Land Cover Patches Research Articles

Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning

Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R2: 0.90, RMSE: 10.1 %), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R2: 0.67, RMSE: 2.73 %), and in residential areas, capturing the totality of canopy cover variation (R2: 0.85, RMSE: 5.66 %). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R2: 0.86, RMSE: 11.4 %), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34 % canopy cover). The two cities (Brampton and Mississauga) have 15.9 % and 17.5 % canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes. Data availabilityThe data used for this paper are available at: https://zenodo.org/records/12549244. The code created for this paper are available at: https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020.

Longitudinal multidiversity pattern and the environmental drivers of riparian bird communities along submontane rivers of Changbai Mountains, China.

Riparian zones are biodiversity hotspots in montane ecosystems and are of critical conservation concern. However, studies on longitudinal diversity patterns and environmental drivers have been restricted to aquatic fauna, while the animals that rely on both river and riparian resources have been of much less concern. Here, we examined the multifaceted diversity distribution of riparian birds along longitudinal gradients and analyzed the importance of environmental factors in shaping these patterns in the Changbai Mountains. Hump-shaped relationships between elevation and taxonomic, phylogenetic, and functional diversity, as well as with the conservation value index, were evident along each of the classic submontane rivers. Forest cover, vegetation height variation, and land cover patches positively affected the taxonomic diversity indices. In addition to the species richness, fluvial geomorphology variables (river sinuosity and gravel bar) were significantly related to the phylogenetic diversity. However, there was no statistical evidence for a relationship between functional diversity and the environmental variables examined. This study emphasized the necessity of including multiple diversity measures beyond taxonomic diversity and demonstrated the importance of both terrestrial and aquatic components in shaping the multifaceted biodiversity pattern of riparian organisms living in riparian zones. The results suggested that conservation priority should be given to both rivers and banks in the middle reaches and that riparian birds could be good candidate indicators of environmental change in the submontane river-forest ecotone.

Updating Distribution, Ecology, and Hotspots for Three Amphibian Species to Set Conservation Priorities in a European Glacial Refugium

The Calabrian Peninsula (Southern Italy) has acted as a glacial refugium and is now considered a hotspot for the genetic diversity of several species. Even if it hosts the highest diversity of many Italian endemic amphibian species, the distribution of some of these needs an update to address conservation measures. We took advantage of a vast dataset for three Italian species (Bombina pachypus, Salamandrina terdigitata, Triturus carnifex), two of which are endemic, deriving from a 40-year field surveys dataset (1982–2022), to update their distribution and basic ecological requirements. We evaluated changes in their distribution, projecting them on a broader spatial scale through a kernel density estimation, inferring statistically-significant hotspots using Corine Land Cover patches, and assessing the protected areas’ coverage. We confirmed that Pollino, Catena Costiera, Sila and Aspromonte massifs are the main statistically-significant hotspots. Kernel densities showed a diversified pattern of gains/losses, sometimes overlapping, depending on the species. The whole outcomes obtained allow us to pinpoint specific areas where effective conservation measures need to be applied. Ousr findings reveal that local-scale monitoring and management should be planned, especially within the existing nationally-designated protected areas, which have been shown to protect far less with respect to the Natura 2000 sites.

A Catchment‐Based Hierarchical Spatial Tessellation Approach to a Better Representation of Land Heterogeneity for Hyper‐Resolution Land Surface Modeling

AbstractTo represent the physical processes at hillslope scales for hyper‐resolution land surface modeling, we propose a hierarchical, catchment‐based spatial tessellation method. The land surface is divided into a hierarchical structure: catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. This catchment‐based structure explicitly represents hillslope drainage networks and can be applied at various resolutions determined by a pre‐defined maximum height band size. The proposed tessellation method is superior to the conventional grid‐based structure in representing land surface heterogeneity, resulting in a higher aggregation skill through the height band representation. The spatial variations in air temperature, leaf area index, saturated soil hydraulic conductivity, and soil porosity are generally lower within a height band than those in a conventional rectangular grid, reflecting the nature of topographic control on climate, vegetation, and soil distribution. The improvement in aggregation skill depends on resolutions and terrain slope angle, more pronounced at 1/6° model resolution and over steeper terrains. Finally, we demonstrate that our proposed catchment‐based structure performs better than the grid‐based structure through modeling tests over the Columbia River basin at resolutions of 1/2°, 1/6°, and 1/20° and a global test at 1/2° using the ILAMB model evaluation metrics.

Classification of Wetland Vegetation Based on NDVI Time Series from the HLS Dataset

Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches.

Observational Scale Matters for Ecosystem Services Interactions and Spatial Distributions: A Case Study of the Ussuri Watershed, China

Understanding how observational scale affects the interactions and spatial distributions of ecosystem services is important for effective ecosystem assessment and management. We conducted a case study in the Ussuri watershed, Northeast China, to explore how observational scale (1 km to 15 km grid resolution) influences the correlations and spatial distributions of ecosystem services. Four ecosystem services of particular importance for the sustainable development of the study area were examined: carbon sequestration, habitat provision, soil retention, and water retention. Across the observational scales examined, trade-offs and synergies of extensively distributed ecosystem services were more likely to be robust compared with those of sparsely distributed ecosystem services, and hot/cold-spots of ecosystem services were more likely to persist when associated with large rather than small land-cover patches. Our analysis suggests that a dual-purpose strategy is the most appropriate for the management of carbon sequestration and habitat provision, and cross-scale management strategies are the most appropriate for the management of soil retention and water retention in the study area. Further studies to deepen our understanding of local landscape patterns will help determine the most appropriate observational scale for analyzing the spatial distributions of these ecosystem services.

Spatial Association of Urban Form and Particulate Matter.

Increasingly detrimental effects of fine particulate matter (PM) have been observed in Northeast Asia owing to its rapid economic development. Previous studies have found that dust, combustion, and chemical reactions are the major sources of PM; nevertheless, the spatial configuration of land use and land cover, which is of most interest to planners and landscape architects, also influences the PM levels. Here, we attempted to unveil the relationship between PM and different types of land use cover (i.e., developed, agricultural, woody, grass, and barren lands) in 122 municipalities of Korea. Landscape ecology metrics were applied to measure the spatial configuration of land use pattern and spatial lag models by taking into account the transboundary nature of air pollution, allowing us to conclude the following regarding PM levels: (1) the size of land cover type matters, but their spatial configuration also determines the variations in PM levels; (2) the contiguity and proximity of landcover patches are important; (3) the patterns of grasslands (e.g., simple, compact, and cluster (with large patches) patterns) and woodlands (e.g., complex, contiguous, and cluster (with large patches) patterns) considered desirable for minimizing PM are dissimilar in terms of contiguity.

The importance of spatial configuration of neighbouring land cover for explanation of surface temperature of individual patches in urban landscapes

ContextRelationships between spatial configuration of urban form and land surface temperature (LST) in the excess heat mitigation context are studied over larger tracts of land not allowing for micro-scale recommendations to urban design.ObjectivesTo identify spatial configuration descriptors (SCDs) of urban form and the size of zone of influence conducive to the formation of the coldest and hottest land cover (LC) patches of different types (buildings, grass, paved and trees) from 2 m resolution LC and 2 and 100 m resolution LST maps at two time-steps in the summer.MethodsRandom Forest regression models were deployed to explain the LST of individual LC patches of different types based on SCDs of core LC patches and patches in their neighbourhoods. ANOVA was used to determine significantly different values of the most important SCDs associated with the coldest and hottest LC patches, and analysis of quartiles informed specification of their ranges.ResultsUrban form in the immediate neighbourhood to the core LC patches had a strong influence on their LST. Low elevation, high proximity to water, and high aggregation of trees, being important to the formation of the coldest patches of all types. High resolution of LST contributed to a higher accuracy of results. Elevation and proximity to water gained in importance as summer progressed.ConclusionsSpatial configuration of urban form in the nearest proximity to individual LC patches and the use of fine resolution LST data are essential for issuing heat mitigation recommendations to urban planners relevant to micro-scales.

Effects of Land Use-Land Cover Thematic Resolution on Environmental Evaluations

Land use/land cover (LULC) maps are a key input in environmental evaluations for the sustainable planning and management of socio-ecological systems. While the impact of map spatial resolution on environmental assessments has been evaluated by several studies, the effect of thematic resolution (the level of detail of LU/LC typologies) is discordant and still poorly investigated. In this paper, four scenarios of thematic resolutions, corresponding to the four levels of the CORINE classification scheme, have been compared in a real case study of landscape connectivity assessment, a major aspect for the biodiversity conservation and ecosystem service provision. The PANDORA model has been employed to investigate the effects of LULC thematic resolution on Bio-Energy Landscape Connectivity (BELC) at the scale of the whole system, landscape units, and single land cover patches, also in terms of ecosystem services. The results show different types of impacts on landscape connectivity due to the changed spatial pattern of the LULC classes across the four thematic resolution scenarios. Moreover, the main priority areas for conservation objectives and future sustainable urban expansion have been identified. Finally, several indications are given for supporting practitioners and researchers faced with thematic resolution issues in environmental assessment and land use planning.

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

The monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

Modelling the Impacts of Habitat Changes on the Population Density of Eurasian Skylark (Alauda arvensis) Based on Its Landscape Preferences

The dramatic decline of the abundance of farmland bird species can be related to the level of land-use intensity or the land-cover heterogeneity of rural landscapes. Our study area in central Europe (Hungary) included 3049 skylark observation points and their 600 m buffer zones. We used a very detailed map (20 × 20 m minimum mapping unit), the Hungarian Ecosystem Basemap, as a land-cover dataset for the calculation of three landscape indices: mean patch size (MPS), mean fractal dimension (MFRACT), and Shannon diversity index (SDI) to describe the landscape structure of the study areas. Generalized linear models were used to analyze the effect of land-cover types and landscape patterns on the abundance of the Eurasian skylark (Alauda arvensis). According to our findings, the proportions of arable land, open sand steppes, closed grassland patches, and shape complexity and size characteristics of these land cover patches have a positive effect on skylark abundance, while the SDI was negatively associated with the skylark population. On the basis of the used statistical model, the abundance density (individuals/km*) of skylarks could be estimated with 37.77% absolute percentage error and 2.12 mean absolute error. We predicted the skylark population density inside the Natura 2000 Special Protected Area of Hungary which is 0–6 individuals/km* and 23746 ± 8968 skylarks. The results can be implemented for the landscape management of rural landscapes, and the method used are adaptable for the density estimation of other farmland bird species in rural landscapes. According to our findings, inside the protected areas should increase the proportion, the average size and shape complexity of arable land, salt steppes and meadows, and closed grassland land cover patches.

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

Geographic object-based image analysis (GEOBIA) is a primary remote sensing tool utilized in land-cover mapping and change detection. Land-cover patches are the primary data source for landscape metrics and ecological indicator calculations; however, their application to visual landscape character (VLC) indicators was little investigated to date. To bridge the knowledge gap between GEOBIA and VLC, this paper puts forward the theoretical concept of using viewpoint as a landscape imageability indicator into the practice of a multi-temporal land-cover case study and explains how to interpret the indicator. The study extends the application of GEOBIA to visual landscape indicator calculations. In doing so, eight different remote sensing imageries are the object of GEOBIA, starting from a historical aerial photograph (1957) and CORONA declassified scene (1965) to contemporary (2018) UAV-delivered imagery. The multi-temporal GEOBIA-delivered land-cover patches are utilized to find the minimal isovist set of viewpoints and to calculate three imageability indicators: the number, density, and spacing of viewpoints. The calculated indicator values, viewpoint rank, and spatial arrangements allow us to describe the scale, direction, rate, and reasons for VLC changes over the analyzed 60 years of landscape evolution. We found that the case study nature reserve (“Kózki”, Poland) landscape imageability transformed from visually impressive openness to imageability due to the impression of several landscape rooms enclosed by forest walls. Our results provide proof that the number, rank, and spatial arrangement of viewpoints constitute landscape imageability measured with the proposed indicators. Discussing the method’s technical limitations, we believe that our findings contribute to a better understanding of land-cover change impact on visual landscape structure dynamics and further VLC indicator development.

Meta-replication, sampling bias, and multi-scale model selection: A case study on snow leopard (Panthera uncia) in western China.

Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining species distribution and their influences on ecological responses. This study explores multi‐scale habitat relationships of the snow leopard (Panthera uncia) in two study areas on the Qinghai–Tibetan Plateau of western China. Our primary objectives were to evaluate the degree to which snow leopard habitat relationships, expressed by predictors, scales of response, and magnitude of effects, were consistent across study areas or locally landcape‐specific. We coupled univariate scale optimization and the maximum entropy algorithm to produce multivariate SDMs, inferring the relative suitability for the species by ensembling top performing models. We optimized the SDMs based on average omission rate across the top models and ensembles’ overlap with a simulated reference model. Comparison of SDMs in the two study areas highlighted landscape‐specific responses to limiting factors. These were dependent on the effects of the hydrological network, anthropogenic features, topographic complexity, and the heterogeneity of the landcover patch mosaic. Overall, even accounting for specific local differences, we found general landscape attributes associated with snow leopard ecological requirements, consisting of a positive association with uplands and ridges, aggregated low‐contrast landscapes, and large extents of grassy and herbaceous vegetation. As a means to evaluate the performance of two bias correction methods, we explored their effects on three datasets showing a range of bias intensities. The performance of corrections depends on the bias intensity; however, density kernels offered a reliable correction strategy under all circumstances. This study reveals the multi‐scale response of snow leopards to environmental attributes and confirms the role of meta‐replicated study designs for the identification of spatially varying limiting factors. Furthermore, this study makes important contributions to the ongoing discussion about the best approaches for sampling bias correction.

Trait–environment relationships of plant species at different stages of the introduction process

The success of alien plant species can be attributed to differences in functional traits compared to less successful aliens as well as to native species, and thus their adaptation to environmental conditions. Studies have shown that alien (especially invasive) plant species differ from native species in traits such as specific leaf area (SLA), height, seed size or flowering period, where invasive species showed significantly higher values for these traits. Different environmental conditions, though, may promote the success of native or alien species, leading to competitive exclusion due to dissimilarity in traits between the groups. However, native and alien species can also be similar, with environmental conditions selecting for the same set of traits across species. So far, the effect of traits on invasion success has been studied without considering environmental conditions. To understand this interaction we examined the trait–environment relationship within natives, and two groups of alien plant species differing in times of introduction (archaeophytes vs. neophytes). Further, we investigated the difference between non-invasive and invasive neophytes. We analyzed the relationship between functional traits of 1,300 plant species occurring in 1000 randomly selected grid-cells across Germany and across different climatic conditions and land-cover types. Our results show that temperature, precipitation, the proportion of natural habitats, as well as the number of land-cover patches and geological patches affect archaeophytes and neophytes differently, regarding their level of urbanity (in neophytes negative for all non-urban land covers) and self-pollination (mainly positive for archaeophytes). Similar patterns were observed between non-invasive and invasive neophytes, where additionally, SLA, storage organs and the beginning of flowering were strongly related to several environmental factors. Native species did not express any strong relationship between traits and environment, possibly due to a high internal heterogeneity within this group of species. The relationship between trait and environment was more pronounced in neophytes compared to archaeophytes, and most pronounced in invasive plants. The alien species at different stages of the invasion process showed both similarities and differences in terms of the relationship between traits and the environment, showing that the success of introduced species is context-dependent.

Environmental Outcomes of Urban Land System Change: Comparing Riparian Design Approaches in the Phoenix Metropolitan Area

In the face of climate change and other environmental challenges, an increasing number of cities are turning to land design to enhance urban sustainability. Land system architecture (LSA)—which examines the role of size, shape, distribution, and connectivity of land units in relation to the system’s social-environmental dynamics—can be a useful perspective for examining how land contributes to the social and environmental aspects of urban sustainability. There are two gaps, however, that prevent LSA from fully contributing to urban sustainability dialogues. First, it is not well understood how urban design goals, as expressed by urban planners and other practitioners, relate to LSA and environmental outcomes. Second, most LSA work focuses on individual environmental outcomes, such as the urban heat island effect, instead of considering the broader suite of outcomes that LSA changes impact. Here, we undertake an integrated assessment of LSA impacts on surface urban heat island (based on land surface temperature), vegetation presence/health (based on NDVI), and bird biota at two riparian sites with different design intentions in the Phoenix, Arizona metropolitan area. The Rio Salado in Tempe underwent a city-led, infill redevelopment that mixed economic, recreational, and flood control design goals. The New River in Peoria experienced a more typical developer-driven urbanization. The contexts and design goals of the sites generated differences in their LSA, but only a few of these differences were sufficiently unique to contribute to divergent environmental outcomes. These differences reside in (1) the greater distribution of recreational land-covers and (2) increased surface water at the Rio Salado site compared to the New River site. Both changes are linked to land-cover patches becoming greener and cooler as well as a greater presence of waterbird and warbler species at the Rio Salado site. The distinctions between the sites provide insight for crafting design goals for redeveloping or restoring urban riparian landscapes in the Phoenix metropolitan area that are grounded in LSA. With the incorporation of additional relevant variables, especially socioeconomic ones, the research approach employed in this study provides a foundation for the assessment of other urban land system change.

Change in landscape and ecosystems services as the basis of monitoring natural protected areas: a case study in the Picos de Europa National Park (Spain).

Services provided by protected areas (PAs) are based on their natural heritage, as stated in the conservation objectives, and many depend on their landscape, the spatial pattern of land cover patches. We study changes in the service provision of a PA based on its landscape changes. As services are defined according to the conservation objectives of the PA, this can be used in monitoring the PA conservation status. Using easily accessible historical land cover maps, landscape changes over time are identified and assessed according to changes in services provision, based on the PA conservation objectives. The PA under study is one of the oldest in Europe. Almost 80years after its initial declaration, it was increased by incorporating surrounding areas. This helps to understand to what extent the oldest PA has conserved its environmental heritage, compared with what happened in the area most recently protected and not subjected to previous conservation regime. As expected in an area managed for conservation, changes in services in the older PA are small. But changes in the recently included area are also small and very similar to those ones. As change in ecosystem services are based on landscape changes, they do not relate only to the increase or decrease in the spatial distribution of single land covers but also on their joint spatial arrangement. This allows us to assess trade-offs among services provided by different land covers implied in landscape changes.

Urban land cover type determines the sensitivity of carbon dioxide fluxes to precipitation in Phoenix, Arizona.

Urbanization modifies land surface characteristics with consequent impacts on local energy, water, and carbon dioxide (CO2) fluxes. Despite the disproportionate impact of cities on CO2 emissions, few studies have directly quantified CO2 conditions for different urban land cover patches, in particular for arid and semiarid regions. Here, we present a comparison of eddy covariance measurements of CO2 fluxes (FC) and CO2 concentrations ([CO2]) in four distinct urban patches in Phoenix, Arizona: a xeric landscaping, a parking lot, a mesic landscaping, and a suburban neighborhood. Analyses of diurnal, daily, and seasonal variations of FC and [CO2] were related to vegetation activity, vehicular traffic counts, and precipitation events to quantify differences among sites in relation to their urban land cover characteristics. We found that the mesic landscaping with irrigated turf grass was primarily controlled by plant photosynthetic activity, while the parking lot in close proximity to roads mainly exhibited the signature of vehicular emissions. The other two sites that had mixtures of irrigated vegetation and urban surfaces displayed an intermediate behavior in terms of CO2 fluxes. Precipitation events only impacted FC in urban patches without outdoor water use, indicating that urban irrigation decouples CO2 fluxes from the effects of infrequent storms in an arid climate. These findings suggest that the proportion of irrigated vegetation and urban surfaces fractions within urban patches could be used to scale up CO2 fluxes to a broader city footprint.

Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery.

Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth’s subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size (n) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 m spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 m land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R2 is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size NS obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NS is much smaller than n, which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.

A multi-taxa functional diversity assessment of the effects of eco-estate development in the mixed land-use mosaic of the KwaZulu-Natal North Coast, South Africa

Global biodiversity is currently under threat from human population expansion and the required land transformation for shelter and resources. Land transformation, in the form of agriculture or urbanisation is believed to reduce habitats and their resources, increase fragmentation of natural habitats, and increase the likelihood of successful invasion by exotic species. These all affect existing biodiversity, its structure, and the ecosystem services which it provides. In recent years, development from natural and agricultural (mainly sugarcane) land into eco-estates (housing developments with the intention of conserving the environment) has become increasingly popular along the north coast of KwaZulu-Natal (KZN), South Africa. However, research on their impacts on the local environment is limited. We predicted that these eco-estates are, by their design, improving the functional diversity of amphibians, birds, and mammals, and ultimately ecosystem functioning. A multi-taxa assessment of the three components of functional diversity (functional richness, functional evenness, and functional divergence) suggested that eco-estate development was improving certain components for each of the taxa assessed. However, the management and development of these eco-estates was the key determinant. Eco-estates with increased interconnected indigenous forest and thicket/dense bush, with reduced housing and road density, exhibited improved functional diversity in comparison with those more densely inhabited containing isolated natural land-cover patches. We believe that these eco-estates could provide an effective mitigation method of population expansion whilst maintaining ecosystem health, but only under the recommended development and management plans.

Spatial–Temporal Super-Resolution Land Cover Mapping With a Local Spatial–Temporal Dependence Model

The mixed pixel problem is common in remote sensing. A soft classification can generate land cover class fraction images that illustrate the areal proportions of the various land cover classes within pixels. The spatial distribution of land cover classes within each mixed pixel is, however, not represented. Super-resolution land cover mapping (SRM) is a technique to predict the spatial distribution of land cover classes within the mixed pixel using fraction images as input. Spatial–temporal SRM (STSRM) extends the basic SRM to include a temporal dimension by using a finer-spatial resolution land cover map that pre- or postdates the image acquisition time as ancillary data. Traditional STSRM methods often use one land cover map as the constraint, but neglect the majority of available land cover maps acquired at different dates and of the same scene in reconstructing a full state trajectory of land cover changes when applying STSRM to time-series data. In addition, the STSRM methods define the temporal dependence globally, and neglect the spatial variation of land cover temporal dependence intensity within images. A novel local STSRM (LSTSRM) is proposed in this paper. LSTSRM incorporates more than one available land cover map to constrain the solution, and develops a local temporal dependence model, in which the temporal dependence intensity may vary spatially. The results show that LSTSRM can eliminate speckle-like artifacts and reconstruct the spatial patterns of land cover patches in the resulting maps, and increase the overall accuracy compared with other STSRM methods.