Classification and boundary vagueness in mapping presettlement forest types

Presettlement forest types were mapped as fuzzy sets from point data representing trees contained in General Land Office survey notes (circa 1850) for Chippewa County, Michigan. The resulting representation agreed with a polygon map of the same forest types at 66 % of the locations (represented as grid cells) in the county. Boundary vagueness was defined in relation to the slope of a linear function fitted to the negative relation between entropy of forest types and distance to polygon boundaries. The similarity between forest type compositions (i.e. classification ambiguity) was shown to account for 55 % of the variation in boundary vagueness

Multi-Resolution Decomposition in Relation to Characteristic Scales and Local Window Sizes Using an Operational Wavelet Algorithm

Data from an IKONOS image acquired over Dallas was used to demonstrate the use of an operational wavelet-based algorithm to examine the performance of different texture measures and window sizes at various resolutions in connection to characteristic scales. It was found that a 63x63 window was the optimal window size, and energy measure produced the highest accuracy. Results from this study suggest that the choice of window size in wavelet-based classification affects the accuracy. Larger window sizes significantly improve the overall accuracy when using homogeneous samples. In the real-world situation, a larger window may not necessarily produce higher accuracy since a larger window tends to cover more land-use and land-cover classes and therefore may miss smaller regions of classes that could lead to poorer accuracy. On the other hand, a smaller window tends to be incomplete in its coverage of texture features that represent a complex class. The classification accuracy can be improved by using more combinations of sub-images at different scales. However, smaller sub-images at the last two levels may lower the classification accuracy.  The characteristic scale of the most complex feature among all selected classes could be the optimal local window size necessary to achieve the highest accuracy.

Measuring the abruptness of patchy ecotones – A simulation-based comparison of landscape pattern statistics

The use of statistics of landscape pattern to infer ecological process at ecotones requires knowledge of the specific sensitivities of statistics to ecotone characteristics. In this study, sets of patch-based and boundary-based statistics were evaluated to assess their suitability as measures of abruptness on simulated ecotone landscapes. We generated 50 realizations each for 25 groups of ecotones that varied systematically in their degree of abruptness and patchiness. Factorial ANOVA was used to evaluate the sensitivity of statistics to the known differences among the simulated groups. Suitability of each index for measuring abruptness was evaluated using the ANOVA results. The statistics were then ranked in order of their suitability as abruptness statistics based on their sensitivity to abruptness, the consistency of the relationship, and their lack of sensitivity to patchiness. The two best statistics for quantifying abruptness were those we developed based on lattice delineation methods, and are called cumulative boundary elements and boundary element dispersion. The results of this research provide support for studies of ecotone process that rely on the interpretation of patch or boundary statistics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43881/1/11258_2004_Article_358202.pd

Artmångfald i boreala fågelsamhällen och hur den påverkas av tredimesionell vegetationsstruktur

Bird populations across the world are in danger with decreasing numbers and more species continually becoming red-listed. One main driver behind this trend is human-caused habitat loss and degeneration, which in particular has been identified as a major threat in forested regions. The importance of forest vegetation structure for bird diversity has been shown in many studies, though typically for small restricted study areas. Here I used a large region of interior boreal Sweden as study area. I used point census count data from the Swedish National Bird Monitoring program combined with recently published nation-covering lidar data, to investigate how bird species richness was affected by 3D forest structure. In total 37 forest-associated bird species were included. Non-parametric random forest models and generalized linear models (GLMs) were used, rendering R2 values of 36% and 15%, respectively. Variation in vegetation density and canopy height were the two most important forest structure features to predict bird species richness. Height evenness, also known as foliage height diversity (FHD), scored low in variable importance despite being considered a significant driver of bird diversity by many authors. A constrained correspondence analysis (CCA) ordination method was performed to explore habitat selection and niche width for individual bird species. Species with similar habitat preferences were nested in the CCA diagram but showed large overlaps, suggesting that there is a signal in the data but also much noise. Thus, separating between habitat generalists and specialists was not possible. For conservation applications and prioritizations, bird species richness is not necessarily a suitable measure. Rather, the contribution to beta and gamma diversity, as well as the specific habitat preferences of rare, red-listed and specialist species, should guide conservation measures and forest management practices. Future studies should extend further towards a landscape-based study design where forest fragmentation and configuration are significant components

Modelling ecological values in heterogeneous and dynamic landscapes with geospatial data

Our surrounding landscape is in a constantly dynamic state, but recently the rate of changes and their effects on the environment have considerably increased. In terms of the impact on nature, this development has not been entirely positive, but has rather caused a decline in valuable species, habitats, and general biodiversity. Regardless of recognizing the problem and its high importance, plans and actions of how to stop the detrimental development are largely lacking. This partly originates from a lack of genuine will, but is also due to difficulties in detecting many valuable landscape components and their consequent neglect. To support knowledge extraction, various digital environmental data sources may be of substantial help, but only if all the relevant background factors are known and the data is processed in a suitable way. This dissertation concentrates on detecting ecologically valuable landscape components by using geospatial data sources, and applies this knowledge to support spatial planning and management activities. In other words, the focus is on observing regionally valuable species, habitats, and biotopes with GIS and remote sensing data, using suitable methods for their analysis. Primary emphasis is given to the hemiboreal vegetation zone and the drastic decline in its semi-natural grasslands, which were created by a long trajectory of traditional grazing and management activities. However, the applied perspective is largely methodological, and allows for the application of the obtained results in various contexts. Models based on statistical dependencies and correlations of multiple variables, which are able to extract desired properties from a large mass of initial data, are emphasized in the dissertation. In addition, the papers included combine several data sets from different sources and dates together, with the aim of detecting a wider range of environmental characteristics, as well as pointing out their temporal dynamics. The results of the dissertation emphasise the multidimensionality and dynamics of landscapes, which need to be understood in order to be able to recognise their ecologically valuable components. This not only requires knowledge about the emergence of these components and an understanding of the used data, but also the need to focus the observations on minute details that are able to indicate the existence of fragmented and partly overlapping landscape targets. In addition, this pinpoints the fact that most of the existing classifications are too generalised as such to provide all the required details, but they can be utilized at various steps along a longer processing chain. The dissertation also emphases the importance of landscape history as an important factor, which both creates and preserves ecological values, and which sets an essential standpoint for understanding the present landscape characteristics. The obtained results are significant both in terms of preserving semi-natural grasslands, as well as general methodological development, giving support to science-based framework in order to evaluate ecological values and guide spatial planning.Ympäröivä maisemamme on alati muuttuvassa tilassa, mutta viime aikoina muutosten nopeus ja niiden vaikutukset ympäristöön ovat kasvaneet. Luontoarvojen kannalta kehitys ei ole ollut pelkästään myönteistä, vaan monin paikoin lajistollisesti arvokkaat elinympäristöt ovat vähentyneet ja yleinen luonnon monimuotoisuus on kaventunut. Vaikka ongelma ja sen laajuus on yleisesti tunnistettu, ovat suunnitelmat ja toimet negatiivisen kehityksen pysäyttämiseksi paljolti keskeneräisiä. Osaltaan tämä johtuu tahtotilan puutteesta, mutta myös siitä että monet arvokkaista maisemakomponenteista ovat hankalasti havaittavia ja puutteellisesti tunnettuja, jolloin niihin ei osata kohdistaa tarvittavaa huomiota. Tässä yhteydessä erilaiset ympäristöön liittyvät digitaaliset tietolähteet voivat auttaa tiedon kartuttamisessa mutta vain, jos tarvittavat taustatekijät tunnetaan ja aineistoja osataan käsitellä soveltuvalla tavalla. Tässä väitöskirjassa keskitytään ekologisesti arvokkaiden maiseman ominaisuuksien tunnistamiseen geospatiaalisten aineistojen avulla, ja suositellaan käyttämään tätä tietoa aluesuunnittelun ja luonnonhoidon tarpeisiin. Tällä tarkoitetaan alueellisesti arvokkaiden lajien ja niiden elinympäristöjen havainnointia paikkatieto- ja kaukokartoitusaineistoja käyttäen, sekä tarkoitukseen sopivien analysointimenetelmien kehittämistä. Tutkimuksen kohteena on lounaissuomalainen maisema hemiboraalisessa kasvillisuusvyöhykkeessä, ja etenkin alueella esiintyvät arvokkaat perinnemaisemat, joilla pitkäkestoinen laidunnus ja hoitotoimenpiteet ovat luoneet monimuotoisen eliölajiston. Tutkimuksessa kehitetään yleistettäviä menetelmiä, ja saatuja tuloksia voidaan soveltaa myös laajempiin käyttötarkoituksiin. Tärkeässä osassa ovat erilaiset tilastollisiin tekijöihin ja muuttujien yhteisvaihteluun perustuvat mallinnusmenetelmät, joilla suuresta määrästä alkuperäisaineistoja erotetaan halutut ominaisuudet. Mallinnukset tehdään yhdistämällä useita maiseman ajallisia ja alueellisia muutoksia kuvaavia paikkatietoaineistoja. Väitöskirjan tulokset osoittavat, että maiseman dynamiikan ymmärtäminen ja muutosten tulkinta on olennaista luontoarvoiltaan tärkeiden kohteiden löytämiseksi. Tämä vaatii tietoa tutkitun ilmiön syntymekanismeista ja tehtävään käytetyistä aineistoista, mutta usein myös havainnoinnin kohdistamista riittävän yksityiskohtaiseen vaihteluun jonka avulla pirstoutuneita ja osin päällekkäisiä maisemakomponentteja voidaan tunnistaa. Näiden syiden takia valmiiksi luokitellut aineistot ovat usein liian yleistettyjä soveltuakseen sellaisenaan pienialaisten maisemakohteiden löytämiseen, mutta niitä voidaan kuitenkin hyödyntää osana pidempää työketjua. Tutkimuksen tulokset tukevat sitä tulkintaa, että maiseman nykytilaa edeltävät muutokset ovat olennaisia ekologisia arvoja maisemassa säilyttäviä tekijöitä.Tästä syystä on erityisen tarpeellista tuntea maiseman menneisyys osana nykyistä maisemarakennetta. Saadut tulokset ovat merkittäviä niin perinnemaisemien säilyttämisen kuin maisemaekologisen tutkimuksen menetelmäkehityksenkin kannalta, ja ne tukevat paikkatietoon ja tieteelliseen tutkimukseen perustuvaa luonnonsuojelua ja aluesuunnittelua.Siirretty Doriast

Changing Primary Production and Biomass in Heterogeneous Landscapes: Estimation and Uncertainty Based on Multi-Scale Remote Sensing and GIS Data.

Changes in vegetation gross primary production (GPP) and forest aboveground standing biomass C (biomass) were investigated to better understand the impacts of human and natural disturbances on carbon uptake and storage in two northern hemisphere terrestrial ecosystems, which represent primarily forest-dominated and primarily urbanizing landscapes, respectively. Research focused on evaluating changes in (a) GPP in Southeastern Michigan, where the dynamics of vegetation carbon flux are tied closely to urbanization and human development characteristics; and (b) biomass in Eastern Siberia, where biomass changes are strongly affected by disturbance and regrowth. Both projects exploited remotely sensed data and biophysical or ecosystem models to estimate GPP and biomass. Altering the scale of spatial data and summary units may generate different results. The scaling effects need to be characterized to evaluate scale-related uncertainties associated with carbon estimates. In Michigan, sensitivity of inferences about productivity trends among several development types to levels of aggregation in the Census housing data were examined from the block-group to county scales. In Siberia, impacts of changes in the remote sensing observational scale (i.e., sensor resolution) on the estimated biomass trends were analyzed at resolutions from 60 to 960 meters. Results showed that GPP increased by 53 g C m-1 in Southeastern Michigan and biomass increased by 3.9 Mg C ha-1 in Eastern Siberia between 1990 and 2000, and that more productive landscapes resulted from tree-cover expansion and forest recovery, respectively. These results corroborate previous findings of increased vegetation activity throughout the northern hemisphere in 1990s. With respect to scaling effects on carbon estimates, in Michigan, relationships between the estimated GPP trends and development types remained consistent across Census scales; and, in Siberia, degradation of remote sensing resolution resulted in the overestimation of changes in biomass by 9-69% at the 960-meter resolution. Results suggested that, for carbon analysis across broader geographic extents (e.g., regional- to national-scale estimation), coarser Census scales up to the county level may be used to evaluate carbon trends by development intensities, while remote sensing data at coarser resolutions may not maintain accuracy of the estimated carbon trends relative to finer resolution data.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57600/2/tzhao_1.pd

Structuring a Wayfinder\u27s Dynamic and Uncertain Environment

Wayfinders typically travel in dynamic environments where barriers and requirements change over time. In many cases, uncertainty exists about the future state of this changing environment. Current geographic information systems lack tools to assist wayfinders in understanding the travel possibilities and path selection options in these dynamic and uncertain settings. The goal of this research is a better understanding of the impact of dynamic and uncertain environments on wayfinding travel possibilities. An integrated spatio-temporal framework, populated with barriers and requirements, models wayfinding scenarios by generating four travel possibility partitions based on the wayfinder\u27s maximum travel speed. Using these partitions, wayfinders select paths to meet scenario requirements. When uncertainty exists, wayfinders often cannot discern the future state of barriers and requirements. The model to address indiscemibility employs a threevalued logic to indicate accessible space, inaccessible space, and possibly inaccessible space. Uncertain scenarios generate up to fifteen distinct travel possibility categories. These fifteen categories generalize into three-valued travel possible partitions based on where travel can occur and where travel is successful. Path selection in these often-complex environments is explored through a specific uncertain scenario that includes a well-defined initial requirement and the possibility of an additional requirement somewhere beforehand. Observations from initial path selection tests with this scenario provide the motivation for the hypothesis that paths arriving as soon as possible to well-defined requirements also maximize the probability of success in meeting possible additional requirements. The hypothesis evaluation occurs within a prototype Travel Possibility Calculator application that employs a set of metrics to test path accessibility in various linear and planar scenarios. The results did not support the hypothesis, but showed instead that path accessibility to possible additional requirements is greatly influenced by the spatio-temporal characteristics of the scenario\u27s barriers

Multiscale Landform Characterization for Land Use Evaluation using Fuzzy Sets

A multi-scale geomorphometric landform model was created through the use of fuzzy semantic import models and fuzzy overlay to measure distribution of landforms within parcels of the Conservation Reserve Program in a portion of the Delaware River Sub-basin in Northeast Kansas. Different fuzzy logic operators (intersect, algebraic mean, and fuzzy gamma) were used to test the impact of different model mechanisms on the resulting distributions of crisp and fuzzy membership values, and classification uncertainty measured by entropy values. Across scales (900 m2 to 16,900 m2 window sizes), only one crisp class (drainages) showed an optimal scale for detection area. The statistical distribution of fuzzy membership values were significantly different between the same classes derived from different overlay operators, but this had limited impact on the agreement between crisp landforms derived from the three operators at a single scale. Within each pairing of overlay operator and scale, the landform classes backslopes and flats had the highest proportional representation of classes at most entropy levels (0.95, 0.90, 0.85, and 0.75) except for locations within the single-highest entropy level (0.99), where the class proportions were more variable. This is significant, as both of these classes were dominant at different scales (backslopes at finer scales, and flats at coarser scales) within CRP Parcels. The fuzzy and multi-scale approach provides flexibility in assessing class-level uncertainty that had been previously unaddressed in the use of geomorphometric systems for mapping, modeling, and applied management applications