Autoregressive Moving Average Models of Conifer Crown Profiles

A time-series autoregressive moving average (ARMA) approach was used to develop stochastic models of tree crown profiles for five conifer species of the Sierran mixed conifer habitat type. Models consisted of three components: (1) a polynomial trend; (2) an ARMA model; and (3) random error. A Bayesian information criterion was used to evaluate alternative models. It was found that 70% of the crown profiles could be modeled using first-order ARMA [AR(1) or MA(1)] models, and that an additional 25% could be modeled using a white noise model [(AR(0)]. When the coefficients of the ARMA models were statistically significant, the models proved to be both visually and statistically an improvement over the polynomial trend (a Euclidean model). A binary classification system was used to determine if model type was related to tree or stand characteristics. Using this classification we found that it was possible to relate the appropriate model type to forest tree size and forest stand density with acceptable accuracy

Autoregressive Moving Average Models of Crown Profiles for Two California Hardwood Species

Time-series Autoregressive Moving Average (ARMA) models were employed to model tree crown profiles for two California hardwood species (blue oak and interior live oak). There are three major components of these models: a polynomial trend, an ARMA model, and unaccounted for variation. The polynomial trend was used to achieve a stationary series. For these crown profiles, the use of a quadratic trend resulted in a stationary series for 60% of the profiles. A cubic trend was used for another 23%, and a quartic for 7%. It was found that 80% of the tree crown profiles could be modeled using a first order ARMA model [AR(1), or MA(1)] in conjunction with a polynomial trend and another 10% as a polynomial trend with white noise. When the coefficients of the ARMA models were statistically significant, the models proved to be both visually and statistically an improvement over the polynomial trend. Using a binary classification scheme it was possible to relate the type of ARMA model needed for a crown profile series to tree size and stand characteristics

Modeling Conifer Tree Crown Radius and Estimating Canopy Cover

Models of tree crown radius were developed for several conifer species of California. Typical forest inventory variables (DBH, height, height-to-crown base, crown class, basal area per hectare, and trees per hectare) were considered as independent variables in model development. Models were fitted using both ordinary and weighted least squares methods. It was found that for the species studied, an ordinary least squares linear regression with DBH as the only independent variable was appropriate. For some species studied, the addition of other independent variables provided minor improvements over the model with only DBH. These models of crown radius could be summed to give an estimation of canopy cover. Using crown mapped data, it was possible to test and calibrate these models to predict non-overlapping canopy cover. Linear and non-linear models were considered for calibration. A non-linear model with an upper asymptote seemed to be the best calibration. These models enable an efficient and unbiased method of estimation of canopy cover as an alternative to photointerpretation estimation of cover

Satellite imagery can support water planning in the Central Valley

Most agricultural systems in California’s Central Valley are purposely flexible and intentionally designed to meet the demands of dynamic markets such as corn, tomatoes and cotton. As a result, crops change annually and semiannually, which makes estimating agricultural water use difficult, especially given the existing method by which agricultural land use is identified and mapped. A minor portion of agricultural land is surveyed annually for land-use type, and every 5 to 8 years the entire valley is completely evaluated. We explore the potential of satellite imagery to map agricultural land cover and estimate water usage in Merced County. We evaluated several data types and determined that images from the Moderate Resolution Imaging Spectrometer (MODIS) onboard NASA satellites were feasible for classifying land cover. A technique called “supervised maximum likelihood classification” was used to identify land-cover classes, with an overall accuracy of 75% achievable early in the growing season

An analysis of spatial clustering and implications for wildlife management: a burrowing owl example

Analysis tools that combine large spatial and temporal scales are necessary for efficient management of wildlife species, such as the burrowing owl (Athene cunicularia). We assessed the ability of Ripley’s K-function analysis integrated into a geographic information system (GIS) to determine changes in burrowing owl nest clustering over two years at NASA Ames Research Center. Specifically, we used these tools to detect changes in spatial and temporal nest clustering before, during and after conducting management by mowing to maintain low vegetation height at nest burrows. We found that the scale and timing of owl nest clustering matched the scale and timing of our conservation management actions over a short timeframe. While this study could not determine a causal link between mowing and nest clustering, we did find that Ripley’s K and GIS were effective in detecting owl nest clustering and shows promise for future conservation uses

Relevance and redundancy in fuzzy classification systems

Fuzzy classification systems is defined in this paper as an aggregative model, in such a way that Ruspini classical definition of fuzzy partition appears as a particular case. Once a basic {\em recursive} model has been accepted, we then propose to analyze relevance and redundancy in order to allow the possibility of {\em learning} from previous experiences. All these concepts are applied to a real picture, showing that our approach allows to check quality of such a classification system

Riisinkäärinnän tehostaminen arkkaamossa

Fuzzy classification systems is defined in this paper as an aggregative model, in such a way that Ruspini classical definition of fuzzy partition appears as a particular case. Once a basic {\em recursive} model has been accepted, we then propose to analyze relevance and redundancy in order to allow the possibility of {\em learning} from previous experiences. All these concepts are applied to a real picture, showing that our approach allows to check quality of such a classification system