Correlated Decision Fusion Accompanied with Quality Information on a Multi-Band Pixel Basis for Land Cover Classification.

Decision fusion plays a crucial role in achieving a cohesive and unified outcome by merging diverse perspectives. Within the realm of remote sensing classification, these methodologies become indispensable when synthesizing data from multiple sensors to arrive at conclusive decisions. In our study, we leverage fully Polarimetric Synthetic Aperture Radar (PolSAR) and thermal infrared data to establish distinct decisions for each pixel pertaining to its land cover classification. To enhance the classification process, we employ Pauli's decomposition components and land surface temperature as features. This approach facilitates the extraction of local decisions for each pixel, which are subsequently integrated through majority voting to form a comprehensive global decision for each land cover type. Furthermore, we investigate the correlation between corresponding pixels in the data from each sensor, aiming to achieve pixel-level correlated decision fusion at the fusion center. Our methodology entails a thorough exploration of the employed classifiers, coupled with the mathematical foundations necessary for the fusion of correlated decisions. Quality information is integrated into the decision fusion process, ensuring a comprehensive and robust classification outcome. The novelty of the method is its simplicity in the number of features used as well as the simple way of fusing decisions.

Analysis of land use/ land cover changes and landscape fragmentation in the Baro-Akobo Basin, Southwestern Ethiopia.

This study investigated the relationship between land use/land cover (LULC) changes and forested landscape fragmentation in the southwestern region of Ethiopia. Satellite images from 1986, 2002 and 2019 were collected and analyzed using standard procedures in ERDAS 2015 software. Fragstat 4.2.1 software was utilized to assess landscape fragmentation by examining a raster datasets derived from the classified LULC map over the research period. The study identified seven LULC classes in the study area. Findings revealed a substantial reduction in shrubland by 46.3%, dense forest by 23.75%, open forest by 17.3%, and wetland by 32.63%, while cropland increased by 38.06%, agroforestry by 20.29%, and settlements by 163.8% during the study period. These changes varied across different agroecological zones and slope gradients. Landscape metrics results indicated an increase in the number of patches and patch density for all LULC classes, demonstrating significant fragmentation of the landscape. The largest patch index, mean patch areas, and the percentage of landscape occupied by open forest, dense forest, shrubland, and wetland declined as a result of conversion to cropland, agroforestry, and settlement. Conversely, the largest patch index, the mean patch area and the percentage of the landscape occupied by agroforestry, cropland and settlement increased, indicating their increasing dominance in the landscape over the study periods. The findings highlighted the potential deleterious impacts of ongoing land use change and fragmentation on the environment, ecosystem function and local livelihoods. Therefore, it is crucial to implement appropriate conservation efforts and sustainable land management practices to mitigate the rapid change and fragmentation of land use and its negative impacts on sub-watershed ecosystems.

Association between environmental gradient of anthropization and phenotypic plasticity in two species of triatomines.

BACKGROUND: Triatoma garciabesi and T. guasayana are considered secondary vectors of Trypanosoma cruzi and frequently invade rural houses in central Argentina. Wing and head structures determine the ability of triatomines to disperse. Environmental changes exert selective pressures on populations of both species, promoting changes in these structures that could have consequences for flight dispersal. The aim of this study was to investigate the relationship between a gradient of anthropization and phenotypic plasticity in flight-related traits. METHODS: The research was carried out in Cruz del Eje and Ischilín departments (Córdoba, Argentina) and included 423 individuals of the two species of triatomines. To measure the degree of anthropization, a thematic map was constructed using supervised classification, from which seven landscapes were selected, and nine landscape metrics were extracted and used in a hierarchical analysis. To determine the flight capacity and the invasion of dwellings at different levels of anthropization for both species, entomological indices were calculated. Digital images of the body, head and wings were used to measure linear and geometric morphometric variables related to flight dispersion. One-way ANOVA and canonical variate analysis (CVA) were used to analyze differences in size and shape between levels of anthropization. Procrustes variance of shape was calculated to analyze differences in phenotypic variation in heads and wings. RESULTS: Hierarchical analysis was used to classify the landscapes into three levels of anthropization: high, intermediate and low. The dispersal index for both species yielded similar results across the anthropization gradient. However, in less anthropized landscapes, the density index was higher for T. garciabesi. Additionally, in highly anthropized landscapes, females and males of both species exhibited reduced numbers. Regarding phenotypic changes, the size of body, head and wings of T. garciabesi captured in the most anthropized landscapes was greater than for those captured in less anthropized landscapes. No differences in body size were observed in T. guasayana collected in the different landscapes. However, males from highly anthropized landscapes had smaller heads and wings than those captured in less anthropized landscapes. Both wing and head shapes varied between less and more anthropogenic environments in both species. CONCLUSIONS: Results of the study indicate that the flight-dispersal characteristics of T. garciabesi and T. guasayana changed in response to varying degrees of anthropization.

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

Associations between heat waves and chronic kidney disease in China: The modifying role of land cover.

The increasing frequency of heat waves under the global urbanization and climate change background poses elevating risks of chronic kidney disease (CKD). Nevertheless, there has been no evidence on associations between long-term exposures to heat waves and CKD as well as the modifying effects of land cover patterns. Based on a national representative population-based survey on CKD covering 47,086 adults and high spatial resolution datasets on temperature and land cover data, we found that annual days of exposure to heat waves were associated with increased odds of CKD prevalence. For one day/year increases in HW_975_4d (above 97.5 % of annual maximum temperature and lasting for at least 4 consecutive days), the odds ratio (OR) of CKD was 1.14 (95 %CI: 1.12, 1.15). Meanwhile, stronger associations were observed in regions with lower urbanicity [rural: 1.14 (95 %CI: 1.12, 1.16) vs urban: 1.07 (95 %CI: 1.03, 1.11), Pinteraction < 0.001], lower water body coverage [lower: 1.14 (95 %CI: 1.12, 1.16) vs higher: 1.02 (95 %CI: 0.98, 1.05), Pinteraction < 0.001], and lower impervious area coverage [lower: 1.16 (95 %CI: 1.14, 1.18) vs higher: 1.06 (95 %CI: 1.03, 1.10), Pinteraction = 0.008]. In addition, this study found disparities in modifying effects of water bodies and impervious areas in rural and urban settings. In rural regions, the associations between heat waves and CKD prevalence showed a consistent decreasing trend with increases in both proportions of water bodies and impervious areas (Pinteraction < 0.05). Nevertheless, in urban regions, we observed significant effect modification by water bodies, but not by impervious areas. Our study indicates the need for targeted land planning as part of adapting to the kidney impacts of heat waves, with a focus on urbanization in rural regions, as well as water body construction and utilization in both rural and urban regions.

Responses of streamflow to forest expansion in a typical subhumid watershed under future climate conditions.

Water availability in the subhumid region is highly vulnerable to frequent droughts. Water scarcity in this region has become a limiting factor for ecosystem health, human livelihood, and regional economic development. A notable pattern of land cover change in the subhumid region of the United States is the increasing forest area due to afforestation/reforestation and woody plant encroachment (WPE). Given the distinct hydrological processes and runoff generation between forests and grasslands, it is important to evaluate the impacts of forest expansion on water resources, especially under future climate conditions. In this study, we focused on a typical subhumid watershed in the United States - the Little River Watershed (LRW). Utilizing SWAT + simulations, we projected streamflow dynamics at the end of the 21st century in two climate scenarios (RCP45 and RCP85) and eleven forest expansion scenarios. In comparison to the period of 2000-2019, future climate change during 2080-2099 will increase streamflow in the Little River by 5.1% in the RCP45 but reduce streamflow significantly by 30.1% in the RCP85. Additionally, our simulations revealed a linear decline in streamflow with increasing forest coverage. If all grasslands in LRW were converted into forests, it would lead to an additional 41% reduction in streamflow. Of significant concern is Lake Thunderbird, the primary reservoir supplying drinking water to the Oklahoma City metropolitan area. Our simulation showed that if all grasslands were replaced by forests, Lake Thunderbird during 2080-2099 would experience an average of 8.6 years in the RCP45 and 9.4 years in the RCP85 with water inflow amount lower than that during the extreme drought event in 2011/2012. These findings hold crucial implications for the formulation of policies related to afforestation/reforestation and WPE management in subhumid regions, which is essential to ensuring the sustainability of water resources.

Seed dormancy, climate changes, desertification and soil use transformation threaten the Mediterranean endemic monospecific plant Petagnaea gussonei.

This study investigated the germination capacity (endogenous factor) of Petagnaea gussonei (Spreng.) Rauschert, an endemic monospecific plant considered as a relict species of the ancient Mediterranean Tertiary flora. This investigation focused also on the temporal trends of soil-use, climate and desertification (exogenous factors) across the natural range of P. gussonei. The final germination percentage showed low values between 14 and 32%, the latter obtained with GA3 and agar at 10 °C. The rising temperatures in the study area will further increase the dormancy of P. gussonei, whose germination capacity was lower and slower at temperatures higher than 10 °C. A further limiting factor of P. gussonei is its dormancy, which seems to be morpho-physiological. Regarding climate trends, in the period 1931-2020, the average temperature increased by 0.5 °C, from 15.4 to 15.9 °C, in line with the projected climate changes throughout the twenty-first century across the Mediterranean region. The average annual rainfall showed a relatively constant value of c. 900 mm, but extreme events grew considerably in the period 1991-2020. Similarly, the land affected by desertification expanded in an alarming way, by increasing from 21.2% in 2000 to 47.3% in 2020. Soil-use changes created also a complex impacting mosaic where c. 40% are agricultural areas. The effective conservation of P. gussonei should be multilateral by relying on germplasm banks, improving landscape connectivity and vegetation cover, and promoting climate policies.

Employing post classification comparison to detect land use cover change patterns and quantify conversions in Abakaliki LGA Nigeria from 2000 to 2022.

Rapid urbanization is restructuring landscapes across sub-Saharan Africa. This study employed post-classification comparison of multi-temporal Landsat imagery to characterize land cover changes in Abakaliki Local Government Area, Ebonyi State, Nigeria between 2000 and 2022, addressing the need for empirical baselines to guide sustainable planning. Four classes were considered and images classified with overall accuracy of 95% for the year 2000 and 97% for the year 2022. Notably, 21,000 hectares of vegetation were lost, while built-up and bare land increased by 7500 and 13,700 hectares respectively. Spatial patterns revealed built-up encroachment from vegetation and bare land; this establishes the first standardized quantification of Abakaliki LGA's shifting landscape, with results supporting compact development models while conserving ecological services under ongoing transformations. The study makes a significant contribution by establishing an empirical baseline characterizing Nigeria's urbanization trajectory essential for evidence-based stewardship of regional resources and livelihoods in a period of accelerating change.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Ecosystem service valuation and multi-scenario simulation in the Ebinur Lake Basin using a coupled GMOP-PLUS model.

The Ebinur Lake Basin is an ecologically sensitive area in an arid region. Investigating its land use and land cover (LULC) change and assessing and predicting its ecosystem service value (ESV) are of great importance for the stability of the basin's socioeconomic development and sustainable development of its ecological environment. Based on LULC data from 1990, 2000, 2010, and 2020, we assessed the ESV of the Ebinur Lake Basin and coupled the grey multi-objective optimization model with the patch generation land use simulation model to predict ESV changes in 2035 under four scenarios: business-as-usual (BAU) development, rapid economic development (RED), ecological protection (ELP), and ecological-economic balance (EEB). The results show that from 1990 to 2020, the basin was dominated by grassland (51.23%) and unused land (27.6%), with a continuous decrease in unused land and an increase in cultivated land. In thirty years, the total ESV of the study area increased from 18.62 billion to 67.28 billion yuan, with regulation and support services being the dominant functions. By 2035, cultivated land increased while unused land decreased in all four scenarios compared with that in 2020. The total ESV in 2035 under the BAU, RED, ELP, and EEB scenarios was 68.83 billion, 64.47 billion, 67.99 billion, and 66.79 billion yuan, respectively. In the RED and EEB scenarios, ESV decreased by 2.81 billion and 0.49 billion yuan, respectively. In the BAU scenario, provisioning and regulation services increased by 6.05% and 2.93%, respectively. The ELP scenario, focusing on ecological and environmental protection, saw an increase in ESV for all services. This paper can assist policymakers in optimizing land use allocation and provide scientific support for the formulation of land use strategies and sustainable ecological and environmental development in the inland river basins of arid regions.

Black carbon in urban soils: land use and climate drive variation at the surface.

BACKGROUND: Black carbon (BC) encompasses a range of carbonaceous materials--including soot, char, and charcoal--derived from the incomplete combustion of fossil fuels and biomass. Urban soils can become enriched in BC due to proximity to these combustion sources. We conducted a literature review of BC in urban soils globally and found 26 studies reporting BC and total organic carbon (TOC) content collected to a maximum of 578 cm depth in urban soils across 35 cities and 10 countries. We recorded data on city, climate, and land use/land cover characteristics to examine drivers of BC content and contribution to TOC in soil. RESULTS: All studies were conducted in the northern hemisphere, with 68% of the data points collected in China and the United States. Surface samples (0-20 cm) accounted for 62% of samples in the dataset. Therefore, we focused our analysis on 0-10 cm and 10-20 cm depths. Urban soil BC content ranged from 0-124 mg/g (median = 3 mg/g) at 0-10 cm and from 0-53 mg/g (median = 2.8 mg/g) at 10-20 cm depth. The median proportional contribution of BC to TOC was 23% and 15% at 0-10 cm and 10-20 cm, respectively. Surface soils sampled in industrial land use and near roads had the highest BC contents and proportions, whereas samples from residential sites had among the lowest. Soil BC content decreased with mean annual soil temperature. CONCLUSIONS: Our review indicates that BC comprises a major fraction (nearly one quarter) of the TOC in urban surface soils, yet sampling bias towards the surface could hide the potential for BC storage at depth. Land use emerged as an importer driver of soil BC contents and proportions, whereas land cover effects remain uncertain. Warmer and wetter soils were found to have lower soil BC than cooler and drier soils, differences that likely reflect soil BC loss mechanisms. Additional research on urban soil BC at depth and from diverse climates is critical to better understand the role of cities in the global carbon cycle.

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.

Human intervention caused massive destruction of the second largest mangrove forest, Chakaria Sundarbans, Bangladesh.

Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering area a total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest area degradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.

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.

A deep inverse convolutional neural network-based semantic classification method for land cover remote sensing images.

The imbalance of land cover categories is a common problem. Some categories appear less frequently in the image, while others may occupy the vast majority of the proportion. This imbalance can lead the classifier to tend to predict categories with higher frequency of occurrence, while the recognition effect on minority categories is poor. In view of the difficulty of land cover remote sensing image multi-target semantic classification, a semantic classification method of land cover remote sensing image based on depth deconvolution neural network is proposed. In this method, the land cover remote sensing image semantic segmentation algorithm based on depth deconvolution neural network is used to segment the land cover remote sensing image with multi-target semantic segmentation; Four semantic features of color, texture, shape and size in land cover remote sensing image are extracted by using the semantic feature extraction method of remote sensing image based on improved sequential clustering algorithm; The classification and recognition method of remote sensing image semantic features based on random forest algorithm is adopted to classify and identify four semantic feature types of land cover remote sensing image, and realize the semantic classification of land cover remote sensing image. The experimental results show that after this method classifies the multi-target semantic types of land cover remote sensing images, the average values of Dice similarity coefficient and Hausdorff distance are 0.9877 and 0.9911 respectively, which can accurately classify the multi-target semantic types of land cover remote sensing images.

Characterising landcover changes and urban sprawl using geospatial techniques and landscape metrics in Bulawayo, Zimbabwe (1984-2022).

Urbanisation is a global trend that significantly impacts sustainable urban development and the quality of urban life. Assessing urban sprawl is critical for sustainable urban planning and aligns with the key objectives of the United Nations sustainable development goals. This study employed geospatial technology and landscape metrics to comprehensively assess, map, and quantify the extent of urban sprawl in Bulawayo from 1984 to 2022. The study leveraged the Support Vector Machine (SVM) supervised machine learning algorithm coupled with landscape metrics to achieve this objective. The combined approach allowed for the classification, detection of land cover changes, analysis of urban dynamics, and quantification of the degree of urban sprawl. The results revealed a 228% increase in built-up areas between 1984 and 2022, while non-built-up areas (agricultural land, vegetation, bare land) decreased by 29.28%. The landscape metrics and change analysis indicated an encroachment of urban-like conditions into urban areas. Land use change assessment revealed that Bulawayo exhibits four district types of urban sprawl: leapfrog, strip/ribbon, low density, and infill. Urban expansion is attributed to urbanisation and evolving land use policy. Urban sprawl has numerous urban planning implications on transport management, habitat loss and deforestation, reduction and contamination of freshwater sources, and many others. This study is strategic to planners, researchers, and decision-makers/policy makers as it provides relevant, up-to-date, and accurate information for sustainable urban planning.

Bridging the national data gap with Google earth engine and landsat imagery by developing annual land cover for Afghanistan.

The national-level land cover database is essential to sustainable landscape management, environmental protection, and food security. In Afghanistan, the existing national-level land cover data from 1972, 1993, and 2010 relied on satellite data from diverse sensors adopted three different land cover classification systems. This inconsistent land cover map across the various years leads to the challenge of assessing landscape changes that are crucial for management efforts. To address this challenge, a 19-year national-level land cover dataset from 2000 to 2018 was developed for the first time to aid policy development, settlement planning, and the monitoring of forests and agriculture across time. In the development of the 19 year span of land cover data products, a state-of-the-art remote sensing approach, employing a harmonized classification scheme was implemented through the utilization of Google Earth Engine (GEE). Publicly accessible Landsat imagery and additional geospatial covariates were integrated to produce an annual land cover database for Afghanistan. The generated dataset bridges historical data gaps and facilitates robust land cover change information. The annual land cover database is now accessible through https://rds.icimod.org/. This repository ensures that the annual land cover data is readily available to all users interested in comprehending the dynamic land cover changes happening in Afghanistan.

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.

Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data.

An integrated, remotely sensed approach to assess land-use and land-cover change (LULCC) dynamics plays an important role in environmental monitoring, management, and policy development. In this study, we utilized the advantage of land-cover seasonality, canopy height, and spectral characteristics to develop a phenology-based classification model (PCM) for mapping the annual LULCC in our study areas. Monthly analysis of normalized difference vegetation index (NDVI) and near-infrared (NIR) values derived from SPOT images enabled the detection of temporal characteristics of each land type, serving as crucial indices for land type classification. The integration of normalized difference built-up index (NDBI) derived from Landsat images and airborne LiDAR canopy height into the PCM resulted in an overall performance of 0.85, slightly surpassing that of random forest analysis or principal component analysis. The development of PCM can reduce the time and effort required for manual classification and capture annual LULCC changes among five major land types: forests, built-up land, inland water, agriculture land, and grassland/shrubs. The gross change LULCC analysis for the Taoyuan Tableland demonstrated fluctuations in land types over the study period (2013 to 2022). A negative correlation (r = - 0.79) in area changes between grassland/shrubs and agricultural land and a positive correlation (r = 0.47) between irrigation ponds and agricultural land were found. Event-based LULCC analysis for Taipei City demonstrated a balance between urbanization and urban greening, with the number of urbanization events becoming comparable to urban greening events when the spatial extent of LULCC events exceeds 1000 m2. Besides, small-scale urban greening events are frequently discovered and distributed throughout the metropolitan area of Taipei City, emphasizing the localized nature of urban greening events.

LULC Assessment and Green Infrastructure Conservation in residential neighborhoods: a case of FESTAC Town, Lagos, Nigeria.

In addressing environmental challenges and ecosystem resilience, green networks are preserved, repaired, and rebuilt by green infrastructure. However, urbanization effects have seen urban land form undergo significant modifications over time due to different anthropogenic activities. The objective of this study is to evaluate the land use and land cover (LULC) change in FESTAC Town, a government-owned residential neighborhood in Lagos, with the goal of recommending interventions for conserving green infrastructure. The study mainly focuses on employing remote sensing and geographic information system (GIS) techniques to detect alterations in land use in FESTAC Town from 1984 to 2022. The ERDAS Imagine software was utilized, employing a supervised classification-maximum likelihood algorithm, to identify changes in LULC. Additionally, an accuracy assessment was conducted using ground truth data. Findings from this study show significant increase in built-up areas at the cost of loss in dense vegetation over a 38-year period thereby, putting pressure on available green spaces. In terms of the area under each LULC category, most significant changes have been observed in built-up area (410.86%), bare surface (- 79.79%), sparse vegetation (- 53.42%), and dense vegetation (- 31.83%). Effective conservation strategies should focus on promoting connectivity between green spaces, engaging stakeholders in the planning and implementation of green infrastructure projects.