Assessment of the internal consistency of physico-geographical units using landscape metrics and statistical methods: case study of the Khmelnytskyi Oblast, Ukraine

This paper documents the analyses that were conducted with regards to investigating an appropriate Minimum Mapping Unit (MMU) to be used to capture the potential changes in vegetation patterns for a 10,924 square km restoration project being conducted in south Florida, USA. Spatial landscape and class metrics that were shown to change predictably with increasing grain size were adopted from previous studies and applied to a multi-scale analysis. Specifically, this study examines the effects of changing grain size on landscape metrics, utilizing empirical data from a real landscape encompassing 234,913 ha of south Florida’s Everglades. The objective was to identify critical thresholds within landscape metrics, which can be used to provide insight in determining an appropriate MMU for vegetation mapping. Results from this study demonstrate that vegetation heterogeneity will exhibit dissimilar patterns when investigating the loss of information within landscape and class metrics, as grain size is increased. These results also support previous findings that suggest that landscape metric “scalograms” (the response curves of landscape metrics to changing grain size), are more likely to be successful for linking landscape pattern to ecological processes as both pattern and process in ecological systems often operate on multiple scales. This study also incorporates an economic cost for various grain dependant vegetation mapping scales. A final selection of the 50 × 50 m grain size for mapping vegetation was based on this study’s investigation of the “scalograms”, the costs, and a composite best professional judgment of seasoned scientists having extensive experience within these ecosystems.

Using remotely sensed data, landscape pattern analysis based on landscape metrics has been one of the major topics of landscape ecology, and more attention has been focused on the effects of spatial scale and the accuracy of remotely sensed data on landscape metrics. However, few studies have been conducted to assess the change of landscape metrics under the influence of land‐use categorization. In this paper, we took the Bao'an district of Shenzhen city as the study area, to analyse how land‐use categorization would influence changes in 24 landscape metrics. The results showed a significant influence, and based on the characteristics of the response curves of landscape metrics associated with the change in land‐use categorization in regression analysis, and the predictability of these relations, the 24 landscape metrics fell into three groups. (1) Type I included 12 landscape metrics, and showed a strong predictability with changing of land‐use categorization with simple function relations in regression analysis. (2) Type II included seven indices, and exhibited complicated behaviours against changing of land‐use categorization. The response curves of these metrics, which were not easy to predict, consisted of two subsections and could not be described by a single function. (3) Type III included five indices, and showed unpredictable behaviours against the change of the land‐use categorization. Their response curves could not be described by a certain function. This study highlights the need for the analysis of effects of land‐use categorization on landscape metrics so as to clearly quantify landscape patterns, and provides insights into the selection of landscape metrics for comparative research on a given area under different land‐use categorizations.

Seasonal variations for combined effects of landscape metrics on land surface temperature (LST) and aerosol optical depth (AOD)

During the recent years, landform detection and mapping has been one of the most active fields of geomorphometry. However, there is still a need for quantitative work addressing the issue of classifying repeating landform types (MacMillan et al., 2004). The main issue in developing an accurate automatic classification algorithm of repetitive landform types is given by the difficulty to integrate contextual information within the analysis (Evans, 2012). Therefore, the motivation of the current approach is strongly related to the importance of context analysis in the field of specific geomorphometry. Introduced in landscape ecology to evaluate the spatial structure of a landscape, the concept of landscape metrics embraces a series of specific indicators for quantifying topological and contextual information. Thus, considering the fact that the assessment of topological and contextual attributes is not possible based on local, statistical and regional land-surface variables, the main objective of this study is to assess the applicability of landscape metrics for the delineation of landform patterns. The quantification of landscape metrics involved the segmentation and classification of the following morphometric variables: elevation, profile curvature and local relief. Using an unsupervised method, the Iso Cluster Unsupervised Classification tool from ArcGIS10® software, a total of 24 classes have been used in order to fulfill the minimum requirement imposed by the concept of landscape metrics and further statistical analysis. In order to test the transferability degree of landscape metrics among different dune fields, a set of statistical analyses was carried out. The proposed methodology has been applied on freely available ASTER GDEM’s. This paper provides new prospects regarding the applicability of landscape metrics for the delineation of landform patterns.

Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics

The use of landscape metrics to characterize the morphological behavior of a landscape has been extensive in the last few years. It is recognized that a single metric is insufficient to characterize a landscape. Such metrics are used individually to derive the morphological aspect of a landscape. No joint use of various metrics has been reported. Therefore, we considered the joint use of landscape metrics in a multivariate classification. We derived the value of a number of landscape metrics of patches from several case studies. A multivariate classification was applied using a hierarchical clustering algorithm. The multivariate classification was carried out using the least correlated landscape metrics. To consider the multivariate classification, a normalization of metrics range was used. The results provided the morphological structure of patches grouped into four or five classes. The classes depicted a morphological structure of patches that ranged from simple to very complex. An index was proposed to quantify the morphological structure of a class-patch. Such an index was defined as the average of the landscape metrics for a class-patch. The distance among the class-patch was given by means of the Jeffries–Matusita distance.

A multi-level approach to wetland assessment and monitoring has been developed to incorporate information from multiple spatial scales and varying levels of effort. In this approach, wetland condition is evaluated in an intensive assessment through detailed, on-site measurement of physical and biological condition, and is inferred in a landscape assessment from a wetland’s landscape setting characterized with available spatial data. This study assessed a comprehensive set of landscape metrics to improve an existing landscape assessment using wetland condition measures from the Upper Juniata intensive assessment data. On-site measures of wetland state (n = 10) were compared with landscape metrics (n = 47) measured at multiple spatial scales using Pearson’s correlation coefficients. Landscape metrics enhanced the existing landscape assessment if they were correlated with condition metrics not correlated with the existing landscape assessment. Finally, landscape metrics identified through the correlation analysis were used to place sites in categories of condition based on the Floristic Quality Assessment Index (FQAI) using classification and regression tree analysis (CART). Results showed the existing landscape assessment metric is correlated with multiple measures of wetland state. The study identified landscape metrics that could enhance the existing landscape assessment, including measures of near-stream land use measured at an upstream scale, the percent of agriculture on steep slopes in a 250-m-radius circle or upstream area, and a measure of interior forest measured at a 250-m landscape circle or an upstream scale. Finally, the CART analysis showed the prediction of the FQAI was significantly (p < 0.001) improved by the addition of the landscape metrics identified in this study.

Quantifying the response of landscape metrics to an altering observation scale is crucial to understanding environmental changes and managing ecosystem services. Whereas the scaling behaviors of landscape metrics in spatial heterogeneity analysis have been well identified by previous research, there remains a need to examine these effects in areas undergoing rapid change. Here, we aim to reveal the landscape scale effect in the Three Gorges Reservoir (TGR) area, China, using a case study on Zigui County. We applied a suite of common landscape metrics (12 indices at the class level and 17 indices at the landscape level) to characterize the landscape pattern and examine the response of the metrics to altering grain size using a series of land-use/land-cover data with gradient resolutions. The results reveal that significant scale effects exist in most pattern metrics in the TGR landscape. In addition, the different responses to the altering grain size occurred with different landscape metrics and various land-use/land-cover types. With respect to changing grain size, all of the selected pattern metrics at the landscape level displayed high or medium sensitivity in response to changing grain size except the Fractal Dimension Index and the landscape-diversity indices. The behavior of the metrics in response to altering grain size can be grouped into four types (Type 1, Type 2, Type 3, and Type 4). The class-level metrics with high sensitivity were Mean Patch Size, the Contiguity Index, the Euclidean Nearest-Neighbor Distance, the Perimeter-Area Ratio, and Patch Density for all land-use/land-cover types, whereas low sensitivities were detected in the response of the Fractal Dimension Index and the Largest Patch Index. Based on the response to the altering resolution of input data, the class-level metrics could be grouped into three types (Type a, Type b, and Type c). Considering the scaling behavior of landscape metrics, we suggest using a set of suitable remote-sensing images to quantify the landscape pattern in the TGR landscape and similar areas.

Influences of landscape pattern on water quality at multiple scales in an agricultural basin of western China

BackgroundUrban forests help in mitigating carbon emissions; however, their associations with landscape patterns are unclear. Understanding the associations would help us to evaluate urban forest ecological services and favor urban forest management via landscape regulations. We used Harbin, capital city of the northernmost province in China, as an example and hypothesized that the urban forests had different landscape metrics among different forest types, administrative districts, and urban–rural gradients, and these differences were closely associated with forest carbon sequestration in the biomass and soils.MethodsWe extracted the urban forest tree coverage area on the basis of 2 GF-1 remote sensing images and object-oriented based classification method. The analysis of forest landscape patterns and estimation of carbon storage were based on tree coverage data and 199 plots. We also examined the relationships between forest landscape metrics and carbon storage on the basis of forest types, administrative districts, ring roads, and history of urban settlements by using statistical methods.ResultsThe small patches covering an area of less than 0.5 ha accounted for 72.6% of all patches (average patch size, 0.31 ha). The mean patch size (AREA_MN) and largest patch index (LPI) were the highest in the landscape and relaxation forest and Songbei District. The landscape shape index (LSI) and number of patches linearly decreased along rural-urban gradients (p < 0.05). The tree biomass carbon storage varied from less than 10 thousand tons in the urban center (first ring road region and 100-year regions) to more than 100 thousand tons in the rural regions (fourth ring road and newly urbanized regions). In the same urban–rural gradients, soil carbon storage varied from less than five thousand tons in the urban centers to 73–103 thousand tons in the rural regions. The association analysis indicated that the total forest area was the key factor that regulates total carbon storage in trees and soils. However, in the case of carbon density (ton ha−1), AREA_MN was strongly associated with tree biomass carbon, and soil carbon density was negatively related to LSI (p < 0.01) and AREA_MN (p < 0.05), but positively related to LPI (p < 0.05).DiscussionThe urban forests were more fragmented in Harbin than in other provincial cities in Northeastern China, as shown by the smaller patch size, more complex patch shape, and larger patch density. The decrease in LSI along the rural-urban gradients may contribute to the forest carbon sequestrations in downtown regions, particularly underground soil carbon accumulation, and the increasing patch size may benefit tree carbon sequestration. Our findings help us to understand how forest landscape metrics are associated with carbon storage function. These findings related to urban forest design may maximize forest carbon sequestration services and facilitate in precisely estimating the forest carbon sink.

Rapid urban growth and subsequent dramatic changes in landscape diversity have been witnessed in some developing countries as a result of rapid economic development. Since the 1960s, the Seoul Metropolitan Region (SMR) increasingly reflects a synthesis of urban and suburban building styles and land use patterns. This paper examines the measurement and monitoring of urban growth and change by the integration of remote sensing and urban geography. The study investigates the value of the combination in conjunction with landscape metrics as applied to the Seoul Metropolitan region between 1970 and 2000. The study introduces a methodology using landscape metrics to enhance understanding urban growth land use patterns with multivariate statistical methods. Using selected landscape metrics and statistical methods, the author describe patterns of landscape change in Seoul Metropolitan Region between 1950 and 2000. This research shows the significance of urban remote sensing in urban.

New nighttime light landscape metrics for analyzing urban-rural differentiation in economic development at township: A case study of Fujian province, China

Assessing modelled outdoor traffic-induced noise and air pollution around urban structures using the concept of landscape metrics

The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite landscape metrics rarely consider the effects of spatial correlation. A more objective approach to formulating composite landscape metrics involves proper weighting that incorporates spatial structure information into integrating individual conventional metrics selected for building a composite metric. This paper proposes an integrated spatial landscape index (ISLI) based on variogram modeling and entropy weighting. It was tested through a case study, which sought to analyze spatiotemporal changes in the landscape pattern in the Changbai Mountains over 30 years based on six global land-cover products with a fine classification system at 30 m resolution (GLC_FCS30). The test results confirm: (1) spatial structure information is useful for weighting conventional landscape pattern metrics when constructing ISLI as validated by correlation analysis between the incorporated conventional metrics and their variogram ranges. In terms of the range parameters of different land cover types, broadleaf forest and needleleaf forest have much larger range values than those of other land cover types; (2) DIVISION and PLAND, two of the conventional landscape metrics considered for constructing ISLI, were assigned the greatest weights in computing ISLI for this study; and (3) ISLI values can be used to determine the dominant landscape types. For the study area, ISLI values of broadleaf forests remained the largest until 2020, indicating that forest landscape characteristics were the most prominent during that period. After 2020, the dominance of needleleaf forest gradually increased, with its ISLI value reaching a maximum of 0.91 in 2025. Therefore, the proposed ISLI not only functions as an extension and complement to conventional landscape metrics but also provides more comprehensive information concerning landscape pattern dynamics.

Associations between forest characteristics and socio-economic development: A case study from Portugal