Class-level Landscape Metrics Research Articles

A landscape metrics-based sample weighting approach for forecasting land cover change with deep learning models

Unaddressed imbalance of multitemporal land cover (LC) data reduces deep learning (DL) model usefulness to forecast changes. To manage geospatial data imbalance, there is a lack of specialized cost-sensitive learning strategies available. Sample weights are typically derived from training instance frequencies which disregard spatial pattern complexities. Therefore, this study proposes a geospatial sample weighting approach underpinned by class-level landscape metrics (LSMs) to assign importance to categories based on relative indicators of spatial form. A case study demonstrates the application and effects of the LSM-based sample weighting approach for projecting LC changes of a region in British Columbia, Canada. Four spatiotemporal DL models are provided weighted training samples including multitemporal LC data and explanatory factors. Sample weights calculated from indicators of patch density, shape irregularity and shape heterogeneity improved figure of merit and related measures over baseline configurations. This study contributes to LC change data imbalance management strategies for DL models.

Effect of landscape pattern of urban surface evapotranspiration on land surface temperature

Land surfaces in urban areas can be classified into artificial surfaces (impervious surface such as buildings and roads) and natural surfaces (vegetation, soil and water), and the natural surfaces have important influence on the urban environmental thermal regulation. Current research on urban spatial characteristics mostly focuses on the regulation of urban green spaces to mitigate the urban heat island effect and rarely explores the effect of the landscape pattern of urban natural surface evapotranspiration (ET) on land surface temperature (LST). Xuzhou, China, was selected as the study area, and the urban RS-PM and improved mono-window algorithm were applied to invert ET and LST values based on Landsat 8 Operational Land Imager and Thermal Infrared Sensor images, respectively. The study area was divided into five levels (LV5 to LV1) from high to weak, according to the ET intensity (ETi), four landscape-level (SHDI, SHEI, PD, and CONTAGION) and five class-level landscape metrics (PLAND, LPI, LSI, AI, and COHESION) were extracted based on the moving-window approach. Bivariate Moran's I was then used to calculate the spatial correlation between each landscape metric and LST. The weighted impact of each landscape metric on LST was obtained using principal component analysis and multiple regression. The results indicate that only five class-level metrics of high (LV5) and sub-high (LV4) ETi patches had significant negative spatial correlations with LST, and the weighted impacts of the high ETi patch metrics were all greater than those of the sub-high ETi patches. The weighted impacts of all class-level metrics that can mitigate LST were ranked as COHESION_LV5 > PLAND_LV5 > LPI_LV5 ≈ AI_LV5 > LSI_LV5 > LSI_LV4 > PLAND_LV4 > COHESION_LV4 > LPI_LV4 > AI_LV4. These findings revealed the importance of ET spatial pattern for urban thermal environment regulation, which may provide a new perspective in urban planning and environmental governance.

Analysis of the association between image resolution and landscape metrics using multi-sensor LULC maps

This study aims to investigate the changes in landscape metrics with varying spatial resolution from Sentinel-2 (10 m), SPOT 7 (1.5 m), Pleaides (0.5 m), and Worldview-4 (0.3 m) images. We implemented Geographic Object-Based Image Analysis (GEOBIA) techniques to all images to identify 21 land use and land cover (LULC) classes, which were then used to calculate several landscape metrics. We performed the Welch hypothesis testing on the class-level landscape metrics and applied Standardized Principal Component Analysis (PCA) with the correlation matrix to reveal the multivariate pattern of landscape metrics. Our results showed that 10 m and even the 1.5 m spatial resolutions cannot guarantee the identification of all LULC classes, and class areas change with varying spatial resolution (sometimes with 200% differences). Sentinel-2 images have some limitations, specifically from the landscape ecological planning perspective; on the other hand, Pleaides and Worldview-4 seem good alternatives to understand habitats’ viability and landscape isolation/connectivity.

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

With the widespread population growth in cities, anthropogenic influences inevitably lead to natural disturbances. The metropolitan area of Istanbul, with its rapid urbanization rate, has faced intense pressure regarding the sustainability of urban habitats. In this context, landscapes comprising patches affected by various disturbances and undergoing temporal changes must be analyzed, in order to assess city-related disturbances. In this study, the main objective was to understand how urbanization changed the function of the spatial distribution of the urban mosaic and, more specifically, its relationship with the size, shape, and connection among land-use classes. For this purpose, we took Besiktas, a district of Istanbul, as the study area. We evaluated the landscape pattern of the urban environment in two stages. First, we used medium-resolution satellite imagery to reveal the general interactions in the urbanization process. Landscape- and class-level landscape metrics were selected to quantify the landscape connectivity, and the distances between classes (green areas and artificial surfaces), patterns, and processes, using five satellite images representing a time span of 51 years (1963, 1984, 1997, 2005, and 2014). The general landscape structure was examined by looking at the temporal–spatial processes of artificial surface and green areas obtained from these medium-resolution satellite images. The trends in selected landscape-level metrics were specified and discussed through the use of a moving window analysis. We then used Pleiades high-resolution satellite imagery (2015) to analyze the landscape structure in more detail. This high-resolution base image allows us to recognize the possibility of classifying basic cultural landscape classes. The findings regarding the spatial arrangement of each class in the areas allocated to 14 cultural landscape classes were interpreted by associating them with the landscape functions. Finally, particulate matter (PM10) concentration data were collected and evaluated as an ecological indicator, in order to reveal the relationships between landscape structure and landscape function. In short, we first evaluated the whole landscape structure using medium-resolution data, followed by the classification of cultural landscapes using high-resolution satellite imagery, providing a time-effective—and, therefore, essential—auxiliary method for landscape evaluation. This two-stage evaluation method enables inferences to be made that can shed light on the landscape functions in an urban environment based on the landscape structure.

Development of a Cellular Automata-based model approach for sustainable planning of affordable housing projects: an application case study in Algiers

Being considered as a basic necessity and a key element in the development of sustainable communities, housing is a major concern for the Algerian government. Algiers is facing for the last years a challenging housing shortage and as a way to overcome this problem, many housing projects have been launched. However, the pressing need to address this crisis has disregarded what the pattern of landscape will be, how the existing infrastructures will accommodate with such housing projects and how they will impact on human well-being. This research aims to advance the challenges of planning for sustainability by proposing a methodological approach in a context of high lack of data to support decision-makers in the elaboration of affordable housing projects. The main objective is to trade off urban growth with residential satisfaction and the preservation of natural resources. We developed two Cellular Automata (CA) based residential development scenarios to identify suitable locations for future affordable housing projects: Urban densification scenario (UD) and Constrained urban sprawl scenario (CUS). Both scenarios are based on indicators of residential preferences and measures taken in order to counter the negative effects of urbanization. Results reveal the low capacity of Algiers to meet the housing shortage according to the conditions set for each scenario. The scenarios were evaluated by quantifying their spatial patterns using a preselected set of six class-level landscape metrics. Results show a combination of aggregated and dispersed patterns growth for both scenarios meeting trade-offs among the advantages and the challenges of urban densification and urban sprawl. Then, Standard deviation and regression analysis were conducted to assess the accuracy of CA simulation and the evaluation of pattern changes in the simulated scenarios. The resulting values indicate the good performance of CA and confirm its effectiveness in predicting the future locations of housing projects.

A simple method for determination of fine resolution urban form patterns with distinct thermal properties using class-level landscape metrics

ContextRelationships between land surface temperature (LST) and spatial configuration of urban form described by landscape metrics so far have been investigated with coarse resolution LST imagery within artificially superimposed land divisions. Citywide micro-scale observations are needed to better inform urban design and help mitigate urban heat island effects in warming climates.ObjectivesThe primary objective was to sub-divide an existing high-resolution land cover (LC) map into groups of patches with distinct spatial and thermal properties suitable for urban LST studies relevant to micro-scales. The secondary objective was to provide insights into the optimal analytical unit size to calculate class-level landscape metrics strongly correlated with LST at 2 m spatial resolution.MethodsA two-tiered unsupervised k-means clustering analysis was deployed to derive spatially distinct groups of patches of each major LC class followed by further subdivisions into hottest, coldest and intermediary sub-classes, making use of high resolution class-level landscape metrics strongly correlated with LST.ResultsAggregation class-level landscape metrics were consistently correlated with LST for green and grey LC classes and the optimal search window size for their calculations was 100 m for LST at 2 m resolution. ANOVA indicated that all Tier 1 and most of Tier 2 subdivisions were thermally and spatially different.ConclusionsThe two-tiered k-means clustering approach was successful at depicting subdivisions of major LC classes with distinct spatial configuration and thermal properties, especially at a broader Tier 1 level. Further research into spatial configuration of LC patches with similar spatial but different thermal properties is required.

Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?

Abstract Characterizing landscape patterns is an important analytical step towards understanding the effects of physical layouts on ecological and social processes. While a continuous representation of landscape structure has great potential as a realistic alternative to traditional patch-based models, its empirical merits have been limited by the lack of measures for quantifying patterns from such continuous surface. This paper examines the utility of Gray-Level Co-Occurrence Matrix (GLCM) indices as spatial metrics applied to the landscape level for measuring underlying spatial properties. Eight GLCM indices (contrast, dissimilarity, homogeneity, energy, entropy, mean, variance, correlation) are compared to most commonly used 18 landscape metrics (LMs) featuring landscape composition, aggregation, dominance, dispersion, and shape complexity, with an application to urban tree canopy landscape. Two different types of map, sub-pixel tree canopy cover percentage map versus binary tree-pixel map, are used to compute GLCM indices and class-level LMs with a moving window approach across 4556 focal points. The data, extracted from the National Land Cover Database (NLCD) and the National Agricultural Imagery Program (NAIP), characterize the city of Columbus and Franklin County, Ohio. Correlation and regression analyses demonstrate that there is a strong and robust analogy between textural traits implied by GLCM indices and patch-based characteristics measured by LMs. Four LM components generated by principal component analysis contribute differently to individual GLCM indices, enabling more nuanced interpretation of GLCM indices in terms of LMs. The identified meanings consist of a unique mix of patch abundance, aggregation, dispersion, large patch dominance, patch size variability, and landscape homogeneity. The prediction of landscape patterns by GLCM indices increases in accuracy with landscape size, to a scale comparable to census tracts, while staying robust to the variation in GLCM bin width. GLCM indices can serve as reliable indicators of spatial configuration, and therefore provide an effective tool for researchers to better utilize continuous landscape models.

Effects of changing spatial extent on the relationship between urban forest patterns and land surface temperature

Abstract The effects of spatial pattern of urban greenspace on land surface temperature (LST) have been extensively documented. Previous studies have confirmed that the relationship between urban green spatial pattern and LST is sensitive to spatial resolution and remote sensing imagery with higher resolution could be more accurate on quantifying the urban green patterns, but little is known about another scaling issue—spatial extent. This paper examined whether the spatial extent applied to derive landscape metrics affect the relationship between LST and spatial pattern of urban forested areas of highly urbanized Shanghai and the seasonal variations using correlation analyses and regression analyses. Spatial pattern of forested areas was measured with eight class-level landscape metrics over four spatial extents/scales (90 m × 90 m, 180 m × 180 m, 360 m × 360 m and 720 m × 720 m) using moving-window approach based on a land-use and land cover (LULC) map derived from SPOT 6 datasets. Results demonstrated that changing spatial extent had significant impacts on the relationship between spatial pattern of urban forested areas and LST. The responses of correlations between spatial pattern metrics and LST to changing extent fell into three categories: correlation decreases with extent increases, correlation increases with extent increases and unpredictable pattern. In general, the amount of forested cover accounts for greater variability in LST than its spatial arrangement. This study extended our scientific understanding of the effects of spatial pattern of urban forested area on LST. In addition, it can provide insights for urban forest planning and management.

Spatial Analysis of Forest–Tundra Ecotones Reveals the Influence of Topography and Vegetation on Alpine Treeline Patterns in the Subarctic

The response of vegetation to recent climate change is a central theme in contemporary biogeography. A tenet of this research is that forests will advance upslope and northward as climate warms, replacing tundra communities. There has been considerable variation, however, in the pace and extent of recent change, particularly in alpine regions. The objectives of this study were to determine how and why tree spatial patterns vary across different topographic features, and to use spatial patterns to infer the mechanisms governing treeline dynamics in an alpine region of subarctic Canada. We mapped trees across different elevations, slope aspects, and slope angles in two QuickBird satellite images, and in field plots established within the bounds of each image. We then quantified the degree of clustering among trees using Ripley’s K(t) statistic. We also classified each image into vegetation classes and used class-level landscape metrics to quantify the degree of treeline abruptness in different topographic settings. We found that clustering of stems was more common on south than north aspects, likely due to the high occurrence of overwinter damage on the former. Treelines were also more abrupt on south aspects, likely because high tall shrub abundance on these slopes inhibits tree seedling establishment. We conclude that the spatial patterns of subarctic alpine treelines are strongly influenced by both physical and biological factors that vary strongly with slope aspect. The response of treelines to future climate change will likely be highly variable at the landscape scale, despite experiencing similar climatic conditions. Key Words: forest–tundra ecotone, FragStats, landscape metrics, QuickBird, spatial pattern analysis, Yukon.

Assessing the dynamic relationship among land use pattern and land surface temperature: A spatial regression approach

ABSTRACTDrastic changes in land use pattern in recent times has translated into severe alteration of urban surface temperatures, whereby the increase in impervious surfaces results in modification of the thermal structure of the city and consequently leads to the problem of Urban Heat Island. Conversely, creation of green surface within the city reduces the temperature and forms a cooling effect. The present study investigates the impact of the shape, pattern, and configuration of the two dominant land use/land cover classes (built-up and green surface) on the Land Surface Temperature (LST) of Hyderabad city. Different class-level landscape metrics at neighborhood level have been employed to evaluate the spatial variation of LST. Significant Moran’s I cluster value indicates spatial dependencies of landscape metrics and LST. The local Geographical Weightage Regression (GWR) model has also been executed to examine the relative importance of land use pattern on LST. Results reveal that during 2002 and 2015, the increasing fragmentation of built-up and LSI have positively influenced the LST, whereas, urban green surface, the PLAND, and IJI have negatively affected the LST. The estimated LST is found to be highly sensitive to the configuration and changes in land-use pattern. The spatial regression result suggests that with the expansion of built-up in the neighborhood, there is a discernible increase in the LST. Similarly, fragmented green cover reduces the neighboring temperature. The outcome of this study may enable planners to obtain insight into where urban management and green surface planning is necessary for mitigating the surface temperature.

Influence of Landscape Structures on Water Quality at Multiple Temporal and Spatial Scales: A Case Study of Wujiang River Watershed in Guizhou

Water quality is highly influenced by the composition and configuration of landscape structure, and regulated by various spatiotemporal factors. Using the Wujiang river watershed as a case study, this research assesses the influence of landscape metrics—including composition and spatial configuration—on river water quality. An understanding of the relationship between landscape metrics and water quality can be used to improve water contamination predictability and provide restoration and management strategies. For this study, eight water quality variables were collected from 32 sampling sites from 2014 through 2017. Water quality variables included nutrient pollutant indicators ammoniacal nitrogen (NH3-N), nitrogen (NO3−), and total phosphate (TP), as well as oxygen-consuming organic matter indicators COD (chemical oxygen demand), biochemical oxygen demand (BOD5), dissolved oxygen (DO), and potassium permanganate index (CODMn). Partial least squares (PLS) regression was used to quantitatively analyze the influence of landscape metrics on water quality at five buffer zone scales (extending 3, 6, 9, 12, and 15 km from the sample site) in the Wujiang river watershed. Results revealed that water quality is affected by landscape composition, landscape configuration, and precipitation. During the dry season, landscape metrics at both landscape and class levels predicted organic matter at the five buffer zone scales. During the wet season, only class-level landscape metrics predicted water contaminants, including organic matter and nutrients, at the middle three of five buffer scales. We identified the following important indicators of water quality degradation: percent of landscape, edge density, and aggregation index for built-up land; aggregation index for water; CONTAGION; COHESION; and landscape shape index. These results suggest that pollution can be mitigated by reducing natural landscape composition fragmentation, increasing the connectedness of region rivers, and minimizing human disturbance of landscape structures in the watershed area.

Correlates of Habitat Fragmentation and Northern Bobwhite Abundance in the Gulf Prairie Landscape Conservation Cooperative

Abstract The northern bobwhite Colinus virginianus has experienced range-wide declines over the past several decades, primarily due to habitat loss and habitat fragmentation. As northern bobwhite populations continue to decline, there is a need for studies that address the impact of habitat changes on population persistence at multiple spatial scales. Our goal was to assess changes in habitat and land use related to northern bobwhite declines across multiple spatial scales in Texas, Oklahoma, and Louisiana. We determined northern bobwhite trends for 1972–2012 using Breeding Bird Survey data. At the regional scale, we compared northern bobwhite population trends with road density (2000, 2012), human population (1970–2010), and land use (1974–2012). At the county and local scales, we compared class-level landscape metrics between counties with stable and declining northern bobwhite abundances using Student's t-tests. Northern bobwhite populations decreased from 45.95 ± 1.01 birds/route in 1970 to 11.55 ± 0.64 birds/route in 2012. Road density and human population increased by 3,331.32 ± 66.28 m/km2 and 42,873 ± 8,687 people/county, respectively. Percent pasture and rangeland was relatively stable, as was percent woodland. Alternatively, the percentage of other land (houses, roads, wasteland) increased. At the county scale, Texas and Oklahoma counties with declining northern bobwhite populations had higher road densities, larger patches of pasture, smaller patches of woodland, and larger patches of cropland compared with stable populations. At the local scale, Texas and Oklahoma counties with declining northern bobwhite populations had less woody cover in smaller patches, and fewer but larger patches of herbaceous and bare ground, compared with populations with stable abundance. Therefore, managers can provide woody cover and reduce cropland effects at the local scale to support stable quail populations; however, the large-scale drivers of northern bobwhite decline, which are human population growth and resulting habitat loss, will be an important aspect of northern bobwhite conservation and management in the future.

Degradation monitoring in Silvo-pastoral systems: A case study of the Mediterranean region of Turkey

To identify policies that will promote positive effects and mitigate negative ones of grazing is a major challenge in the Silvo-pastoral system. This paper presents the role of examining land-cover change trajectories by remote sensing imagery in grazing policy monitoring. The study was conducted for Duzlercami forest ecosystem located in the Mediterranean geographical region of Turkey and administrated by the General Directorate of Forestry (GDF) of the Ministry of Forestry and Water Affairs. Time series land-cover datasets from Landsat images between 1988 and 2016 were collected and classified. To link the conversions among trajectories and grazing policy, class level landscape metrics derived from the classified images were used. To validate the approach, yearly grazing-plans managed by GDF and populations of livestock were used. Results of this research have indicated that even though there is a yearly grazing plan, overgrazing can happen on the pilot site, and it can be easily identified by the destruction of woody vegetation. The notable correlation (r2 = 0.89) between degraded woody vegetation and cattle population has occurred in the last 30 years in the landscape, and Landsat imagery can effectively support the grazing policy mapping and monitoring.

Procedural Generation of Large-Scale Forests Using a Graph-Based Neutral Landscape Model

Specifying the positions and attributes of plants (e.g., species, size, and height) during the procedural generation of large-scale forests in virtual geographic environments is challenging, especially when reflecting the characteristics of vegetation distributions. To address this issue, a novel graph-based neutral landscape model (NLM) is proposed to generate forest landscapes with varying compositions and configurations. Our model integrates a set of class-level landscape metrics and generates more realistic and variable landscapes compared with existing NLMs controlled by limited global-level landscape metrics. To produce patches with particular sizes and shapes, a region adjacency graph is transformed from a cluster map that is generated based upon percolation theory; subsequently, optimal neighboring nodes in the graph are merged under restricted growth conditions from a source node. The locations of seeds are randomly placed and their species are classified according to the generated forest landscapes to obtain the final tree distributions. The results demonstrate that our method can generate realistic vegetation distributions representing different spatial patterns of species with a time efficiency that satisfies the requirements for constructing large-scale virtual forests.

Analyzing Spatiotemporal Patterns of Urbanization in Treasure Valley, Idaho, USA

Knowledge of spatiotemporal patterns of urbanization in terms of overall urban growth, growth forms (or types) and land use types contributes to an understanding of the consequences of urban growth on human health, biodiversity and natural resources, thereby providing guidance for formulating effective land use policies. In this study, we delineated spatial extent of urban area in Treasure Valley of Idaho for five different years with a decadal interval beginning in 1974 based on remote sensing data. The newly urbanized area during each change period was categorized into three growth forms: edge-expansion, infill and outlying development, and into two built-intensity classes: high and low intensity development. We applied ten class-level landscape metrics to the urban class as a whole. Results show that edge-expansion dominated the growth with more than 60% of new development for each of the four study periods. Infill and outlying growth witnessed a temporal oscillation with an alternating dominance. Despite some regularities observed in spatiotemporal patterns of urban class and urban growth forms, we concluded that diffusion and coalescence are two concurrently occurring processes of urban growth rather than two dichotomous temporal phases. Our study documented that a combined study involving patch dynamics, gradient analysis, and time-series analysis of new development, growth forms and built intensity was effective in identifying different spatiotemporal patterns and associate them to underlying processes.

Analyzing Three-Decadal Patterns of Land Use/Land Cover Change and Regional Ecosystem Services at the Landscape Level: Case Study of Two Coastal Metropolitan Regions, Eastern China

Rapid urbanization, land scarcity, and accompanying ecological deterioration in China have received growing attention. In this paper, two fast-growing metropolitan regions, Greater Shanghai and Greater Hangzhou, were selected as case studies to quantify the impact of land use/land cover (LULC) change on regional ecosystem services value (ESV) at the landscape scale since the late 1970s. The results show that in both regions, dramatic LULC change, especially recent land development at the urban fringes, led to a steady decline in the available area of productive agricultural land, natural land and semi-natural land. This consequently caused remarkable landscape fragmentation along the urban-rural gradient as measured by five class-level landscape metrics. It was estimated that in Greater Shanghai, regulating, supporting, provisioning, and cultural ESVs decreased by 32.05%, 17.89%, 53.72%, and 17.06%, respectively. In Greater Hangzhou, these values decreased by 27.82%, 23.86%, 28.62%, and 22.85%, respectively. In addition, the relationship is quantified between zonal buffer-based ESV and class-level landscape metrics. Further analysis shows that spatiotemporal patterns of zonal ESVs along the urban-rural gradient in these two regions exhibited unbalanced patterns of ecological services delivery.

Scaling functions evaluation for estimation of landscape metrics at higher resolutions

Understanding the relationship between landscape pattern and environmental processes requires quantification of landscape pattern at multiple scales. This will make it possible to relate broad-scale patterns to fine-scale processes and vice versa. In this study, we used class level landscape metrics calculated at multiple scales to fit scaling functions that were used to downscale metrics at higher resolutions. The main objectives were to assess the performance of different type of functions (i.e. power, logarithmic, etc.) to downscale metrics at the subpixel level and to analyze the variability of the accuracy of subpixel estimates among patch classes for each landscape metric. We used thirteen frequently used landscape metrics, computed on a land use/land cover map derived from Landsat imagery through visual interpretation and supervised classification using Support Vector Machines. The performance of scaling functions was assessed with the Accuracy Improvement percentage (AI). In general, the power function fitted better for most landscape metrics and classes; however, in several cases, more than one type of function showed similar R2 values. Accuracy of subpixel estimates was very variable among landscape metrics and also among patch classes within a metric. The amount of variation was such that no generalization about the predictability of a landscape metric calculated at the class level was possible. Indeed, predictability seemed to be more of a characteristic of the class than a characteristic of the landscape metric. Additionally, the goodness of fit of the scaling functions was not a good indicator of the functions' ability to downscale landscape metrics accurately, indicating that different scaling functions should be analyzed when downscaling landscape metrics at higher resolutions is required.

Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona

The structure of urban environments is known to alter local climate, in part due to changes in land cover. A growing subset of research focuses specifically on the UHI in terms of land surface temperature by using data from remote sensing platforms. Past research has established a clear relationship between land surface temperature and the proportional area of land covers, but less research has specifically examined the effects of the spatial patterns of these covers. This research considers the rapidly growing City of Phoenix, Arizona in the United States. To better understand how landscape structure affects local climate, we explored the relationship between land surface temperature and spatial pattern for three different land uses: mesic residential, xeric residential, and industrial/commercial. We used high-resolution (2.4 m) land cover data and an ASTER temperature product to examine 90 randomly selected sample sites of 240 square-meters. We (1) quantify several landscape-level and class-level landscape metrics for the sample sites, (2) measure the Pearson correlation coefficients between land surface temperature and each landscape metric, (3) conduct an analysis of variance among the three land uses, and (4) model the determinants of land surface temperature using ordinary least squares linear regression. The Pearson’s correlation coefficients reveal significant relationships between several measures of spatial configuration and LST, but these relationships differ among the land uses. The ANOVA confirmed that mean land surface temperature and spatial patterns differed among the three land uses. Although a relationship was apparent between surface temperatures and spatial pattern, the results of the linear regression indicate that proportional land cover of grass and impervious surfaces alone best explains temperature in mesic residential areas. In contrast, temperatures in industrial/commercial areas are explained by changes in the configuration of grass and impervious surfaces.

A Hierarchical Approach to Forest Landscape Pattern Characterization

Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

Land use/cover change and landscape fragmentation analysis in the Bindura District, Zimbabwe

AbstractRapid land use/cover change and landscape fragmentation is occurring in many countries in central and southern Africa, as a result of colonial imbalances in land distribution, demographic pressure, agricultural expansion, government policies and environmental factors such as drought. This study analysed the dynamics of land use/cover and land degradation as revealed in landscape fragmentation in the Bindura District of Zimbabwe based on Landsat data for 1973 and 2000. A hybrid supervised/unsupervised classification approach coupled with GIS analyses was employed to generate land use/cover maps from which various class level landscape metrics were calculated using FRAGSTATS®, in order to analyse landscape fragmentation. The results show that agriculture, mixed rangeland, settlements, bareland and water increased, while woodland areas decreased. Consequently, the landscape became more highly fragmented as indicated by an increase in the patch number and a decrease in the mean patch size (MPS) of the woodland and mixed rangeland classes. This suggests that anthropogenic activities driven by agricultural expansion were the main causes of landscape fragmentation, leading to landscape degradation in the study area. Copyright © 2006 John Wiley & Sons, Ltd.