Relating landscape ecological metrics with public survey data on perceived landscape quality and place attachment

Atlantic forest fragmentation is considered a serious threat to biodiversity since this biome is considered the hottest hotspot. Due to this reason, many environmental strategies are being developed in order to support its, one of them being the prioritization of forest remnants using landscape ecology metrics. Thus, the main objective of this study is the development of a patches prioritization index (PPI) in order to support conservation actions and research. Firstly, a diagnosis of forest remnants in the study area was performed using landscape ecology metrics. Secondly, by literature review and expert consulting, were selected the adequate landscape ecology metrics, next, their importance was determined for PPI composition. Selected landscape metrics (AREA, SHAPE, and NEARD) composed the PPI. Finally, using a rapid ecological assessment (BII) the PPI was validated in the field. The results showed that the study area has patches able to aid biodiversity maintenance in the landscape. Further, the selection and importance attributed to landscape ecology metrics were demonstrated to be adequate. Also, the index is accurate enough to identify priority patches, classes, and regions for biodiversity conservation. Finally, the validation of PPI in the field showed that PPI is effective to estimate patches integrity in the field. In conclusion, our results suggest that PPI could be used for the prioritization of Atlantic forest remnants in a landscape covered mainly by Atlantic forest remnants and agriculture.

Forest structure optimization using evolutionary programming and landscape ecology metrics

Forest fragmentation may negatively impact fauna and flora. An important tool for the development and implementation of research on these effects is the use of a geographic information system (GIS). This paper aims to perform an integrated analysis of the landscape fragments that compose the Alonzo River watershed, Parana State, by using remote sensing tools and landscape ecology metrics. The analyzed landscape metrics were Patton’s index, total area of the patches, edge length, edge density, forest fragment density, and the core area of the patches. The results showed 888 forest patches with area values ranging from 0.15 ha to 2509.82 ha, and it represents 12.5% of the total forest land cover of the studied basin; this means that 85.3% of the forest patches are less than 50 ha and that 75% of those fragments have a Patton’s index value of less than 3.9. The fragments that compose the studied area may be subject to edge effects and biodiversity loss as long as they present reduced areas and small core areas. Thus, the use of GIS and landscape ecology metrics is a quick and efficient way to evaluate the effect of fragmentation over large areas.

Leporids play a dynamic role in the ecosystem and assessments must be undertaken in order to improve research efforts and methods. Landscape ecology metrics are used to quantify components of leporid habitat such as vegetation structure, vegetation cover, habitat type, and fragmentation; however, the degree to which the metrics are utilized in leporid research is relatively unknown. This paper assessed fifty-three published, peer reviewed papers on leporids from various European countries on where the study was done, the species of leporid that was studied, the content of the study (i.e. what the paper focused on), the length of the study, the size of the study area, and the method of study. The quantified landscape metrics within these papers were assessed. This study found that most of the studies occurred in Spain, the European rabbit and European hare were the most studied leporids, many papers were concerned with habitat relationships, many of the studies were conducted in a year or less, many papers utilized pellet surveys and trapping, and the most common landscape metric utilized was habitat type. This survey of research on leporids highlights that there is a lack of utilizing landscape structure and function metrics such as slope, fragmentation, and edge effect. These are important variables to help connect structure and function of ecological processes in the context of leporid habitat and landscapes. It is recommended that leporid researchers and landscape planners exchange research findings so that the best planning practices can occur on the ground for the leporids

Since the 1970s, land settlement programs and the construction of highways have accelerated the occupation of Rondônia, intensifying deforestation. This study analyzes land use and forest fragmentation in the Jaru River watershed for the years 1985, 1995, 2005, 2015, and 2019, and models a future scenario for 2050 using Landscape Ecology metrics. Land use data were sourced from the MapBiomas project. Modeling was performed in IDRISI using 2005 (T1) and 2019 (T2) as reference years to simulate 2050 (T3), incorporating deforestation drivers such as roads and urban centers. A second 2050 scenario excluded areas of Legal Reserves (RL) and Permanent Preservation Areas (APP). Landscape metrics were calculated using FRAGSTATS 10.5, including AREA_MN, SHAPE_MN, ENN_MN, CAI_MN, CORA_MN, and LPI. GIS tools supported raster processing and data visualization. Forest cover declined from 67.17% in 1985 to 22.39% in 2019, with a projected 22% in 2050. Fragmentation metrics revealed a consistent loss in habitat quality, with decreasing core areas, average patch size, and increased isolation. The findings highlight the impact of anthropic pressures and emphasize the importance of conservation policies to mitigate biodiversity loss and maintain ecological functions in fragmented landscapes.

The goal of this study was to determine the value of including landscape ecology patterns and structure metrics extracted from high-resolution, remotely-sensed imagery in the development of a Shoreline Environmental Impact Index (SEII). Methods of combining landscape ecology metrics to create a meaningful Shoreline Environmental Impact Index included multiple linear regression, multiple discriminant analysis, genetic neural networks, and feed-forward, backpropagation neural networks. The landscape ratings produced by the SEII’s generated using these methods were then compared to landscape ratings by experts. There was very little difference in the performance of several SEII’s generated despite differences in metrics and their weighting chosen by the different methods. The ratings from all methods showed their ability to reflect the expert ratings with moderate accuracy: � 84 percent agreement. Conclusions indicate that the contributions of landscape metrics to the ability of an SEII to discriminate between levels of shoreline degradation are variable, dependent upon the method of combination. Any of the current forms of the SEII is suitable for generating general indication of shoreline health.

Designing an optimized landscape restoration with spatially interdependent non-linear models

RESUMO As métricas de ecologia da paisagem associadas à mineração de dados podem ser utilizadas para aumentar o potencial de análise e aplicações de dados de sensoriamento remoto, tornando-se uma importante ferramenta para a tomada de decisão. Dessa forma, objetivou-se classificar e quantificar diferentes tipos de vegetação por meio de técnicas de mineração de dados e métricas de ecologia da paisagem em uma análise multitemporal (2001 e 2011), em São Luís do Paraitinga, São Paulo, Brasil. A análise de imagens orientada a objetos e o algoritmo de mineração de dados C4.5 foram utilizados para realizar a classificação automática, cuja precisão foi avaliada com o índice kappa e com as medidas de discordância de alocação e de quantidade, recentemente propostas na literatura. Foram classificadas quatro classes de uso e cobertura da terra, entre elas o Eucalipto cuja área aumentou de 4,4% para 8,6%. A classificação automática apresentou kappa de 0,79 e 0,8, erros de quantidade de 2% e 3,5% e alocação de 5,5% e 5% para 2001 e 2011, respectivamente. Conclui-se que o método de mineração de dados e as métricas de ecologia da paisagem foram eficientes na separação de classes de vegetação.

<p>Cities are the keystone landscape features for achieving sustainability locally, regionally, and globally. With the increasing impacts of urban expansion eminent, policymakers have encouraged researchers to advance or invent methods for managing coupled human–environmental systems associated with local and regional sustainable development planning. Although progress has been made, there remains no universal instrument for attaining sustainability on neither regional nor local planning scales. Previous sustainable urbanization studies have revealed that landscape configuration metrics can supplement other measures of urban well-being, yet few have been included in public data dashboards or contrasted against local well-being indicators. To advance this sector of sustainable development planning, this study had three main intentions: (1) to produce a foundational suite of landscape ecology metrics from the 2007 land cover dataset for the City of Toronto; (2) to visualize and interpret spatial patterns of neighborhood <em>streetscape</em> patch cohesion index (COHESION), Shannon’s diversity index (SHDI), and four Wellbeing Toronto indicators across the 140 Toronto neighborhoods; (3) to quantitatively assess the global collinearity and local explanatory power of the well-being and landscape measures showcased in this study. One-hundred-and-thirty landscape ecology metrics were computed: 18 class configuration metrics across seven land cover categories and four landscape diversity metrics. Anselin Moran’s <em>I</em>-test was used to illustrate significant spatial patterns of well-being and landscape indicators; Pearson’s correlation and conditional autoregressive (CAR) statistics were used to evaluate relationships between them. Spatial “hot-spots” and/or “cold-spots” were found in all <em>streetscape</em> variables. Among other interesting results, Walk Score® was negatively related to both tree canopy and grass/shrub connectedness, signifying its lack of consideration for the quality of ecosystem services and environmental public health—and subsequently happiness—during its proximity assessment of socioeconomic amenities. In sum, landscape ecology metrics can provide cost-effective ecological integrity addendum to existing and future urban resilience, sustainable development, and well-being monitoring programs.</p>

<p>Cities are the keystone landscape features for achieving sustainability locally, regionally, and globally. With the increasing impacts of urban expansion eminent, policymakers have encouraged researchers to advance or invent methods for managing coupled human–environmental systems associated with local and regional sustainable development planning. Although progress has been made, there remains no universal instrument for attaining sustainability on neither regional nor local planning scales. Previous sustainable urbanization studies have revealed that landscape configuration metrics can supplement other measures of urban well-being, yet few have been included in public data dashboards or contrasted against local well-being indicators. To advance this sector of sustainable development planning, this study had three main intentions: (1) to produce a foundational suite of landscape ecology metrics from the 2007 land cover dataset for the City of Toronto; (2) to visualize and interpret spatial patterns of neighborhood <em>streetscape</em> patch cohesion index (COHESION), Shannon’s diversity index (SHDI), and four Wellbeing Toronto indicators across the 140 Toronto neighborhoods; (3) to quantitatively assess the global collinearity and local explanatory power of the well-being and landscape measures showcased in this study. One-hundred-and-thirty landscape ecology metrics were computed: 18 class configuration metrics across seven land cover categories and four landscape diversity metrics. Anselin Moran’s <em>I</em>-test was used to illustrate significant spatial patterns of well-being and landscape indicators; Pearson’s correlation and conditional autoregressive (CAR) statistics were used to evaluate relationships between them. Spatial “hot-spots” and/or “cold-spots” were found in all <em>streetscape</em> variables. Among other interesting results, Walk Score® was negatively related to both tree canopy and grass/shrub connectedness, signifying its lack of consideration for the quality of ecosystem services and environmental public health—and subsequently happiness—during its proximity assessment of socioeconomic amenities. In sum, landscape ecology metrics can provide cost-effective ecological integrity addendum to existing and future urban resilience, sustainable development, and well-being monitoring programs.</p>

Cities are the keystone landscape features for achieving sustainability locally, regionally, and globally. With the increasing impacts of urban expansion eminent, policymakers have encouraged researchers to advance or invent methods for managing coupled human–environmental systems associated with local and regional sustainable development planning. Although progress has been made, there remains no universal instrument for attaining sustainability on neither regional nor local planning scales. Previous sustainable urbanization studies have revealed that landscape configuration metrics can supplement other measures of urban well-being, yet few have been included in public data dashboards or contrasted against local well-being indicators. To advance this sector of sustainable development planning, this study had three main intentions: (1) to produce a foundational suite of landscape ecology metrics from the 2007 land cover dataset for the City of Toronto; (2) to visualize and interpret spatial patterns of neighborhood streetscape patch cohesion index (COHESION), Shannon’s diversity index (SHDI), and four Wellbeing Toronto indicators across the 140 Toronto neighborhoods; (3) to quantitatively assess the global collinearity and local explanatory power of the well-being and landscape measures showcased in this study. One-hundred-and-thirty landscape ecology metrics were computed: 18 class configuration metrics across seven land cover categories and four landscape diversity metrics. Anselin Moran’s I-test was used to illustrate significant spatial patterns of well-being and landscape indicators; Pearson’s correlation and conditional autoregressive (CAR) statistics were used to evaluate relationships between them. Spatial “hot-spots” and/or “cold-spots” were found in all streetscape variables. Among other interesting results, Walk Score® was negatively related to both tree canopy and grass/shrub connectedness, signifying its lack of consideration for the quality of ecosystem services and environmental public health—and subsequently happiness—during its proximity assessment of socioeconomic amenities. In sum, landscape ecology metrics can provide cost-effective ecological integrity addendum to existing and future urban resilience, sustainable development, and well-being monitoring programs.

Distributional shifts in species ranges provide critical evidence of ecological responses to climate change. Assessments of climate-driven changes typically focus on broad-scale range shifts (e.g. poleward or upward), with ecological consequences at regional and local scales commonly overlooked. While these changes are informative for species presenting continuous geographic ranges, many species have discontinuous distributions-both natural (e.g. mountain or coastal species) or human-induced (e.g. species inhabiting fragmented landscapes)-where within-range changes can be significant. Here, we use an ecosystem engineer species (Sabellaria alveolata) with a naturally fragmented distribution as a case study to assess climate-driven changes in within-range occupancy across its entire global distribution. To this end, we applied landscape ecology metrics to outputs from species distribution modelling (SDM) in a novel unified framework. SDM predicted a 27.5% overall increase in the area of potentially suitable habitat under RCP 4.5 by 2050, which taken in isolation would have led to the classification of the species as a climate change winner. SDM further revealed that the latitudinal range is predicted to shrink because of decreased habitat suitability in the equatorward part of the range, not compensated by a poleward expansion. The use of landscape ecology metrics provided additional insights by identifying regions that are predicted to become increasingly fragmented in the future, potentially increasing extirpation risk by jeopardising metapopulation dynamics. This increased range fragmentation could have dramatic consequences for ecosystem structure and functioning. Importantly, the proposed framework-which brings together SDM and landscape metrics-can be widely used to study currently overlooked climate-driven changes in species internal range structure, without requiring detailed empirical knowledge of the modelled species. This approach represents an important advancement beyond predictive envelope approaches and could reveal itself as paramount for managers whose spatial scale of action usually ranges from local to regional.

Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture

Applying landscape ecological concepts and metrics in sustainable landscape planning

Socioeconomic forces are not only among the main drivers of landscape dynamics; they are also influenced by landscape patterns. Landscape structure and functions are closely related to natural and social factors. The objective of this study was to investigate the relationships among some human-related factors and landscape ecological metrics as landscape pattern indicators and to identify suitable metrics for modeling these relationships. To this goal, landscape ecological metrics were calculated for each of the 32 counties of Mazandaran and Guilan provinces located in the southern basin of the Caspian Sea using land use/cover maps in class level. Stream network metrics were calculated using a digital elevation model, road density metrics were calculated using map of main roads separately, and significant metrics were selected according to results of correlation tests and factor analysis. The correlations between these metrics and socioeconomic factors were tested, and their relationships were modeled with multiple linear regressions. Significant relationships were found among socioeconomic factors and landscape ecological metrics, and land use/cover data are applicable for modeling socioeconomic factors, especially demographic and employment structure factors. Among the landscape metrics applied in this study, road density, mean patch size, mean nearest neighbor distance, and percentage of a land use/cover class in landscape were important metrics for predicting socioeconomic factors. Our findings indicated that road density metric and percentages of urban class are useful for predicting urban socioeconomic factors and percentage of agriculture and forest classes in the landscape are suitable metrics for predicting rural socioeconomic factors.