Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America.

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.

Understanding variation in impacts from private protected areas across regions and protection mechanisms to inform organizational practices.

Private land protection is an important and growing tool to address biodiversity loss and climate change. Thus, better empirical evidence on the effectiveness of private land protection and organizational practices, such as targeting of lands for protection and choice of protection mechanism (i.e., fee simple land acquisition and conservation easements), is needed. We addressed this gap by estimating the impacts of The Nature Conservancy's (TNC) (a large nongovernmental organization with relatively decentralized management) conservation land acquisitions and easements from 1988 to 2016 in three regions of the United States (Mid-Atlantic, New England and New York, and California). We estimated impact in terms of avoided conversion by comparing natural land cover on 3179 protected parcels with matched unprotected parcels. Nineteen of 21 ecoregional plans used threats of agriculture and development to identify priorities for protection. When regions and protection mechanisms were pooled, on average there was no evidence of avoided conversion from 1988 to 2016. Accounting for mechanisms, TNC land acquisitions avoided conversion and easements did not. TNC's easements on parcels acquired by conservation partners did avoid conversion. Limitations of these results include focus on a single measure of impact, inability to capture future avoided conversion, and low land cover change accuracy in California. Our results suggest that private land protection managers who seek to avoid land conversion in the near to medium term should increase focus on areas with higher threats. Special attention should be paid to strengthening accountability and the role of partners, improving or clarifying how easements are used, and facilitating the flow of resources to work with the greatest potential impact.

Comprensión de la variación en el impacto de las áreas protegidas privadas sobre las regiones y los mecanismos de protección para guiar las prácticas organizativas Resumen La protección de terreno privado es una herramienta importante, aunque su naturaleza voluntaria puede sesgar la protección hacia parcelas menos amenazadas­lo que resulta en que la conversión no se evite o se evite muy poco. Además, muchos programas de protección privada tienen una supervisión limitada y pocos reportes de sus resultados. Por lo tanto, se necesitan mejores evidencias empíricas de la efectividad de la protección en suelo privado y las prácticas organizativas, como el enfoque en tierras para protección y la selección de los mecanismos de protección (adquisición de terrenos a título oneroso y servidumbres de conservación). Abordamos esta brecha con la estimación de impactos de las adquisiciones de suelo de The Nature Conservancy (TNC, una gran organización no gubernamental con un manejo relativamente descentralizado) y las servidumbres implementadas entre 1988 y 2016 en tres regiones de los Estados Unidos: Atlántico Medio, Nueva Inglaterra y Nueva York, y California. Estimamos el impacto en términos de la conversión que se evitó al comparar la cobertura de suelo en 3,179 parcelas protegidas con parcelas desprotegidas equivalentes. Diecinueve de los 21 planes eco­regionales usaron las amenazas a la agricultura y al desarrollo para identificar las prioridades de protección. Cuando agrupamos las regiones y los mecanismos de protección, en promedio no hubo impacto alguno. Si se consideran los mecanismos, las adquisiciones de suelo de la TNC tuvieron un impacto mientras que las servidumbres no. Las servidumbres de la TNC en las parcelas adquiridas por socios de conservación sí tuvieron un impacto, aunque esta manera de proteger sólo se presentó en las regiones del Atlántico Medio y de Nueva Inglaterra y Nueva York. Nuestros resultados sugieren que la protección privada, especialmente mediante servidumbres, puede estar sesgada hacia suelos no amenazados. Los gestores que buscan evitar la conversión del suelo a mediano o corto plazo deberían enfocarse más en las áreas con más amenazas. Se debería prestar especial atención al fortalecimiento del papel y las responsabilidades de los socios, a la mejora o aclaración de cómo se usan las servidumbres y la facilitación del flujo de recursos para trabajar con el mayor impacto potencial.

Dynamics of carbon storage driven by land use/land cover transformation in coal mining areas with a high groundwater table: A case study of Yanzhou Coal Mine, China.

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985-1995, 1995-2005, 2005-2015, and 2015-2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management.

Cluster pattern analysis of environmental stressors and quantifying their impact on all-cause mortality in Belgium.

Environmental stress represents an important burden on health and leads to a considerable number of diseases, hospitalisations, and excess mortality. Our study encompasses a representative sample size drawn from the Belgian population in 2016 (n = 11.26 million, with a focus on n = 11.15 million individuals). The analysis is conducted at the geographical level of statistical sectors, comprising a total of n = 19,794 sectors, with a subset of n = 18,681 sectors considered in the investigation. We integrated multiple parameters at the finest spatial level and constructed three categories of environmental stress through clustering: air pollution, noise stress and stress related to specific land-use types. We observed identifiable patterns in the spatial distribution of stressors within each cluster category. We assessed the relationship between age-standardized all-cause mortality rates (ASMR) and environmental stressors. Our research found that especially very high air pollution values in areas where traffic is the dominant local component of air pollution (ASMR + 14,8%, 95% CI: 10,4 - 19,4%) and presence of industrial land (ASMR + 14,7%, 95% CI: 9,4 - 20,2%) in the neighbourhood are associated with an increased ASMR. Cumulative exposure to multiple sources of unfavourable environmental stress (simultaneously high air pollution, high noise, presence of industrial land or proximity of primary/secondary roads and lack of green space) is associated with an increase in ASMR (ASMR + 26,9%, 95% CI: 17,1 - 36,5%).

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data.

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery.

Are human-induced changes good or bad to dynamic landscape connectivity?

Land managers must find a compromise between rapidly changing landscapes and biodiversity conservation through ecological networks. Estimating ecological networks is a key approach to enhance or maintain functional connectivity by identifying the nodes and links of a graph, which represent habitats and their corresponding functional corridors, respectively. To understand the current state of biodiversity, it is necessary to consider dynamic landscape connectivity while relying on relevant land cover maps. Although a current land cover map is relatively easy to produce using existing data, this is challenging for past landscapes. Here we investigated the impact of changes in landscape connectivity in an urban landscape at a fine scale on the habitat availability of two bird species: the tree pipit Anthus trivialis and the short-toed treecreeper Certhia brachydactyla. These species, exhibiting different niche ecologies, have shown contrasting population trends at a medium-term scale. The occurrences of C. brachydactyla were better correlated with resistance values that maximise the use of corridors, whereas the occurrences of A. trivialis better fitted with intermediate resistance values. The statistical approach indirectly highlighted relevant information about the ecology the capacity of both species to use urban habitats. Landscape connectivity increased for both species over the 24-year study period and may have implications for local abundances, which could explain, at the national scale, the increase in populations of C. brachydactyla, but not the decrease in populations of A. trivialis. Thus, more attention must be paid on rural habitats and their associated species that are more impacted by human activities, but efforts could also be achieved in urban areas especially for highly corridor-dependent species. Studying dynamic landscape connectivity at a fine scale is essential for estimating past and future land cover changes and their associated impacts on ecological networks, to better reconcile human and biodiversity concerns in land management.

Deep learning models for monitoring landscape changes in a UNESCO Global Geopark.

By identifying Earth heritage sites, UNESCO Global Geoparks (UGGps) have promoted geo-tourism and regional economic prosperity. However, commercial and tourism development has altered the natural contexts of these geoparks, diminishing their initial value. Before implementing land use policies, spatial landscape parameters should be monitored in multiple dimensions and in real time. This study aims to develop Bilateral Segmentation Network (BiSeNet) models employing an upgraded U-structured neural network in order to monitor land use/cover changes and landscape indicators in a Vietnamese UGGp. This network has proven effective at preserving input image data and restricting the loss of spatial information in decoding data. To demonstrate the utility of deep learning, eight trained BiSeNet models were evaluated against Maximum Likelihood, Support Vector Machine, and Random Forest. The trained BSN-Nadam model (128x128), with a precision of 94% and an information loss of 0.1, can become a valuable instrument for analyzing and monitoring monthly changes in land uses/covers once tourism activities have been rapidly expanded. Three tourist routes and 41 locations in the Dak Nong UGGp were monitored for 30 years using three landscape indices: Disjunct Core Area Density (DCAD), Total Edge Contrast Index (TECI), Shannon's Diversity Index (SHDI), based on the results of the model. As a result, 18 identified geo-sites in the Daknong Geopark have been influenced significantly by agricultural and tourist activities since 2010, making these sites less uniform and unsustainable management. It promptly alerts UNESCO management to the deterioration of geological sites caused by urbanization and tourist development.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach.

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45-59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83-87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89-91) and fallow land (CN = 88-93). The period 1985-1995 experienced an increased ET scenario (911-1050 mm), while the recent period (2005-2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Ecosystem health assessment of East Kolkata Wetlands, India: Implications for environmental sustainability.

The East Kolkata Wetlands (EKW) in Kolkata, India, span 12,500 ha and are a vital ecological zone providing several benefits, including water purification, flood control, and biodiversity support. This study investigated land use and land cover (LULC) alterations in the EKW from 1991 to 2023, using a random forest (RF) machine learning model. Significant LULC changes were observed over the 32 years, with wetland areas decreasing from 91.2 km2 in 1991 to 33.4 km2 in 2023, reflecting substantial habitat loss and reduced ecosystem services. Conversely, agricultural land expanded from 27.8 km2 to 58.7 km2, driven by economic and food production needs, and built-up areas increased dramatically from 0.2 km2 to 10.5 km2, indicating rapid urbanization. This study evaluated the health, resilience, and ecosystem functionality of EKW by analysing human-induced land use changes and using ecological indicators and landscape metrics. Landscape and class level metrics such as PLAND, largest patch index (LPI), total edge (TE), edge density (ED), number of patches (NP), and patch density (PD) were used to analyse the spatiotemporal dynamics of the wetlands. This study revealed a significant increase in fragmentation, with the number of patches increasing from 2689 in 1991 to 4532 in 2023, despite a consistent decrease in core wetland areas. Ecosystem health indicators, such as the ecosystem structure index (ESI) and landscape deviation degree (LDD), were used to assess landscape metrics and fragmentation changes. The ESI and other metrics revealed significant temporal fluctuations, providing insights into landscape structure, connectivity, and heterogeneity. The ESI improved from 0.87 in 1991 to 1.03 in 2023, indicating enhanced connectivity and diversity. Conversely, the LDD increased from 20.6% to 56.85%, indicating a shift towards impervious surfaces. The vegetation productivity and ecosystem health index (EHI) decreased, indicating biodiversity loss and reduced carbon sequestration. The EHI also dropped from 0.67 to 0.55, signifying ongoing environmental stress. This study emphasizes the need for conservation efforts to maintain the ecological integrity of the EKW amidst urbanization and land use changes and recommends a balanced approach for sustainable urban development and enhanced wetland resilience.

Spatial targeting of irrigation development and water resource management to mitigate vector-borne disease.

Scaling irrigated agriculture is a global strategy to mitigate food insecurity concerns. While expanding irrigated agriculture is critical to meeting food production demands, it is important to consider how these land use and land cover changes (LULCC) may alter the water resources of landscapes and impact the spatiotemporal epidemiology of disease. Here, a generalizable method is presented to inform irrigation development decision-making aimed at increasing crop production through irrigation while simultaneously mitigating malaria risk to surrounding communities. Changes to the spatiotemporal patterns of malaria vector (Anopheles gambiae s.s.) suitability, driven by irrigated agricultural expansion, are presented for Malawi's rainy and dry seasons. The methods presented may be applied to other geographical areas where sufficient irrigation and malaria prevalence data are available. Results show that approximately 8.60% and 1.78% of Malawi is maximally suitable for An. gambiae s.s. breeding in the rainy and dry seasons, respectively. However, the proposed LULCC from irrigated agriculture increases the maximally suitable land area in both seasons: 15.16% (rainy) and 2.17% (dry). Proposed irrigation development sites are analyzed and ranked according to their likelihood of increasing malaria risk for those closest to the schemes. Results illustrate how geospatial information on the anticipated change to the malaria landscape driven by increasing irrigated agricultural extent can assist in altering development plans, amending policies, or reassessing water resource management strategies to mitigate expected changes in malaria risk.

Responses of precipitation and water vapor budget on the Chinese Loess Plateau to global land cover change forcing.

There have been notable changes in precipitation patterns on the Loess Plateau (LP) of China in recent decades, and numerous attribution studies have focused on sea surface temperature anomalies and atmospheric circulation changes induced by aerosols and greenhouse gases emission. However, the influences of global land use and land cover change (LULCC) as an important forcing factor in the climate system on regional precipitation remains poorly understood. In this study, we quantified the impacts of LULCC on precipitation and the water vapor budget in the LP region, utilizing data from LULCC forcing experiments conducted by the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Although global LULCC forcing exerted a negative effect on long-term mean precipitation on the LP region from 1850 to 2014, the different response characteristics were detected during different time periods. The global LULCC caused a decrease of 14 mm in annual precipitation during the period of 1850-1960. Conversely, from 1961 to 2014, it led to an increase of 6.4 mm, which is largely attributed to the enhanced water vapor transport along the southern boundary and westerly belt of the LP region. Moreover, from the perspective of the net water vapor balance of the entire LP, although LULCC caused net water vapor export during both periods 1850-1960 and 1961-2014, the export during the latter period (0.20 × 104 kg s-1) was smaller than that during the former period (0.28 × 104 kg s-1), indicating that the global expansion of grassland and cropland, along with the continuous rise in the leaf area index from 1961 to 2014, contributed to retaining more water vapor within the LP, which in turn was more favorable for precipitation. These findings provide valuable insights into the reasons behind precipitation variations in the LP region, emphasizing that global vegetation restoration and greening play a significant role in improving precipitation in ecologically fragile areas.

Identifying broken linkages coupling water availability and dryland urbanization for sustainability: The case of the Phoenix metropolitan region, USA.

One third of the world's largest cities are located in drylands, where much of future urbanization is projected to occur. This is paradoxical and unsustainable considering water scarcity in drylands, which is exacerbated by climate change. Thus, it is critical to better understand why and how dryland urbanization and water scarcity are decoupled so that sustainable measures can be designed. Focusing on the Phoenix Metropolitan Area (PMA) of the United States, we addressed the following questions: 1) What are the relative influences of water and economic factors on urbanization in recent decades? 2) Which linkages connecting water storage to urban development have been decoupled? and 3) How can water availability and development be better coupled to improve regional sustainability? We tested the relationships between economic factors, water availability, and urbanization, with Pearson Correlation Analysis and Structural Equation Modeling. We found that, from 1986 to 2019, urban population growth and urban land expansion in the PMA were driven by economic factors, and not influenced by fluctuations in water supply. We identified specific broken linkages among water storage, water deliveries, municipal water supply, and urbanization, which must be coupled to enforce water availability constraints on urban expansion in the context of climate change. Our study has important implications for dryland urban sustainability as urbanization on borrowed water is, by definition, unsustainable.

Towards understanding climate change: Impact of land use indices and drainage on land surface temperature for valley drainage and non-drainage areas.

The continuous increase of urbanization and industrialization brought various climatic changes, leading to global warming. The unavailability of meteorological data makes remotely sensed data important for understanding climate change. Therefore, the land surface temperature (LST) is critical in understanding global climate changes and related hydrological processes. The main objective of this work is to explore the dominant drivers of land use and hydrologic indices for LST in drainage and non-drainage areas. Specifically, the relationship between LST changes, land use, and hydrologic indices in Northeast Qena, Egypt, was investigated. The Landsat 5 and 8 imagery, Geographic Information System (GIS), and R-package were applied to identify the change detection during 2000-2021. The normalized difference between vegetation index (NDVI), bare soil index (BSI), normalized difference built-up, built-up index (BUI), modified normalized difference water index (MNDWI), and soil-adjusted vegetation index (SAVI) were employed. The non-drainage or mountain areas were found to be more susceptible to high LST values. The comprehensive analysis and assessment of the spatiotemporal changes of LST indicated that land use and hydrologic indices were driving factors for LST changes. Considerably, LST retrieved from the Landsat imaginary showed significant variation between the maximum LST during 2000 (44.82°C) and 2021 (50.74°C). However, NDBI has got less spread during the past (2000) with 10-13%. A high negative correlation was observed between the LST and NDVI, while the SAVI and LST positively correlated. The results of this study provide relevant information for environmental planning to local management authorities.

Community-based assessment of the dynamics of urban landscape characteristics and ecosystem services in the rainforest and guinea savanna ecoregions of Nigeria.

Understanding the dynamics of urban landscapes and their impacts on ecological well-being is crucial for developing sustainable urban management strategies in times of rapid urbanisation. This study assesses the nature and drivers of the changing urban landscape and ecosystem services in cities located in the rainforest (Akure and Owerri) and guinea savannah (Makurdi and Minna) of Nigeria using a combination of remote sensing and socioeconomic techniques. Landsat 8 datasets provided spatial patterns of the normalised difference vegetation index (NDVI) and normalised difference built-up index (NDBI). A household survey involving the administration of a semi-structured questionnaire to 1552 participants was conducted. Diminishing NDVI and increasing NDBI were observed due to the rising trend of urban expansion, corroborating the perception of over 54% of the respondents who noted a decline in landscape ecological health. Residential expansion, agricultural practices, transport and infrastructural development, and fuelwood production were recognised as the principal drivers of landscape changes. Climate variability/change reportedly makes a 28.5%-34.4% (Negelkerke R2) contribution to the changing status of natural landscapes in Akure and Makurdi as modelled by multinomial logistic regression, while population growth/in-migration and economic activities reportedly account for 19.9%-36.3% in Owerri and Minna. Consequently, ecosystem services were perceived to have declined in their potential to regulate air and water pollution, reduce soil erosion and flooding, and mitigate urban heat stress, with a corresponding reduction in access to social services. We recommend that urban residents be integrated into management policies geared towards effectively developing and enforcing urban planning regulations, promoting urban afforestation, and establishing sustainable waste management systems.

Centennial dynamics of floodplain wetland in the largest freshwater lake in China: Implications on floodplain lake restoration.

Long-term mapping of floodplain wetland dynamics is fundamental for wetland protection and restoration, but it is restricted to decadal scales using satellite observations owing to scarcity of spatial data over long-term scales. The present study concentrates on the centennial dynamics of floodplain wetland in Poyang Lake, the largest freshwater lake in China. Historical topographic maps and Landsat imagery were combined to reconstruct the centennial floodplain wetland map series. A robust random forest algorithm for the land cover classification was used to investigate the conversion of the floodplain wetland to other land cover types and quantify the magnitude of the influence of hydrological disconnection over the past century. Results show that the Poyang Lake floodplain wetland experienced a net loss of 35.7 %, from 5024.3 km2 in the 1920s-1940s to 3232.1 km2 in the 2020s, with the floodplain wetland loss occurring mostly from the 1950s to the 1970s. In addition, agricultural encroachment was identified as the predominant driver of floodplain wetland loss, with a total area of 931.0 km2 of the floodplain wetland converted into cropland. Furthermore, approximately 600 km2 of sub-lakes (larger than 1 km2) became isolated from the floodplain and thus unaffected by seasonal flood pulses, which highlights the need to account for the impact of hydrological disconnection on floodplain wetland dynamics. This study indicated the combination of historical maps and satellite observations as an effective tool to track long-term wetland changes. The resultant dataset provides an extended baseline and could shed some light on floodplain wetland conservation and restoration.

Long-term vegetation trajectories to inform nature recovery strategies: The Greater Côa Valley as a case study.

Active and passive approaches to rewilding and ecological restoration are increasingly considered to promote nature recovery at scale. However, historical data on vegetation trajectories have rarely been used to inform decisions on whether active or passive management is most appropriate to aid recovery of a specific ecosystem, which can lead to sub-optimal approaches being deployed and reduced biodiversity benefits. To demonstrate how understanding past changes can inform future management strategies, this study used satellite remote sensing data to analyse the changes in land cover and primary productivity within the Greater Côa Valley in Portugal, which has experienced wide-scale land abandonment. Results show that some areas in the Valley regenerated well following land abandonment in the region, leading to a more heterogeneous landscape of habitats for wildlife, whereas in other areas passive recovery was slow. As Rewilding Portugal intensifies its nature recovery efforts in the region, this study calls for strategic deployment of passive and active approaches to maximise conservation benefits. More broadly, our results highlight how baseline vegetational trajectories and contextual information can help inform whether active or passive management approaches may be suitable on a site-by-site basis for both rewilding and restoration projects.

Spatial distribution of degradation and deforestation of palm swamp peatlands and associated carbon emissions in the Peruvian Amazon.

The vast peat deposits in the Peruvian Amazon are crucial to the global climate. Palm swamp, the most extensive regional peatland ecosystem faces different threats, including deforestation and degradation due to felling of the dominant palm Mauritia flexuosa for fruit harvesting. While these activities convert this natural C sink into a source, the distribution of degradation and deforestation in this ecosystem and related C emissions remain unstudied. We used remote sensing data from Landsat, ALOS-PALSAR, and NASA's GEDI spaceborne LiDAR-derived products to map palm swamp degradation and deforestation within a 28 Mha area of the lowland Peruvian Amazon in 1990-2007 and 2007-2018. We combined this information with a regional peat map, C stock density data and peat emission factors to determine (1) peatland C stocks of peat-forming ecosystems (palm swamp, herbaceous swamp, pole forest), and (2) areas of palm swamp peatland degradation and deforestation and associated C emissions. In the 6.9 ± 0.1 Mha of predicted peat-forming ecosystems within the larger 28 Mha study area, 73% overlaid peat (5.1 ± 0.9 Mha) and stored 3.88 ± 0.12 Pg C. Degradation and deforestation in palm swamp peatlands totaled 535,423 ± 8,419 ha over 1990-2018, with a pronounced dominance for degradation (85%). The degradation rate increased 15% from 15,400 ha y-1 (1990-2007) to 17,650 ha y-1 (2007-2018) and the deforestation rate more than doubled from 1,900 ha y-1 to 4,200 ha y-1. Over 1990-2018, emissions from degradation amounted to 26.3 ± 3.5 Tg C and emissions from deforestation were 12.9 ± 0.5 Tg C. The 2007-2018 emission rate from both biomass and peat loss of 1.9 Tg C yr-1 is four times the average biomass loss rate due to gross deforestation in 2010-2019 reported for the hydromorphic Peruvian Amazon. The magnitude of emissions calls for the country to account for deforestation and degradation of peatlands in national reporting.

Impacts of future climate and land use/land cover change on urban runoff using fine-scale hydrologic modeling.

Future changes in land use/land cover (LULC) and climate (CC) affect watershed hydrology. Despite past research on estimating such changes, studies on the impacts of both these nonstationary stressors on urban watersheds have been limited. Urban watersheds have several important details such as hydraulic infrastructure that call for fine-scale models to predict the impacts of LULC and CC on watershed hydrology. In this paper, a fine-scale hydrologic model-Personal Computer Storm Water Management Model (PCSWMM)-was applied to predict the individual and joint impacts of LULC changes and CC on surface runoff attributes (peak and volume) in 3800 urban subwatersheds in Midwest Florida. The subwatersheds a range of characteristics in terms of drainage area, surface imperviousness, ground slope and LULC distribution. The PCSWMM also represented several hydraulic structures (e.g., ponds and pipes) across the subwatersheds. We analyzed changes in the runoff attributes to determine which stressor is most responsible for the changes and what subwatersheds are mostly sensitive to such changes. Six 24-h design rainfall events (5- to 200-year recurrence intervals) were studied under historical (2010) and future (year 2070) climate and LULC. We evaluated the response of the subwatersheds in terms of runoff peak and volume to the design rainfall events using the PCSWMM. The results indicated that, overall, CC has a greater impact on the runoff attributes than LULC change. We also found that LULC and climate induced changes in runoff are generally more pronounced in greater recurrence intervals and subwatersheds with smaller drainage areas and milder slopes. However, no relationship was found between the changes in runoff and original subwatershed imperviousness; this can be due to the small increase in urban land cover projected for the study area. This research helps urban planners and floodplain managers identify the required strategies to protect urban watersheds against future LULC change and CC.

Assessment of vegetation net primary productivity variation and influencing factors in the Beijing-Tianjin-Hebei region.

Exploring the spatiotemporal variations of vegetation net primary productivity (NPP) and analyzing the relationships between NPP and its influencing factors are vital for ecological protection in the Beijing-Tianjin-Hebei (BTH) region. In this study, we employed the CASA model in conjunction with spatiotemporal analysis techniques to estimate and analyze the spatiotemporal variations of NPP in BTH and different ecological function sub-regions over the past two decades. Subsequently, we established three scenarios (actual, climate-driven and land cover-driven) to assess the influencing factors and quantify their relative contributions. The results indicated that the overall NPP in BTH exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 3.83 gC·m-2a-1. Furthermore, all six sub-regions exhibited an increase. The Bashang Plateau Ecological Protection Zone (BP) exhibited the highest growth rate (5.03 gC·m-2a-1), while the Low Plains Ecological Restoration Zone (LP) exhibited the lowest (2.07 gC·m-2a-1). Geographically, the stability of NPP exhibited a spatial pattern of gradual increase from west to east. Climate and land cover changes collectively increased NPP by 0.04 TgC·a-1 and 0.07 TgC·a-1, respectively, in the BTH region. Climate factors were found to have the greatest influence on NPP variations, contributing 40.49% across the BTH region. This influence exhibited a decreasing trend from northwest to southeast, with precipitation identified as the most influential climatic factor compared to temperature and solar radiation. Land cover change has profound effects on ecosystems, which is an important factor on NPP. From 2000 to 2020, 15.45% area of the BTH region underwent land cover type change, resulting in a total increase in NPP of 1.33 TgC. The conversion of grass into forest brought about the 0.89 TgC increase in NPP, which is the largest of all change types. In the area where land cover had undergone change, the land cover factor has been found to be the dominant factor influencing variations in NPP, with an average contribution of 49.37%. In contrast, in the south-central area where there has been no change in land cover, the residual factor has been identified as the most influential factor influencing variations in NPP. Our study highlights the important role of land cover change in influencing NPP variations in BTH. It also offers a novel approach to elucidating the influences of diverse factors on NPP, which is crucial for the scientific assessment of vegetation productivity and carbon sequestration capacity.