharge record. Reconstruction of these dominant frequencies is effected to isolate the climatic components. The discharge pattern shows two dominant scales of pattern coincident with the precipitation record A 3-year half-period pattern is found to be correlated with the Southern Oscillation Index at the same frequency.
Journal of Hyperspectral Remote Sensing, 2019
This study aimed to evaluate and compare the seasonal and spatial profiles of soil temperature (ST) in the biomes of the Amazon Forest and Atlantic Forest, using the wavelet transform. In the Amazon rainforest were used the data from the year 2009. In the Atlantic Forest used up to year 2010 data. The results showed that the ST in the Amazon rainforest shows little variation in time with temperature range below 5 °C. In the rainforest, this exhibited high thermal amplitude throughout the year, more than 10 °C. The wavelet transform showed that the variability of ST is defined by multi-scale time: 24 hours for both biomass, 8 to 16 days for Amazon and 4 to 16 days to Atlantic forest.
Spatial and temporal variations in the response of the vegetation indices to surface temperature
2015
Temperature and water are two environmental factors that greatly affect vegetation dynamics. Vegetation response to variations in these factors is very complex, particularly due to the combination of these factors and modulation by topographic gradient. Moreover, the response is affected by vegetation adaptation to its local environment. Temperate deciduous broadleaf forests are an ideal venue to study the response patterns due to their conspicuous seasonality driven by climates. This thesis examined response signatures of deciduous broadleaf forests dominated by oak (Quercus spp.) in South Korea (referred to as Korea), Bosnia, West Virginia (USA) and patches of Nothofagus gunnii in Tasmania (Australia) on variations in temperature and precipitation as viewed by remotely sensed vegetation indices. The overall aim of the thesis is to obtain response signatures of vegetation indices to surface temperature with variations in altitudinal gradients and precipitation. In the first stage, ...
2005
Identifying scales of pattern in ecological systems and coupling patterns to processes that create them are ongoing challenges. We examined the utility of three techniques (lacunarity, spectral, and wavelet analysis) for detecting scales of pattern of ecological data. We compared the information obtained using these methods for four datasets, including: surface temperature across space (linear transect), surface temperature across time, understory plant diversity across space (linear transect), and a simulated series of known structure. For temperature and plant diversity across the transect, we expected to find dominant scales of pattern of approximately 220 m and, for plant diversity scales of pattern of >450 m, corresponding to management activities on the study landscapes. For temperature across time, we predicted a dominant scale of 24 h. The simulated data included a sine wave with a known period of 9.9 units, an edge at approximately 30 units, and a random component. The different analyses provided unique but complementary information. Lacunarity and spectral analyses were most consistent with each other across datasets, both indicating a dominant scale of pattern at 400-500 m (coarser than expected) for the transect temperature series, a lack of dominant scale in the pattern of understory diversity, and scales of pattern at 1.8-5.1 and 8.5-11.1 units (%wavelength) for the simulated data series. Spectral analysis best approximated an expected, 24 h period in the temporal temperature series. Wavelet variance detected finer scales of pattern (240 m) in transect temperature and suggested patterns in plant diversity at scales of 460 and 1100 m. By retaining locational information, only the wavelet transform and associated position variance detected the abrupt edge in the simulated data series. Wavelet analysis also emphasized variability, even within cyclic phenomena, not identified by spectral or lacunarity analyses, and suggested hierarchical structure in the pattern of understory plant diversity. The appropriate technique for assessing scales of pattern depends on the type of data available, the question being asked, and the detail of information desired. This comparison highlighted the importance of: (1) using multiple techniques to examine scales of pattern in ecological data; (2) interpreting analysis results in concert with examination and ecological
Tree physiology, 2005
Orthonormal wavelet transformation (OWT) is a computationally efficient technique for quantifying underlying frequencies in nonstationary and gap-infested time series, such as eddy-covariance-measured net ecosystem exchange of CO2 (NEE). We employed OWT to analyze the frequency characteristics of synchronously measured and modeled NEE at adjacent pine (PP) and hardwood (HW) ecosystems. Wavelet cospectral analysis showed that NEE at PP was more correlated to light and vapor pressure deficit at the daily time scale, and NEE at HW was more correlated to leaf area index (LAI) and temperature, especially soil temperature, at seasonal time scales. Models were required to disentangle the impacts of environmental drivers on the components of NEE, ecosystem carbon assimilation (Ac) and ecosystem respiration (RE). Sensitivity analyses revealed that using air temperature rather than soil temperature in RE models improved the modeled wavelet spectral frequency response on time scales longer tha...
1999
Increasing aWareness of the importance of scale and landscape structure to landscape processes and concern about loss of biodiversity has resulted in efforts to understand patterns of biodiversity.across multiple scales. We examined plant species distributions and their relationships to landscape structure at varying spatial scales across a pine barrens landscape in northern Wisconsin, U.S.A. We recorded plant species cover in 1 x 1 m plots every 5 m along a 3575 m transect, along with variables describing macro-and micro-landscape structure. A total of 139 understory plant species were recorded. The distributions of many species appeared to be strongly associated with landscape .strUctural features, such as distinct management patches and roads. TWINSPAN and detrended correspondence 'analysis (DCA) identified three groups of species that overlapped extensively in the ordination, possibly reflecting thereiatively homogeneous nature of disturbance in the pine barrens landscape. Distribution of understory plants did not reflect all of the patch types we identified along the transect; plot ordination and classification resulted in three to five plot groups that differed in niche breadth. Wavelet transforms showed varying relationships between landscape features and plant diversity indices (Shannon-Weiner, Simpson's Dominance) at different resolutions. Wavelet variances indicated that patterns of Shannon diversity were dominated by coarse resolutions ranging from-_900-1500 m, which may have been related to topography. Patterns of Simpson's Dominance were dominated by-'_700 m resolution, possibly associated with canopy cover. However, a strong correspondence between overstory patch type and diversity was found for several patch types at ranges of scales that varied by patch type. Effects of linear features such as roads were apparent in the wavelet transforms at resolutions of about 5-1000 m, suggesting roads may have an important impact on plant diversity at landscape scales. At broad scales, landscape context appeared more important to diversity than individual patches, suggesting that changes in structure at fine resolutions ' .couldalter overall diversity characteristics of the landscape. Therefore, a hierarchical perspective is necessary to recognizepotential large-scale change resulting from small-scale activities. • ,
Forest Ecology and Management, 2000
Soil temperature is a variable that links surface structure to soil processes and yet its spatial prediction across landscapes with variable surface structure is poorly understood. In this study, a hybrid soil temperature model was developed to predict daily spatial patterns of soil temperature in a forested landscape by incorporating the effects of topography, canopy and ground litter. The model is based on both heat transfer physics and empirical relationship between air and soil temperature, and uses input variables that are extracted from a digital elevation model (DEM), satellite imagery, and standard weather records. Model-predicted soil temperatures ®tted well with data measured at 10 cm soil depth at three sites: two hardwood forests and a bare soil area. A sensitivity analysis showed that the model was highly sensitive to leaf area index (LAI) and air temperature. When the spatial pattern of soil temperature in a forested watershed was simulated by the model, different responses of bare and canopy-closed ground to air temperature were identi®ed. Spatial distribution of daily air temperature was geostatistically interpolated from the data of weather stations adjacent to the simulated area. Spatial distribution of LAI was obtained from Landsat Thematic Mapper images. The hybrid model describes spatial variability of soil temperature across landscapes and different sensitivity to rising air temperature depending on site-speci®c surface structures, such as LAI and ground litter stores.
Geophysical Research Letters
Mapping the spatiotemporal patterns of soil moisture within heterogeneous landscapes is important for resource management and for the understanding of hydrological processes. A critical challenge in this mapping is comparing remotely sensed or in situ observations from areas with different vegetation cover but subject to the same precipitation regime. We address this challenge by wavelet analysis of multiyear observations of soil moisture profiles from adjacent areas with contrasting plant functional types (grassland, woodland, and encroached) and precipitation. The analysis reveals the differing soil moisture patterns and dynamics between plant functional types. The coherence at high-frequency periodicities between precipitation and soil moisture generally decreases with depth but this is much more pronounced under woodland compared to grassland. Wavelet analysis provides new insights on soil moisture dynamics across plant functional types and is useful for assessing differences and similarities in landscapes with heterogeneous vegetation cover.
Predicting Forest Microclimate in Heterogeneous Landscapes
Ecosystems, 2009
Forest microclimate plays an integral role in ecosystem processes, yet a predictive understanding of its spatial and temporal variability in heterogeneous landscapes is largely lacking. In this study, we used regression kriging (RK) to analyze the degree to which physiographic versus ecological variables influence spatio-temporal variation in understory microclimate conditions. We monitored understory temperature in 200 forest plots within a 274 km 2 environmentally heterogeneous region in northern California (0.55 obs/km 2 ). For each plot location, we measured four physiographic influences (elevation, coastal proximity, potential solar radiation, topographic wetness index) and three ecological drivers (forest patch size, proximity to forest edge, tree abundance). Temperature observations were aggregated to three time scales (hourly, daily, and monthly) to examine temporal variability in microclimate dynamics and its effect on spatial prediction. The obtained prediction models included both physiographic and vegetative effects, although the relative importance of indi-vidual effects varied greatly between the different models. Across time scales, elevation and coastal proximity had the most consistent physiographic effects on temperature, followed by the vegetative effects of forest patch size and distance to forest edge. RK captured significantly more landscapescale variability in understory temperature than a regression-only approach with considerably better model performance at hourly and daily time scales than at a monthly scale. Using varied sampling density scenarios our results also suggest that predictive accuracy drops considerably at densities less than 0.34 obs/km 2 . This research illustrates how geospatial and statistical modeling can be used to distinguish physiographic versus ecological effects on microclimate dynamics and elucidates the spatial and temporal scales that these processes operate.