Land Cover Composition Research Articles

Impacts of Proximity to Primary Source Areas on Concentrations of POPs at Global Sampling Stations Estimated from Land Cover Information.

Given the considerable financial and logistical resources supporting long-term monitoring for air pollutants, and the use of these data for performance evaluation of mitigation measures, it is important to account for contributions from primary versus secondary sources. We demonstrate a simple approach for using open source Global land cover raster data from the National Mapping Organization from the Geospatial Information Authority of Japan to assess local source inputs for air measurements of legacy persistent organic pollutants (POPs)-polychlorinated biphenyls (PCBs) and organochlorine pesticides-reported under the Global atmospheric passive sampling (GAPS) Network at 119 locations for the time period 2005-2014. The land cover composition within a 10 km radius around the GAPS sites was identified to create source impact indicator (SII) vectors to quantify and rank the remoteness of the sites from human infrastructure. Using principal component analysis, three SII vectors were established to rank sites by impact of (i) general infrastructure/remoteness, (ii) urban infrastructure, and (iii) agricultural infrastructure. General infrastructure describes the combined effects of settlements and agricultural infrastructure. We found significant correlations (p < 0.05) between POP concentrations in air and specific SIIs. PCB levels in air had a statistically significant correlation to the SII ranking urban impacts around the sampling sites, while Endosulfan I, Endosulfan II, and Endosulfan sulfate had a statistically significant correlation with SII ranking agricultural impacts. The complete GAPS data set from 2004-2014 (1040 samples at 119 locations) was standardized based on the SII rankings to assess the global temporal trends of legacy POPs. SIIs were incorporated in the multiple regression analysis to determine global halving times. This includes short-term monitoring data from 79 locations that were previously excluded. Furthermore, the SII approach allows the integration of global monitoring data from different studies for broader global temporal trend analysis. This ability to link the results of independent and small-scale studies can enhance temporal trend analysis in support of the larger scale initiatives, such as inter alia, the Global Monitoring Plan and Effectiveness Evaluation of the Stockholm Convention in the case of POPs. This simple approach using open source data has a broad potential for application for other classes of air pollutants.

Northern bobwhite breeding season and nest survival are greater on native grasslands

AbstractNorthern bobwhites (Colinus virginianus) are declining across their range. Population recovery requires a better understanding of the influence of management strategies and land cover on specific demographic components of full annual cycle population dynamics. We used known‐fate daily survival models to relate adult breeding season survival and nest survival to land cover composition, woody cover, and management actions on 5 study sites in southwest Missouri, USA. We collected 3,516 observations of daily nest survival from 364 bobwhite nests for 7,509 exposure days and 34,386 observations of adult survival from 955 bobwhites for 85,225 exposure days during breeding seasons in 2014–2018. The most supported model for nest and adult breeding season survival included environmental covariates for percent native grass and agricultural cover, distance to shrub, and distance to tree. Both nest and adult breeding season survival were positively related to distance to trees. Nest survival was negatively related to percent agricultural crop cover. Areal nest and adult breeding season survival were greater on native grassland sites compared to conservation areas that maintained small units of strip crop and wooded cover. Management to sustain or expand native grass cover and reduce tree cover should benefit bobwhite nest survival and adult breeding season survival in the landscapes we studied.

How have South Australian urban planning policies affected Blakeview’s surface temperatures?

ABSTRACT The South Australian Government is creating plans, policies, and strategies (PPS) that are preferencing infill development. While there are benefits to infill development, if scientific evidence is not adequately incorporated into these PPS, they can elevate urban temperatures. This study investigated Blakes Crossing, an infill development of Blakeview (South Australia) to identify the extent to which scientific evidence has informed the PPS that guided its urban fabric and their impact on Blakeview’s urban temperature. This was achieved by comparatively analysing the temporal PPS that guided Blakes Crossing’s urban fabric, with primary remote sensing data (using Landsat 8 imagery) that identified and compared Blakes Crossing’s land cover composition and surface temperature against four neighbouring areas. This study found that all tiers of government have applied a reductionist approach when applying science to form their PPS. This has resulted in inadequate cooling policies and principles being incorporated into Blakes Crossing’s urban design, thereby, elevating its surface temperature by as much as 2.67°C when compared to this study’s other analysed areas and consequently, elevating Blakeview’s surface temperature. To ameliorate the disconnect between science and planning, scientific tools such as remote sensing should help guide future PPS to effectively reduce urban temperatures.

Community dynamics of bumblebee across elevation gradients and habitat mosaics in Chitwan Annapurna Landscape, Nepal

The species composition of bumblebees (Bombus species) across the elevation gradients in Chitwan-Annapurna Landscape (CHAL) was studied from April to November 2019. We performed opportunistic surveys to collect the bumblebee specimens. The walking transects were followed in the accessible places along the Kaligandaki, Marshyandi, and Budhigandaki river basins in different habitats (e.g., agricultural, forest, grassland and home garden). We identified 16 Bombus species from the sampling areas. The highest relative abundance was of B. haemorrhoidalis (20%), followed by B. festivus (20%) and B. eximius (19%). The least abundant species were B. branickii, B. miniatus, B. novus, and B. pressus with 1% relative abundance of each. We examined the effects of elevation on bumblebee richness and found a significant relationship. The Highest species richness was detected in the mid-elevation. Likewise, the highest species richness and diversity were found in the forest habitat in Gorkha site (n = 12, Shannon index H’ = 2.18) followed by the grassland habitat of the Mustang site (n = 11, Shannon index H’ = 2.10). Whereas, comparatively, species diversity was higher in habitats of the Gorkha site comparing Manang and Mustang. The elevation gradients create immense variations in microclimatic conditions and vegetation dynamics, which influence bumblebee abundance, species richness and diversities in different habitats in the study area. The mid-elevation range (2000–3000 m asl) of CHAL exhibited the highest species richness probably due to the higher availability of pollinator-dependent flowering plants in this range. The landcover composition and anthropogenic activities along the elevation gradient is the governing factor for the species composition, distribution and diversity of bumblebees in CHAL. We recommend to decision-makers for formulating their conservation strategies under a socio-ecological framework.

Depensation in fish recruitment driven by context-dependent interactions with another predator

Recruitment depensation describes elevated juvenile mortality with declining adult population size which can prevent or delay stock recovery. Understanding the factors influencing when a population undergoes depensation provides resource agencies with targets for management action. Using estimates of depensation from 28 walleye (Sander vitreus, Percidae) populations in Wisconsin identified by Sass et al., (2021), we tested for potential abiotic and biotic predictors of walleye recruitment depensation. The best fitting model contained covariates for climate, land cover, and fish community composition, all interacting with the relative abundance of largemouth bass (Micropterus salmoides, Centrarchidae). The consistent interaction effect of largemouth bass across the other covariates suggests a key role of this species in regulating walleye recruitment dynamics at low population size. The risk of depensation was negatively correlated with largemouth bass abundance in our dataset, pointing towards continued challenges for walleye populations given the increasingly favorable social and environmental conditions for largemouth bass. Using the model, vulnerability to depensation was predicted for an additional 115 walleye lakes with insufficient data to directly estimate the risk of depensation. Predictions suggested that 73 prediction lakes are vulnerable to depensatory recruitment should population sizes significantly decrease. This predictive framework could be used to prioritize lakes for different management actions based on depensation strength and average adult population size. Lakes with low walleye abundances, but low risk of depensation, may be more likely to respond positively to management efforts and are likely better candidates than those where depensation effects are likely strong when abundance is low.

Assessment of public and private land cover change in the United States from 1985–2018

An assessment of annual land cover on publicly and privately managed lands across the conterminous United States (CONUS) from 1985–2018 was performed, including land cover conversions within their management category, to inform future policy and land-use decision-making in natural resource management. Synthesizing land cover data with land management delineations aids our ability to address effects of land management decisions by public or private entities. The U.S. Geological Survey (USGS) Protected Areas Database of the United States (PAD-US) version 2.1 data delineate land management categories and enable examination of land cover composition and change using the USGS Land Change Monitoring, Assessment, and Projection (LCMAP) reference data. Average composition of our delineated CONUS results using LCMAP land cover classes is 40% Grass/Shrub (GS), 29% Tree Cover (TC), 18% Cropland (CP), 5% Developed (DV), 5% Wetland (WL), 1.8% Water (WR), and 0.9% Barren (BN). Private (public) land is composed of 35% (52%) GS, 27% (36%) TC, 25% (1%) CP, 7% (1%) DV, 5% (5%) WL, 2% (2%) WR, and less than 1% (3%) BN. Land cover change averaged less than 1% per year. The largest net percentage gains across CONUS were in DV land and GS, and the greatest net losses were in CP and TC. Approximately 73% of CONUS is private land and, thus, land cover change across CONUS is largely a reflection of private land change dynamics. Private compositional changes show net gains from 1985–2018 in DV (2.3%), WR (0.2%), and GS (0.1%) classes, while net losses occurred in CP (−1.9%), TC (−0.6%), WL (−0.1%), and BN (−0.01%). Public land cover changes show net gains in GS (1%), DV (0.2%), WR (0.01%), WL (0.05%), and BN (0.1%) classes, and net losses in CP (−0.3%) and TC (−1%). Our study reveals connections between land cover conversion and various policy and socioeconomic decisions through time.

Microclimate and its influencing factors in residential public spaces during heat waves: An empirical study in Hong Kong

Creating thermally comfortable living environment under the new normal of heat waves requires pertinent knowledge as references. In subtropical residential areas, various types of public spaces exist, whose microclimate differences during heat waves, as well as their relationship with surrounding building and greenery characteristics, have not been systematically investigated. We therefore evaluated microclimate performances in three typical types of residential public spaces, i.e., open squares, vegetated spaces, and semi-outdoor spaces, and their relationship with surrounding built environment during summertime heat waves. Field measurement of microclimate parameters was conducted in two selected public housing estates in Hong Kong, followed by calculation of thermal comfort indices. Their relationship with building and greenery factors was analyzed. Results show inconsistent patterns in different microclimate variables among three types of spaces across different times of the day, while thermal comfort conditions in the three types of spaces are significantly different. In vegetated spaces and open squares, three-dimensional factors play dominant roles in determining microclimate and thermal comfort condition, with sky view factor (SVF) contributing the most. We detected key SVF threshold for effective thermal comfort enhancement around 0.4, based on which we discussed building and greenery optimization in similar urban context from a point-based SVF perspective and its potential application in practice. In semi-outdoor spaces, two-dimensional land-cover composition contributes greater than three-dimensional factors. This study provides empirical evidence on thermal performance of residential public spaces, which can assist practitioners in achieving adaptation to heat waves in high-density urban contexts in subtropical regions.

Ecosystem restoration reduces community vulnerability to water-induced disasters: Need to rethink Chure conservation in Nepal

Disaster risk reduction strategies are often accompanied by conventional adaptation approaches, involving high costs and limited flexibility. Conversely, ecosystem-based adaptation (EbA) has been advocated as a sustainable approach because it is cost-effective, efficient and provides co-benefits (e.g., ecosystem services). However, there is limited awareness and understanding among both disaster risk reduction professionals and ecologists about the linkages of ecosystem restoration to disaster risk reduction. Here, we assessed people's perceptions towards the availability of ecosystem services in the highly degraded and fragile landscape to floods and soil erosion in Central Nepal. We then assessed vulnerabilities at household level in both pre- and post-restoration periods, using indicators such as distance to the river, land cover composition (ratio of forest to non-forest), occupation, availability of ecosystem services, and effectiveness of the restoration projects in the vicinity. Our results show that vulnerability as a function of adaptive capacity and sensitivity decreased even though there were no significant improvements in some of the provisions of ecosystem services we assessed. Since our results showed restoration projects were not integrated following the principles of EbA criteria, careful integration during project design and implementation would strengthen community resilience in future. Our work provides new insight into the restoration response to disasters and provides a basis for future research and policy development.

Multicriteria land cover design via coupled hydrologic and multi-sector water management models

We investigate how hydrologic-land feedbacks and a hydrologic-water management linkage impact land cover arrangements optimized within a multiobjective land cover design framework. The framework integrates a spatially-distributed and physically-based hydrologic model, for simulating surface and subsurface flow and land processes, with a network-based multi-sector water resources management and allocation model. Both models used (Parflow, Pywr) are open-source. The framework is applied to a hillslope problem to identify land cover patterns that optimize trade-offs between water, food, energy and environment objectives. Results show trade-offs depend on land cover composition and the spatial arrangement of land covers within the catchment. Total runoff volume and peak flow of runoff was found to change 3 and 2-fold, respectively, between optimized solutions as land cover composition and spatial patterns were altered to satisfy different combinations of objectives. At the same time, up to a 15% reduction in the total runoff volume and an 8% reduction in the peak flow of runoff were observed within optimized land cover patterns having equal composition but different spatial arrangements. This emphasizes the impact on hydrologic behavior of the spatial location of land covers within a catchment. The emergence of patterns in land cover distribution for different trade-offs between objectives is driven by feedback mechanisms between subsurface hydrology and land processes, which are implicitly linked to the properties of each land cover and the interactions between neighboring land covers through lateral groundwater flow. The study demonstrates the added benefits of coupling distributed hydrologic models with water management simulation for multisector multicriteria land cover planning.

Plant species composition and local habitat conditions as primary determinants of terrestrial arthropod assemblages

Arthropods respond to vegetation in multiple ways since plants provide habitat and food resources and indicate local abiotic conditions. However, the relative importance of these factors for arthropod assemblages is less well understood. We aimed to disentangle the effects of plant species composition and environmental drivers on arthropod taxonomic composition and to assess which aspects of vegetation contribute to the relationships between plant and arthropod assemblages. In a multi-scale field study in Southern Germany, we sampled vascular plants and terrestrial arthropods in typical habitats of temperate landscapes. We compared independent and shared effects of vegetation and abiotic predictors on arthropod composition distinguishing between four large orders (Lepidoptera, Coleoptera, Hymenoptera, Diptera), and five functional groups (herbivores, pollinators, predators, parasitoids, detritivores). Across all investigated groups, plant species composition explained the major fraction of variation in arthropod composition, while land-cover composition was another important predictor. Moreover, the local habitat conditions depicted by the indicator values of the plant communities were more important for arthropod composition than trophic relationships between certain plant and arthropod species. Among trophic groups, predators showed the strongest response to plant species composition, while responses of herbivores and pollinators were stronger than those of parasitoids and detritivores. Our results highlight the relevance of plant community composition for terrestrial arthropod assemblages across multiple taxa and trophic levels and emphasize the value of plants as a proxy for characterizing habitat conditions that are hardly accessible to direct environmental measurements.

Understanding spatiotemporal evolution of the surface urban heat island in the Bangkok metropolitan region from 2000 to 2020 using enhanced land surface temperature

The urbanization process has significantly intensified surface urban heat island (SUHI) effects in the Bangkok Metropolitan Region (BMR). However, understanding the evolution of the urban thermal environment is challenging due to the difficulty in obtaining consistent remote sensing data of the cloud-prone landscape in the BMR. In this study, the data fusion algorithm was utilized to fill cloud-induced data gap and create high spatiotemporal-resolution data by blending Landsat and MODIS remote sensing images. The fused data was used to retrieve land surface temperature (LST) for winter months from 2000 to 2020. The spatiotemporal variations in SUHI were then captured using spatial cluster analysis. Finally, gradient analysis and geographically weighted regression (GWR) were employed to analyse the effects of land cover composition on LST. The SUHI intensity in winter increased from 4.40 °C in 2000 to 5.76 °C in 2020. The areal percentage of SUHI hot spots increased from 24.86% to 29.13%. The gradient analysis results indicated that vegetation with a density higher than 0.3 had a significant effect on LST compared to low-density areas. The woody lands were more effective in lowering LST than cultivated lands. These results provide useful information for developing heat mitigation strategies in the metropolitan regions.

Insights into the Effect of Urban Morphology and Land Cover on Land Surface and Air Temperatures in the Metropolitan City of Milan (Italy) Using Satellite Imagery and In Situ Measurements

With a concentration of people, activities, and infrastructures, urban areas are particularly vulnerable to the negative effects of climate change. Among others, the intensification of the Urban Heat Island (UHI) effect is leading to an increased impact on citizen health and the urban ecosystem. In this context, this study aims to investigate the effect of urban morphology and land cover composition—which are established by exploiting the Local Climate Zone (LCZ) classification system—on two urban climate indicators, i.e., Land Surface Temperature (LST) and air temperature. The study area is the Metropolitan City of Milan (northern Italy). LCZ and LST maps are derived by leveraging satellite imagery and building height datasets. Both authoritative and crowdsourced in situ measurements are used for the analysis of air temperature. Several experiments are run to investigate the mutual relation between LCZ, LST, and air temperature by measuring LST and air temperature patterns in different LCZs and periods. Besides a strong temporal correlation between LST and air temperature, results point out vegetation and natural areas as major mitigating factors of both variables. On the other hand, higher buildings turn out to increase local air temperature while buffering LST values. A way lower influence of building density is measured, with compact building areas experiencing slightly higher air temperature yet no significant differences in terms of LST. These outcomes provide valuable tools to urban planners and stakeholders for implementing evidence-based UHI mitigation strategies.

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification.

As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.

CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic

Abstract. The entire Arctic is rapidly warming, which brings in a multitude of environmental consequences far beyond the northern high-latitude limits. Land cover maps offer biophysical insights into the terrestrial environment and are therefore essential for understanding the transforming Arctic in the context of anthropogenic activity and climate change. Satellite remote sensing has revolutionized our ability to capture land cover information over large areas. However, circumpolar Arctic-scale fine-resolution land cover mapping has so far been lacking. Here, we utilize a combination of multimode satellite observations and topographic data at 10 m resolution to provide a new baseline land cover product (CALC-2020) across the entire terrestrial Arctic for circa 2020. Accuracy assessments suggest that the CALC-2020 product exhibits satisfactory performances, with overall accuracies of 79.3 % and 67.3 %, respectively, at validation sample locations and field/flux tower sites. The derived land cover map displays reasonable agreement with pre-existing products, meanwhile depicting more subtle polar biome patterns. Based on the CALC-2020 dataset, we show that nearly half of the Arctic landmass is covered by graminoid tundra or lichen/moss. Spatially, the land cover composition exhibits regional dominance, reflecting the complex suite of both biotic and abiotic processes that jointly determine the Arctic landscape. The CALC-2020 product we developed can be used to improve Earth system modelling and benefit the ongoing efforts on sustainable Arctic land management by public and non-governmental sectors. The CALC-2020 land cover product is freely available on Science Data Bank: https://doi.org/10.57760/sciencedb.01869 (Xu et al., 2022a).

Impact of agricultural landscape structure on the patterns of bird species diversity at a regional scale

The loss of bird species diversity is a crucial problem in the European agricultural landscape. Change in the area coverage of major land cover types has been mentioned as one of the main factors responsible for bird biodiversity impoverishment. In this study, we focused on the impact of landscape matrix characteristics on bird species richness and on Faith's phylogenetic diversity index on a spatial scale of 1000-m radius around the measured occurrence points. We investigated how land cover composition affects bird diversity on the landscape scale using nationwide citizen science data. In total, 168,739 records of bird occurrence in the South Moravian Region of the Czech Republic during growing season from 2009 to 2019 were evaluated. We found that the presence of water bodies and wetlands significantly corresponded to the areas of highest bird species richness. We also revealed that the presence of forests (∼60% of the forest in the Czech Republic is occupied by commercial forests), urban areas and arable land were negatively associated with bird species richness and phylogenetic diversity. Forests (both coniferous and deciduous) and urban habitats were found to have a tendency to host a clustered phylogenetic community structure in comparison with wetland and arable land. A strong negative association between forest proportion and bird diversity led us to conclude that the expansion of the forest (with simple species composition, horizontal and vertical structure) could be one of the critical drivers of the decline of bird species diversity in the European agricultural landscape. On the other hand, our results also pointed out that small woody features (i.e., woodlots) and scattered woodland shrub vegetation were one of the main landscape characteristics supporting a bird diversity in rural landscape. This is in concordance with other studies which mention these landscape structures as important elements for nesting and foraging of farmland birds. We thus recommend to maintain and restore scattered trees or woodlots with complex structure in agricultural landscape.

A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models

Growing concerns on excessive urban heat call for better approaches to modeling urban thermal environment and developing effective mitigation strategies. A hybrid model integrating the geographically weighted regression (GWR) and deep neural network (DNN) was developed to estimate land surface temperature (LST). This model was compared with three other data-driven approaches to predicting LST, including the ordinary least squares (OLS) regression, GWR, and DNN. Sixteen satellite image datasets (a total of 155,728 data points) during a four-year period in Hong Kong were used for model development, validation, and comparison. The datasets cover two distinguishable geographical regions and consist of sixteen explanatory variables from five groups, including (1) land use and land cover (LULC) composition and surface characteristics, (2) LULC configuration, (3) urban form, (4) anthropogenic activities, and (5) location and local climate. The results indicate that the hybrid model performs the best in terms of model fitness and prediction accuracy, with R2 equal to 0.85 and 0.73 and the mean squared error (MSE) equal to 0.52 and 0.70 in the two regions, respectively. Compared to the OLS, DNN, and GWR models, the overall R2 for all the datasets of the hybrid model increases by 97.3%, 16.6%, and 6.9%, respectively. The promising result of the hybrid model is due to its ability to capture both spatial heterogeneity and address possible correlations between explanatory variables. Sensitivity of LST to various explanatory variables is also discussed and strategies to mitigate excessive heat are recommended. This study is anticipated to contribute to model development in urban LST estimation and quantitative evaluation of those factors driving LST variations.

Factors Associated with American Kestrel (Falco sparverius) Nest Box Occupancy and Reproductive Success in an Agricultural Landscape

ABSTRACT The American Kestrel (Falco sparverius), a small, cavity-nesting falcon that breeds across most of North America, is experiencing an apparent population decline across much of the United States that has yet to be sufficiently explained. With landscape change as a potential factor, the association of landscape composition with kestrel reproduction may assist not only in managing declines but also in improving the effectiveness and efficiency of nest box programs, which can bolster local populations and support monitoring and research efforts. We monitored 163 unique American Kestrel nest boxes over a 12-yr study period (2008–2019) in western Virginia to investigate the factors influencing kestrel reproductive success and we documented relatively high rates of both box occupancy (80%) and nest success (78.9%). In a study area dominated by both high- and low-intensity agriculture, we focused on identifying patterns in land cover composition that may limit reproductive success and associated each box with land cover composition metrics at four spatial scales. Generalized linear mixed models indicated nest boxes with a lower proportion of developed land within 500 m, and a higher proportion of crops and pasture/hay within 3 km were more likely to be occupied by kestrels. Although probability of kestrel nest success increased with reduced proportions of developed land within 100 m, the primary factors influencing success were clutch initiation date and the presence of European starlings (Sturnus vulgaris), with earlier nesting and the absence of starling activity resulting in higher nest success. Overall, cover-type associations with nest success and box occupancy were not strong and establishment of nest boxes in targeted kestrel habitat, while likely maximizing reproductive output, limits research opportunities to identify critical land cover thresholds for the species.

Relationships between fishery catch rates and land cover along a longitudinal gradient in floodplains of the Amazon River

Land cover is an important driver of patterns of fish diversity and assemblage structure in Amazonian floodplains. In the floodplain of the Amazon River, remarkable changes in the land cover composition across a longitudinal gradient, from the middle Amazon to the lower Amazon, likely affects fishery catch rates at local scales. Here, we investigated potential relationships between fishery catch rates and extent of land cover types in lakes of these two regions, the middle and lower floodplains of the Amazon River. Areas of flooded shrubs, herbaceous vegetation, and open water, within buffers of 1000 and 2000 m, were quantified around the areas of 23 lakes and modeled as predictors of fishing catch rate in the two subregions of the River i.e., the lower and middle Amazon, while these regions were also included as fixed effects of our models. Comparisons of the spatial extent of the land cover types between the two regions showed a predominance of flooded herbaceous vegetation in the downstream-lower portion, and a predominance of flooded shrubs and forests upstream-middle portion. In the models, the region was consistently the variable of greatest relative importance (Σwi > 0.9), and indicates higher fishing catch rate in the middle Amazon region when compared to the lower Amazon. Flooded shrub quantified on the smallest scale was the land cover type of strongest influence driving higher fishing catch rates, regardless of the region. In particular, our results support the idea that loss of flooded shrubs in adjacent areas of floodplain lakes can lead to reduced fishing catch rates at local and regional scales.

“The 20 July 2021 Major Flood Event” in Greater Zhengzhou, China: A Case Study of Flooding Severity and Landscape Characteristics

Climate change and rapid urbanization are two global processes that have significantly aggravated natural disasters, such as drought and flooding. Urbanization without resilient and sustainable planning and execution could lead to undesirable changes in landscapes and stormwater regulation capacity. These changes have exacerbated the effects of extreme climatic events with disastrous consequences in many cities worldwide. Unfortunately, the major storm in Zhengzhou, China on 20 July 2021 was one of these examples. This event provided a rare opportunity to study the key roles of green infrastructures (GI) in mitigating flooding risks in a major urban center after a devasting flood event. Using the data from high-resolution images collected via two satellites, a comprehensive study of the Jialu System in Greater Zhengzhou was conducted to systematically compare how far the river water had reached before and after the 20 July 2021 major storm in order to identify the main weak links in the city’s GI and stormwater management system. A flood inundation intensity index (FI) in the Upper (UJLR), Middle (MJLR), and Lower (LJLR) Regions of the Jialu River System was generated. Bivariate Moran’s I, a correlation coefficient between FI and landscape characteristics, was calculated and used to identify problem areas for future improvements. Our results showed that the MJLR had the severest flooding impacts. LJLR had the biggest change in how far the river water reached after flooding, ranging from 4.59 m to 706.28 m. In UJLR, the percentages of mine, crop land, and green space had the highest global bivariate Moran’s I correlation coefficients. In MJLR, the percentages of vacant land, impervious surfaces, and water body had the highest global bivariate Moran’s I correlation coefficients. In LJLR, the percentages of vacant land, water body, and crop land had the highest global bivariate Moran’s I correlation coefficients. The total percentages of both high landscape characteristics indices-high flood inundation intensity indices and low landscape characteristics indices-high flood inundation intensity indices areas are 12.96%, 13.47%, and 13.80% in UJLR, MJLR, and LJLR, respectively. These land cover composition types identified for each region can be treated as areas of primary focus. However, GeoDector Model (GDM) analyses showed that our eight variables of landscape characteristics were not independent. Hence, a more comprehensive approach integrating all eight variables is still necessary in future flood mitigation efforts.

Surviving in a hostile landscape: Nothofagus alessandrii remnant forests threatened by mega-fires and exotic pine invasion in the coastal range of central Chile

AbstractNothofagus alessandrii, categorized as Endangered on the IUCN Red List, is an endemic, deciduous tree species of the coastal range of central Chile. We assessed the effects of fire severity, invasion by the exotic fire-prone Pinus radiata, and land-cover composition and configuration of the landscape on the resilience of fragments of N. alessandrii after a mega-fire in 2017. We used remote sensing data to estimate land-use classes and cover, fire severity and invasion cover of P. radiata. We monitored forest composition and structure and post-fire responses of N. alessandrii forests in situ for 2 years after the mega-fire. In the coastal Maule region wildfires have been favoured by intense drought and widespread exotic pine plantations, increasing the ability of fire-adapted invasive species to colonize native forest remnants. Over 85% of N. alessandrii forests were moderately or severely burnt. The propagation and severity of fire was probably amplified by the exotic pines located along the edges of, or inside, the N. alessandrii fragments and the highly flammable pine plantations surrounding these fragments (&gt; 60% of land use is pine plantations). Pinus radiata, a fire-adapted pioneer species, showed strong post-fire recruitment within the N. alessandrii fragments, especially those severely burnt. Positive feedback between climate change (i.e. droughts and heat waves), wildfires and pine invasions is driving N. alessandrii forests into an undesirable and probably irreversible state (i.e. a landscape trap). A large-scale restoration programme to design a diverse and less flammable landscape is needed to avoid the loss of these highly threatened forest ecosystems.