Spatial pattern analysis for monitoring protected areas

The management of diverse biota within protected areas is affected by both land cover change within a protected area and habitat loss and fragmentation in the surrounding landscape. Satellite images provide a synoptic view of land cover patterns, but the use of such imagery requires careful consideration of sensor type, resolution, extent, and the metrics used to quantify ecologically significant change. We examined these factors for landscape monitoring applications in four small National Parks near Washington, DC: Antietam National Battlefield, Catoctin Mountain Park, Prince William Forest Park and Rock Creek Park. Using 4 m Ikonos, 10 m SPOT, 15 m pan-sharpened Landsat ETM+ and 30 m Landsat ETM+ imagery, the parks and surrounding areas were mapped to National Land Cover system classes. For each park, we examined four methods for defining map extent, including park administrative boundaries, two variable buffer widths, and watershed boundaries, and then analyzed patterns of forest habitat for the maps using a graph theoretic approach (critical dispersal threshold distance) and common landscape metrics (number of patches, percent forest, forest edge density, and forest area-weighted mean patch size). As expected, landscape metrics for maps derived at differing resolutions varied significantly, but map extent often yielded even larger differences. We found that for most applications, coarser scale data (e.g., 30 m Landsat) are adequate for characterizing landscape pattern, although ultimately data from multiple sensors may be appropriate or necessary based on different objectives of landscape monitoring (e.g., mapping single trees vs. forest stands) and the scale at which a resource of interest interacts with the larger landscape (e.g., birds vs. herptiles). Our results provide a strong caution regarding the practical issues associated with combining data sources from multiple satellite sensors for monitoring applications.     

A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery

Based on very high resolution satellite images, object-based classification methods can be used to produce large scale maps for forest management. These new products require a method to derive quantitative information about the accuracy and precision of delineated boundaries. This assessment would complement any measure of thematic accuracy derived from the confusion matrix. This study aims to assess the positional quality of the boundaries between different landscape units produced by automated segmentation of IKONOS and SPOT-5 satellite images over temperate forests. A robust method was developed to assess the accuracy and the precision of the forest boundaries, respectively measured by the bias and the standard deviation. The two main sources of positional error, namely residual parallax and automatic segmentation, were independently assessed. Positional errors caused by the residual parallax were quantified using a 3D model. Forest stand boundaries generated by automatic segmentation were compared to corresponding visual delineations. The results showed that residual parallax was the major source of positive bias (area overestimation) along forest/non-forest boundaries and depended on the interactions between forest stand patterns and sensor viewing angles. Due mainly to tree shade, the automatic segmentation also produced a positive bias on forest areas, which remained under 1 m for both IKONOS-2 and SPOT-5 images. Standard deviation did not increase linearly with pixel size and was influenced by the nature of the boundary. Production of 1:20,000 scale forest maps from very high resolution satellite data clearly requires acquisition of near nadir imagery or knowledge of landscape object height for true orthorectification. In these cases, IKONOS-2 segmentation outputs were found to correspond with 1:20,000 scale map specification, and SPOT-5 imagery with 1:30,000 scale.     

Distinguishing wetland vegetation and channel features with object-based image segmentation

Mapping landscape features within wetlands using remote-sensing imagery is a persistent challenge due to the fine scale of wetland pattern variation and the low spectral contrast among plant species. Object-based image analysis (OBIA) is a promising approach for distinguishing wetland features, but systematic guidance for this use of OBIA is not presently available. A sensitivity analysis was tested using OBIA to distinguish vegetation zones, vegetation patches, and surface water channels in two intertidal salt marshes in southern San Francisco Bay. Optimal imagery sources and OBIA segmentation settings were determined from 348 sensitivity tests using the eCognition multiresolution segmentation algorithm. The optimal high-resolution (≤1 m) imagery choices were colour infrared (CIR) imagery to distinguish vegetation zones, CIR or red, green, blue (RGB) imagery to distinguish vegetation patches depending on species and season, and RGB imagery to distinguish surface water channels. High-resolution (1 m) lidar data did not help distinguish small surface water channels or other features. Optimal segmentation varied according to segmentation setting choices. Small vegetation patches and narrow channels were more recognizable using small scale parameter settings and coarse vegetation zones using larger scale parameter settings. The scale parameter served as a de facto lower bound to median segmented object size. Object smoothness/compactness weight settings had little effect. Wetland features were more recognizable using high colour/low shape weight settings. However, an experiment on a synthetic non-wetland image demonstrated that, colour information notwithstanding, segmentation results are still strongly affected by the selected image resolution, OBIA settings, and shape of the analysis region. Future wetland OBIA studies may benefit from strategically making imagery and segmentation setting choices based on these results; such systemization of future wetland OBIA approaches may also enhance study comparability.     

A temporal analysis of urban forest carbon storage using remote sensing

Quantifying the carbon storage, distribution, and change of urban trees is vital to understanding the role of vegetation in the urban environment. At present, this is mostly achieved through ground study. This paper presents a method based on the satellite image time series, which can save time and money and greatly speed the process of urban forest carbon storage mapping, and possibly of regional forest mapping. Satellite imagery collected in different decades was used to develop a regression equation to predict the urban forest carbon storage from the Normalized Difference Vegetation Index (NDVI) computed from a time sequence (1985-1999) of Landsat image data. This regression was developed from the 1999 field-based model estimates of carbon storage in Syracuse, NY. The total carbon storage estimates based on the NDVI data agree closely with the field-based model estimates. Changes in total carbon storage by trees in Syracuse were estimated using the image data from 1985, 1992, and 1999. Radiometric correction was accomplished by normalizing the imagery to the 1999 image data. After the radiometric image correction, the carbon storage by urban trees in Syracuse was estimated to be 146,800 tons, 149,430 tons, and 148,660 tons of carbon for 1985, 1992, and 1999, respectively. The results demonstrate the rapid and cost-effective capability of remote sensing-based quantitative change detection in monitoring the carbon storage change and the impact of urban forest management over wide areas.     

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

Ecosystem models can be used to estimate potential net primary production (pNPP) using GIS data, and remote sensing input of actual forest leaf area to such models can provide estimates of current actual net primary production (aNPP) . Comparisons of pNPP and aNPP for a given site or regional landscape can be used to identify forest stands for different management treatments, and may provide new information on wildlife habitat, forest diversity and growth characteristics. Leaf area estimates may be obtained from satellite imagery through correlation with physiologically-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI). However, in areas with high Leaf Area Index (LAI), vegetation indices usually saturate at leaf areas greater than about 4. In predominantly deciduous (hardwood) and mixedwood stands remote sensing estimates may be influenced by understory and other factors. We examined digital Landsat TM imagery and GIS data in the Fundy Model Forest of southeastern New Brunswick to determine relations to forest leaf area index within different stand structures or covertypes. The image data were stratified using GIS covertype information prior to development of LAI predictive equations using spectral reflectance, and the prediction of LAI from Landsat TM imagery was improved with reference to estimates of stem density which are standard forest inventory information contained in GIS databases. Actual stand LAI was compared to assumed maximum LAI values for several species and sites using an ecosystem process model (BIOME-BGC) which relies on climate, soils and topographic information also obtained from the GIS. Subsequent comparison of pNPP and aNPP revealed that even disturbed sites in this environment can reach close to maximum site potential. Specific sites with suboptimal species composition were identified. A future refinement of this approach is to classify the imagery independently of the GIS, which assumes a homogeneous covertype for each polygon in the system, and thus improve still further the aNPP estimates through higher covertype and LAI estimation accuracy.     

Mapping National Park landscape from ground,air and space

Aerial photograph interpretation and field mapping were used in a series of experiments to evaluate the use of Landsat and Système Probatoire pour l'Observation de la Terre (SPOT) satellite imagery for landscape mapping. The 'Monitoring Landscape Change in the National Parks' (MLCNP) Project mapped landscape in each of the National Parks of England and Wales in terms of 38 land cover classes with significant visual impact. The main source of data was aerial photography but satellite imagery for selected areas was also analysed. It was found that single-date multi-spectral imagery could be classified to an acceptable level of agreement with ground data only if the 38 sub-classes of the interpretation scheme were grouped into the seven main class headings. Visual interpretation of SPOT panchromatic imagery at the 38 sub-class level proved comparable with aerial photograph interpretation for an area of the North York Moors. This paper describes the approaches taken in data analysis and presents the main results obtained. The use of confusion matrices allowed measurements of agreement to be made between the three sources of data. A significant problem in mapping landscape was to arrive at unambiguous class definitions when many of the categories had no clear boundaries on the ground. Confusion matrix analysis, together with the use of a hierarchical classification scheme, allowed links to be made between data collected from ground, air and space. Some classification problems were attributable to all sources of data due to inherent difficulties with the classification system.     

Individual tree detection based on variable and fixed window size local maxima filtering applied to IKONOS imagery for even-aged Eucalyptus plantation forests

Detection of individual trees remains a challenge for forest inventory efforts especially in homogeneous, even-aged plantation scenarios. Airborne imagery has mainly been used for detection of individual trees using local maxima filtering, as point spread function and signal-to-noise ratio are smaller than with satellite-borne imagery. This led to the development of a novel approach to local maxima filtering for tree detection in plantation forests in KwaZulu-Natal, South Africa, using satellite remote sensing imagery. Our approach is based on Gaussian smoothing for noise elimination and image classification, that is, natural break classification to determine the threshold for removing pixels of extremely bright and dark areas in the imagery. These pixels are assumed to belong to the background and hinder the search for tree peaks. A semivariogram technique was applied to determine variable window sizes for local maxima filtering within a plantation stand. A fixed window size for local maxima filtering was also applied using pre-determined tree spacing. Evaluation of the various approaches was based on aggregated assessment methods. The overall accuracy using a variable window size was 85%, root mean square error (RMSE)?=?189 trees, whereas a fixed window size resulted in an accuracy of 80%, RMSE?=?258 trees. The approach worked remarkably well in mature forest stands as compared to young forest stands. These results are encouraging for temperate–warm climate plantation forest companies, who deal with even-aged, broadleaf plantations and forest inventory practices that require assessment 1 year before harvesting.     

Predicting temperate conifer forest successional stage distributions with multitemporal Landsat Thematic Mapper imagery

Forest succession is a fundamental ecological process which can impact the functioning of many terrestrial processes, such as water and nutrient cycling and carbon sequestration. Therefore, knowing the distribution of forest successional stages over a landscape facilitates a greater understanding of terrestrial ecosystems. One way of characterizing forest succession over the landscape is to use satellite imagery to map forest successional stages continuously over a region. In this study we use a forest succession model (ZELIG) and a canopy reflectance model (GORT) to produce spectral trajectories of forest succession from young to old-growth stages, and compared the simulated trajectories with those constructed from Landsat Thematic Mapper (TM) imagery to understand the potential of mapping forest successional stages with remote sensing. The simulated successional trajectories captured the major characteristics of observed regional mean succession trajectory with Landsat TM imagery for Tasseled Cap indices based on age information from the Pacific Northwest Forest Inventory and Analysis Integrated Database produced by Pacific Northwest Research Station, USDA Forest Service. Though the successional trajectories are highly nonlinear in the early years of succession, a linear model fits well the regional mean successional trajectories for brightness and greenness due to significant cross-site variations that masked the nonlinearity over a regional scale (R² = 0.8951 for regional mean brightness with age; R² = 0.9348 for regional mean greenness with age). Regression analysis found that Tasseled Cap brightness and greenness are much better predictors of forest successional stages than wetness index based on the data analyzed in this study. The spectral history based on multitemporal Landsat imagery can be used to effectively identify mature and old-growth stands whose ages do not match with remote sensing signals due to change occurred during the time between ground data collection and image acquisition. Multitemporal Landsat imagery also improves prediction of forest successional stages. However, a linear model on a stand basis has a limited predictive power of forest stand successional stages (adjusted R² = 0.5435 using the Tasseled Cap indices from all four images used in this study) due to significant variations in remote sensing signals for stands at the same successional stage. Therefore, accurate prediction of forest successional stage using remote sensing imagery at stand scale requires accounting for site-specific factors influence remotely sensed signals in the future.     

斑块状植被遥感检测研究进展

斑块状植被是世界上干旱—半干旱区常见的景观类型,对于它们的形成、结构和演替研究能够提高人们对干旱—半干旱地区生态系统动态及其重要的生态水文过程的理解,具有重要的理论研究意义和应用价值.传统的基于地面调查和长期定位观测的方法观测范围有限,已无法满足目前区域斑块状植被分布及其空间格局特征研究的需要.利用遥感技术快速重复获取...     

Ecological fingerprinting of ecosystem succession: Estimating secondary tropical dry forest structure and diversity using imaging spectroscopy

We evaluated the use of EO-1 Hyperion hyperspectral satellite imagery for mapping structure and floristic diversity in a Neotropical tropical dry forest as a way of assessing a region's ecological fingerprint. Analysis of satellite imagery provides a means to spatially appraise the dynamics of the structure and diversity of the forest. We derived optimal models for mapping canopy height, live aboveground biomass, Shannon diversity, basal area and the Holdridge Complexity Index from a dry season image. None of the evaluated models adequately estimated stem or species density. Due to the dynamic nature of the leaf phenology we found that for the application of remote sensing in Neotropical dry forests, the spectro-temporal domain (changes in the spectral signatures over time-season) must be taken into account when choosing imagery. The analyses and results presented here provide a means for rapid spatial assessment of structure and diversity characteristics from the microscale site level to an entire region.     

Evaluating temporal changes using Multi-Spectral Scanner and Thematic Mapper data on the landscape of a natural reserve: The New Jersey Pine Barrens,a case study

Natural disturbance suppression and anthropogenic perturbations have altered the composition and structure of the New Jersey Pinelands National Reserve (NJPNR). The combination of satellite remote sensing imagery and GIS provided the means to map and monitor land cover change at landscape level scales in the NJPNR. The Pinelands has experienced a change in landcover, with the mixed deciduous forest replacing the pine forest community.     

Monitoring change in the spatial heterogeneity of vegetation cover in an African savanna

The extent to which a new intensity‐dominant scale approach to characterizing spatial heterogeneity from remote sensing imagery can be used to monitor two‐dimensional changes (i.e. variability and patch size) in the spatial heterogeneity of vegetation cover (estimated from a Landsat Thematic Mapper (TM)‐derived Normalized Difference Vegetation Index (NDVI)) was tested in the Sebungwe region in north‐western Zimbabwe between 1984 and 1992. Intensity of spatial heterogeneity (i.e. the maximum variance obtained when a spatially distributed landscape property is measured with a successively increasing window size) was used to measure variability in vegetation cover. Dominant scale of spatial heterogeneity (i.e. the window size at which the maximum variance in the landscape property is measured) was used to measure the dominant patch dimension of vegetation cover. This approach was validated by testing whether the observed change in the dominant scale and intensity of spatial heterogeneity of vegetation cover between 1984 and 1992 was related to changes in the proportion of arable fields. The results also indicated that there was a significant relationship (p<0.05) between changes in the proportion of agricultural fields and changes in the intensity and the product of intensity and dominant scale of spatial heterogeneity (intensity×dominant scale), suggesting that the new approach captures observable changes in the landscape, and is not an artefact of the data. The results imply that the intensity‐dominant scale approach to quantifying spatial heterogeneity in remote sensing imagery can be used for a comprehensive characterization and monitoring of changes in landscape condition.     

Assessing the response of the MODIS vegetation indices to landscape disturbance in the forested areas of the legal Brazilian Amazon

In this study we assessed the impacts of forest fragmentation on the Amazon landscape using remote sensing techniques. Landscape disturbance, obtained for an area of approximately 3.5 × 10⁶ km² through simple spatial metrics (i.e. number of fragments, mean fragment area and border size) and principal component transformation were then compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) seasonal responses. As expected, higher disturbance values prevail in the southern border of the Amazon, near the intensively converted deforestation arc, and close to the major roads. NDVI seasonal responses more closely follow human-induced patterns, i.e. forest remnants from areas more intensively converted were associated with higher NDVI seasonal values. The significant correlation between NDVI seasonal responses and landscape disturbances were corroborated through analysis of geographically weighted regression (GWR) parameters and predictions. On the other hand, EVI seasonal responses were more complex with significant variations found over intact, less fragmented forest patches, thus restricting its utility to assess landscape disturbance. Although further research is needed, our results suggest that the degree of fragmentation of the forest remnants can be remotely sensed with MODIS vegetation indices. Thus, it may become possible to upscale field-based data on overall canopy condition and fragmentation status for basin-wide extrapolations.     

Spatial and temporal patterns of gap dominance by low-canopy lianas detected using EO-1 Hyperion and Landsat Thematic Mapper

Woody lianas are critical to tropical forest dynamics because of their strong influence on forest regeneration, disturbance ecology, and biodiversity. Recent studies synthesizing plot data from the tropics indicate that lianas are increasing in both abundance and importance in tropical forests. Moreover, lianas exhibit competitive advantages over trees in elevated CO₂ environments and under strong seasonal droughts, suggesting that lianas may be poised to increase not only in abundance but also in spatial distribution in response to changing climate. We used a combination of high-resolution color-infrared videography and hyperspectral imagery from EO-1 Hyperion to map low-lying lianas in Noel Kempff Mercado National Park (NKMNP) in the Bolivian Amazon. Evergreen liana forests comprise as much as 14% of the NKMNP landscape, and low-stature liana patches occupy 1.5% of these forests. We used change vector analysis (CVA) of dry season Landsat TM and ETM+ imagery from 1986 and 2000 to determine changes in liana-dominated patches over time and to assess whether those patches were regenerating to canopy forest. The spatial distribution of liana patches showed that patches were spatially aggregated and were preferentially located in proximity to waterways. The CVA results showed that most of the dense liana patches increased in brightness and greenness and decreased in wetness over the 14 years of the change analysis, while non-liana forest patches changed less and in more random directions. Persistent liana patches increased in area by an average of 59% over the time period. In comparison, large burned areas appeared to recover completely to canopy forest in the same time period. This suggests that the dense liana patches of NKMNP represent an alternative successional pathway characterized not by tree regeneration but rather by a stalled state of low-canopy liana dominance. This research supports hypotheses that liana forests can be a persistent rather than transitional component of tropical forests, and may remain so due to competitive advantages that lianas enjoy under changing climatic conditions.     

Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation

High spatial resolution remotely sensed data has the potential to complement existing forest health programs for both strategic planning over large areas, as well as for detailed and precise identification of tree crowns subject to stress and infestation. The area impacted by the current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has increased 40-fold over the previous 5 years, with approximately 8.5 million ha of forest infested in 2005. As a result of the spatial extent and intensity of the outbreak, new technologies are being assessed to help detect, map, and monitor the damage caused by the beetle, and to inform mitigation of future beetle outbreaks. In this paper, we evaluate the capacity of high spatial resolution QuickBird multi-spectral imagery to detect mountain pine beetle red attack damage. ANOVA testing of individual spectral bands, as well as the Normalized Difference Vegetation Index (NDVI) and a ratio of red to green reflectance (Red-Green Index or RGI), indicated that the RGI was the most successful (p < 0.001) at separating non-attack crowns from red attack crowns. Based on this result, the RGI was subsequently used to develop a binary classification of red attack and non-attack pixels. The total number of QuickBird pixels classified as having red attack damage within a 50 m buffer of a known forest health survey point were compared to the number of red attack trees recorded at the time of the forest health survey. The relationship between the number of red attack pixels and observed red attack crowns was assessed using independent validation data and was found to be significant (r² = 0.48, p < 0.001, standard error = 2.8 crowns). A comparison of the number of QuickBird pixels classified as red attack, and a broader scale index of mountain pine beetle red attack damage (Enhanced Wetness Difference Index, calculated from a time series of Landsat imagery), was significant (r² = 0.61, p < 0.001, standard error = 1.3 crowns). These results suggest that high spatial resolution imagery, in particular QuickBird satellite imagery, has a valuable role to play in identifying tree crowns with red attack damage. This information could subsequently be used to augment existing detailed forest health surveys, calibrate synoptic estimates of red attack damage generated from overview surveys and/or coarse scale remotely sensed data, and facilitate the generation of value-added information products, such as estimates of timber volume impacts at the forest stand level.     

Locating Amazonian Dark Earths (ADE) using vegetation vigour as a surrogate for soil type

Amazonian Dark Earths (ADE) are patches of archaeological soils scattered throughout the Amazon Basin. These soils are a mixture of charcoal, nutrient vegetable matter and the underlying Oxisol soil. ADE are extremely fertile in comparison to the surrounding soils and they are sought after by local residents for agricultural food production. Research is being conducted to learn how ADE were created and to explore the possibility of replicating them to sequester carbon and to reclaim depleted soils in the Amazon Basin. A factor limiting the success of this research is our current inability to locate ADE sites hidden beneath the tropical forest canopy. We use annual time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) satellite imagery from 2001 to 2005 and harmonic analysis (HA) to examine the spectral differences between forest vegetation growing on ADE and forest vegetation growing on non-ADE. There is a significant difference between the reflectances of vegetation growing on the two soil types, due primarily to lower EVI values over ADE during the dry season (multiple analysis of variance (MANOVA) p-value?=?0.040). A logistic model is used to create a predictive map of ADE location.     

Constraints on using AVHRR composite index imagery to study patterns of vegetation cover in boreal forests

A wide range of techniques are being developed to map vegetation cover types using multi-date imagery from the Advanced Very High Resolution Radiometer. To date, these techniques do not account for severe constraints which exist for the world's boreal forest. Using composite AVHRR imagery collected over Alaska, we demonstrate how several factors influence the time-series normalized vegetation difference index (NDVI) signatures developed for the boreal forests in this region, including the effects of: (1) clouds and atmospheric haze; (2) climate variations on plant phenology; (3) fire on forest succession; and (4) forest stand patch size with respect to system resolution. Based on the analysis of AVHRR composite data from Alaska, the results of this study show: (1) clouds and haze have distinct effects on the intra-seasonal NDVI signature; (2) there are significant interseasonal variations in NDVI signatures caused by variations in the length of the growing season as well as variations in precipitation and moisture during the growing season; (3) disturbances affect large areas in interior Alaska and forest succession after fire results in significant variations in the inter-seasonal NDVI signatures; and (4) much of the landscape in interior Alaska consists of heterogeneous patches of forest which are much smaller than the resolution cell size of the AVHRR sensor, resulting in significant sub-pixel mixing. Based on these findings, the overall conclusion of this study is scientists using AVHRR to map land cover types in boreal regions must develop approaches which account for these sources of variation.     

Evaluating the complementarity of acoustic and satellite remote sensing for seagrass landscape mapping

Accurate maps representing seagrass spatial distribution are essential components for effective monitoring and management of coastal vegetated habitats. Satellite and acoustic remote sensing provide valuable spatial data for seagrass mapping, though few studies have evaluated the complementarity of these methods. In this study, the complementarity of seagrass mapping was assessed through comparison of acoustic and satellite remote-sensing data sets. QuickBird^® satellite imagery representing the seagrass landscape of the Richibucto estuary, New Brunswick, Canada, was classified through an object-based procedure and evaluated against a single-beam sonar data set. Acoustic percentage cover values were classified into binary presence/absence format through the application of a decision threshold, allowing comparison with satellite data using the error matrix and derived metrics. Though the binary satellite classification resulted in relatively high accuracy compared with independent ground reference data, agreement between satellite and acoustic data sets was limited. Local differences in seagrass prevalence and patchiness affected classification accuracy, highlighting the potential for under- or overestimating seagrass cover when applying bay-scale classification to areas with different landscape structure. These results emphasize the importance of landscape context in seagrass mapping. Satellite and acoustic remote sensing were seen to fundamentally differ in their depiction of the landscape. Comparison of multiple remote-sensing methods allowed for assessment of complementarity as well as ecologically relevant insight to seagrass spatial dynamics, with implications for mapping and monitoring of seagrass habitats.     

Spatial pattern analysis of land cover change trajectories in Tarim Basin,northwest China

This study attempts to develop a methodology to quantify spatial patterns of land cover change using landscape metrics. First, multitemporal land cover types are derived based on a unified land cover classification scheme and from the classification of multitemporal remotely sensed imagery. Categorical land cover change trajectories are then established and reclassified according to the nature and driving forces of the change. Finally, spatial pattern metrics of the land cover change trajectory classes are computed and their relationships to human activities and environmental factors are analysed. A case study in the middle reach of Tarim River in the arid zone of China from 1973 to 2000 shows that during the 30‐year study period, the natural force is dominant in environmental change, although the human impact through altering water resources and surface materials has increased dramatically in recent years. The human‐induced change trajectories generally show lower normalized landscape shape index (NLSI), interspersion and juxtaposition index (IJI) and area‐weighted mean patch fractal dimension (FARC_AM), indicating greater aggregation, less association with others and simpler and larger patches in shape, respectively. The results suggest that spatial pattern metrics of land cover change trajectories can provide a good quantitative measurement for better understanding of the spatio‐temporal pattern of land cover change due to different causes.     

Remote detection of invasive Melaleuca trees (Melaleuca quinquenervia) in South Florida with multispectral IKONOS imagery

The distribution of invasive Melaleuca (Melaleuca quinquenervia (Cav.) S.T. Blake) was mapped using 4‐m spatial resolution, multispectral IKONOS imagery in an area of south Florida along the eastern edge of Everglades National Park. Detection of Melaleuca stands was achieved using a back‐propagation neural network classifier, which allowed identification of dense stands, but in some instances misclassified other woody canopies as Melaleuca. The use of IKONOS multispectral imagery to detect low‐density occurrences of Melaleuca appears limited relative to traditional methods of aerial photographic interpretation. However, analysis of landscape‐level distribution of moderate‐to‐dense Melaleuca, using Fragstats, indicated a highly aggregated Melaleuca distribution relative to other woody vegetation patches. The distribution of Melaleuca stands was associated with cultural features in the suburban environment such as canals and roads, which may act as dispersal corridors for seeds. Thus, classified IKONOS imagery may be useful for inferring landscape patterns that relate to the persistence and spread of Melaleuca and other invasive species.