Updated Land Research Articles

Quantifying the influence of updated land use/land cover in simulating urban climate: A case study of Metro Manila, Philippines

Quantifying the influence of updated land use/land cover in simulating urban climate: A case study of Metro Manila, Philippines

Improved WRF simulation of surface temperature and urban heat island intensity over Metro Manila, Philippines

Accurate representation of the land use/land cover (LULC) in the Weather Research and Forecasting (WRF) model has been shown to be crucial for improved simulation of surface variables and urban heat island (UHI) phenomenon. In this study, a ground truthed LULC dataset over Metro Manila (MM) was integrated in the WRF model to assess and quantify the relative contributions of using an updated land use/land cover (ULULC), activating the single-layer urban canopy model (WSLUCM) and anthropogenic heat (WAH), and consideration of urban surface heterogeneity (LIR) representation on simulated Surface Temperature (ST) relative to the default model settings (CTRL) of the WRF model. Additionally, the study examines the daytime and nighttime UHI features over MM, using a reference simulation where urban grids were replaced with cropland (CRP). Numerical simulations were carried out for April 2018, a typical dry month without monsoonal influence over MM to get a clear UHI characterization. Data from nine weather stations distributed across MM was used for performance evaluation. Results show negligible daytime biases in ST for CTRL, ULULC, and WAH, and underestimated ST for WSLUCM. At night, CTRL and ULULC (WSLUCM and WAH) tend to overestimate (underestimate) ST. Of the four factors analyzed, updating LULC, using SLUCM, including AH, and accounting for urban surface heterogeneity can change daytime ST by 0.3 °C, -0.6 °C, 0.3 °C, and 0 °C, respectively, and nighttime ST by 0.5 °C, -1.2 °C, 0.3 °C, and 0.4 °C, respectively. Widespread daytime warming and a distinct nocturnal UHI following urbanization were confirmed for MM when the LULC accounted for urban heterogeneity. While data limitations for MM limit the specification of the SLUCM, its UHI features are best represented in WRF when an updated land cover dataset is used.

Blockchain and ML in land registries a transformative alliance

This study presents a novel method for merging blockchain security and machine learning (ML) valuation to update land register systems. The system offers a safe, open, and effective framework for documenting and managing land ownership, addressing issues with conventional land registry procedures. Blockchain technology creates a tamper-proof record by cryptographically combining transactions and time-stamped entries to provide an immutable and decentralized ledger. In addition to building a solid foundation for the land registry system, this strengthens trust. Simultaneously, ML algorithms examine variables such as amenities and location to remove inflated pricing, providing accurate assessments and encouraging openness in the real estate sector. The system has been put into practice and verified in small-scale applications. Its features include enhanced data security, expedited ownership transfers, and accurate asset appraisals. Collaboration between governments, regulatory agencies, and technology suppliers is necessary for widespread deployment. Land registration procedures will change as a result of the revolutionary partnership between blockchain and ML technology, which offers a more effective, safe, and future-ready environment. Accepting this ground-breaking technique establishes a new benchmark for the updating of land ownership data and is a major step toward a more sophisticated and dependable method in the industry.

Evapotranspiration increment was underestimated in China due to underrepresented land cover changes

Numerous evapotranspiration (ET) products have been produced using various approaches and diverse forcing data even as the magnitude and trends of ET show divergence. We simulated ET using updated land use and cover change (LUCC) data in China from 1900 to 2020. We found that China’s ET increased slightly from 1900 to 1980, but it increased rapidly after 1980 due to LUCC characterized by forest expansion (2.05 mm yr−1, P < 0.01). We also found that the ET trends derived from our simulation were significantly higher than other ET products (−0.70–1.47 mm yr−1, P < 0.01), implying that existing, long-term ET products might have underestimated ET trends in China during the post-1980 period because of underrepresented LUCC. These underestimated ET trends could introduce biases in the regional water budget and water resources management. We advocate for future studies to take into account the impacts of LUCC in global ET simulations.

Reconstruction of the Indian Ocean Tsunami in 2004 in Sabang Based on the Current Land Cover for Tsunami Evacuation Sites Recommendations

Sabang City has grown in term of city’s population as well as the tourism activity. The development also meant there are more area that has been used when compared to the time before the 2004 tsunami. This research was developed to re-identify tsunami-prone zones with the current land cover condition in Sabang City and to provide recommended safe locations, alternative evacuation routes, and additional evacuation sites. We used Cornell Multi-grid Coupled Tsunami Model (COMCOT) to carried out the tsunami simulation added with updated land cover to provide more accurate simulation model. The simulation pointed out several tsunami hazard zones in Sabang City, such as Balohan, Kuta Ateuh, and Iboih with expected tsunami heights to be more than 3 meters and arrival time less than 60 minutes. Those areas then surveyed to develop recommendations for tsunami risk reductions. The recommendations included nine additional evacuation buildings are proposed, three sites in each zone. Another recommendation is in form of evacuation routes in each zone to complement existing routes stated in RTRW document.

Leveraging Time-Series Imageries and Open Source Tools for Enhanced Land Cover Classification

The urgency of regularly updated land cover information is crucial for monitoring the dynamics of regional landscapes. Current land cover classification techniques are typically based on snapshots taken on specific dates; therefore, potential variations over time are ignored. This limitation is exacerbated in regions that require land cover classification professionals. To address these challenges, we propose the use of existing innovative and open-source tools. This study presents a comparative analysis of two methods: the first uses machine learning classification using Google Earth Engine and the second uses time-series imagery using R’s sits package. Both methods were tested to classify land cover in Sragen Regency, Central Java, Indonesia, and our results demonstrated the superiority of R’s sits package in terms of accuracy and reproducibility. This facilitated the acquisition of a more precise and comprehensive land cover classification and demonstrated a robust application. The sits package also benefits from being open -source, encouraging wider accessibility and collaboration in the scientific community. The implications of these results underscore the potential of leveraging time-series imagery and open-source tools such as the sits package in R for more accurate and reproducible land cover classification. This approach can significantly improve our ability to monitor and understand the dynamics of regional landscapes, particularly in areas with limited skilled human resources. Future research should refine these techniques and examine their applicability to different geographic contexts.

Impact of Updating Vegetation Information on Land Surface Model Performance

AbstractVegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modeled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover data sets in these models are often outdated. In this study, we update land surface data in the European Centre for Medium‐range Weather Forecast (ECMWF) land surface modeling system (ECLand) using Earth observation‐based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation‐based data sets. Updating the vegetation information does not always yield better model performances because the model's parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation‐related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth‐observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.

Integrating remote sensing temporal trajectory and survey statistics to update land use/land cover maps

ABSTRACTRemote sensing and land resource surveys have been used in recent decades for land use/land cover (LULC) mapping; however, keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging. This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data. This study employed Landsat imagery time series, change detection algorithms, sample migration, and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022. The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products. Additionally, a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps, increasing the correlation coefficient (r) with China’s second and third national land survey statistics from 0.41–0.89 to 0.86–0.99. Thus, the framework and products developed in this study provide valuable tools for sustainable land use and policy planning.

Effect of land surface parameters on meteorology and ozone air quality simulations in the Great Bay Area, China

Land surface parameters play a crucial role in simulations of weather and air quality. This study investigated the impact of underlying surface parameters on meteorological fields and pollutant concentrations in the Greater Bay Area (GBA), China, during warm and cold season. Updated land surface parameters, namely land use type, leaf area index, vegetation fraction, albedo, and roughness length, were incorporated to improve simulations. The model showed 7%–14% improvements (in terms of the index of agreement) in wind speed simulations, whereas temperature and relative humidity were not sensitive to the adoption of updated parameters. Moreover, compared with the model with default parameters, the model with updated natural underlying surface parameters showed a 2–3 m/s reduction in cross-border wind speed during cold season in the GBA. The model also showed a 1–2 m/s increase in the wind speed in urban areas, owing to thermal contrast. During periods in which clean air came from the north, the reduced wind speed hindered ozone removal in the GBA. The use of updated underlying surface parameters enhanced the urban heat island effect by ∼2 K and increased the wind speed over urban areas by ∼2 m/s. The intensified urban heat island circulation generated strong updrafts and intensified sea breezes, promoting the penetration of sea breezes inland. Additionally, the intensified sea breeze and urban heat island circulation contributed to a ∼10-ppb reduction in ozone concentrations. Hence, incorporating the latest land surface parameters improved the simulation of meteorological fields and influenced cross-border pollutant transportation. This study highlights the importance of considering underlying surface characteristics in regional modelling and provides insights for air pollutant management.

Evaluation of site selections for solid waste landfill by using GIS in Phu Quoc island, Kien Giang province, Vietnam.

Selecting the location for domestic waste disposal and waste treatment plant is a crucial issue as it directs impacts on health-related, social and environmental factors. One of the effective tools for solving this problem is Geographic Information System (GIS) which employs Multi-Criteria Analysis (MCA). This research aims to identify the optimal locations to evaluate the existing solid waste landfill site and waste treatment plant for land use planning in Phu Quoc island in Kien Giang province for reducing the plastic waste leaking into the ocean. The island is categorized as the urban at small scale with population of 100,000 to 500,000; construction of waste treatment facilities (middle scale) with the capacity of 500 tons per day is now planned. These contents of this study are the new contributions to the methodology for data processing and data collection. The main research methods were applied to geo-spatial data collection derived from maps, satellite imagery, and existing local land use planning information. The criteria for site selections for solid waste landfill were chosen under Vietnamese construction standards and Ministry of Natural Resources and Environment, Ministry of Construction, international regulation, and the provincial policy. Then Analytic Hierarchy Process (AHP) and GIS processes were based on 9 main factors such as (1) distance to an urban area, (2) distance to a rural village, (3) distance to the water surface, (4) distance to roads, (5) distance to electrical supply stations, (6) distance to industrial zones, (7) distance to waste disposal, (8) compliance with the local land use planning, and (9) agreement for approval for site locations for solid waste landfill from the local community. National standard requirements for waste landfill site selection are adjusted to meet the local context. The updated land use data source is applied, and the recommendation for the suitability of the waste landfill sites is formulated. Study results showed that there are four potential sites located in Cua Can, Bai Thom, and Ham Ninh communes with a total area of 84.5 hectares. These areas are identified to be very suitable for solid waste landfill locations and location of waste treatment plants; and corresponding to the land use planning of the local government. The results of the study also recommend policies and solutions for environmental management in the coastal area of Phu Quoc island.

Impact of different land use data on WRF model short range forecasts during pre-monsoon and monsoon seasons in India

This study focuses to analyse the impact of land use changes on short range weather forecasts over Indian region. Weather Research and Forecasting (WRF) model simulation experiments are conducted by using land use data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Indian Space Research Organization (ISRO) satellites for pre-monsoon and monsoon season. MODIS 2001 land use is used in control (CNT) experiment and updated land use with recent urban class from MODIS (EXP1) and ISRO (EXP2) for the year 2019 is used to generate model lower boundary conditions in other two experiments. Quantitative error measures and skill score computed for rainfall forecast showed that model skill is better with the use of realistic recent land use data from MODIS and ISRO during pre-monsoon and monsoon period. Extreme Dependency Index score computed also revealed that model skill in predicting extreme rare rainfall events is improved with recent landuse data. Model simulated surface meteorological variables and profiles at lower levels also found to be improved with the inclusion of realistic land use class from MODIS and ISRO. Between the two experiments, the one which used ISRO based land use showed larger improvement particularly during the monsoon season.

Impacts of Complex Terrain Features on Local Wind Field and PM2.5 Concentration

Complex topography has nonnegligible effects on local meteorological conditions as well as the transportation of atmospheric pollutants, which deserves more extensive study. In this study, the impacts of complex terrain features (mountains and river valleys) on local wind field and PM2.5 concentration in a typically developed mega city along the Yangtze River were studied numerically using the WRFCALMET-CALPUFF system. The impacts of different model grid and terrain horizontal resolutions were firstly investigated against observations. Then, the impacts of terrain features, specifically the impacts of Mt. LS and the Yangtze River, on wind field and PM2.5 transportation were analyzed by “removing” Mt. LS and the Yangtze River from the meteorological diagnostic model and simulating the dispersion of PM2.5 from three virtual point sources in the chemical model. Results showed that: (i) higher terrain elevation and model horizontal resolutions, and updated land cover types, can effectively improve the prediction of wind direction where terrain features are complex; (ii) Mt. LS mainly acts as a barrier, and ridge wind is weakened after “removing” Mt. LS; (iii) after “removing” the Yangtze River, the transport of PM2.5 along the Yangtze River is weakened; (iv) the simulation of PM2.5 from virtual point sources showed that Mt. LS could have an effect of up to 55% on the PM2.5 concentration in Nanjing. This study showed that the local complex topographies have an obvious effect on the local wind field and the concentration of PM2.5. Therefore, it is important to consider the influence of local topographies and land cover types when predicting local wind field and air quality.

Tsunami evacuation sites in the northern Sumatra (Indonesia) determined based on the updated tsunami numerical simulations

In many parts of tsunami prone areas, providing tsunami evacuation structures is often regarded as costly and difficult to manage, whereas several existing building and hills can be introduced as tsunami evacuation sites. This research intends to introduce practical and scientific methods in assessing the feasibility of hills and buildings for vertical tsunami evacuation facilities. Here, the aim is to combine tsunami numerical simulations and field assessment to determine suitable hills and buildings as vertical tsunami evacuation facilities in Calang and Banda Aceh (Indonesia) that were severely destroyed by the 2004 Indian Ocean tsunami. The numerical simulations were based on the latest condition of land cover of the study area, which has changed significantly in the last 20 years. Using the newly updated land cover, the tsunami model reveals that the tsunami estimated maximum height is 13 m in Calang. The existing buildings and hills, with an altitude higher than tsunami heights, which can be reached within 30 min (15 min less than the minimum tsunami arrival times) from the centres of the villages, are proposed as tsunami evacuation sites. In Banda Aceh and Calang, 10 public buildings, 13 mosques, and 4 hills are proposed as alternative escape sites.

Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model

The goal of this study is to examine the impact of assimilating satellite derived surface temperature over land (LST) in the surface scheme of AROME-France model. The LST is retrieved from SEVIRI radiances during the assimilation process in the atmospheric model. The assimilation of LST is performed using an optimal interpolation technique, similarly to the assimilation of other near-surface parameters (temperature and relative humidity at 2 meters). Observation and background errors were diagnosed before to prescribe them in the surface assimilation scheme. First, this LST assimilation has been evaluated in terms of analysis and forecast quality over a two-month summer period. A positive impact has been found on the assimilation of 2 m temperature and relative humidity with a slight decrease in bias of the background departure. An improvement has also been found for the assimilation of microwave humidity sensitive channels. The assimilation of microwave sensors benefits from an updated land surface temperature through the retrieval of the emissivity. Moreover, the assimilation of SEVIRI LST has improved the nighttime forecasts of temperature and relative humidity near the surface and up to 700 hPa. Several open issues for improving these preliminary results are finally proposed.

Questioning the hazard map-based rebuilding process: learning from the 2018 Sulawesi earthquake in Indonesia

ABSTRACT To reduce hazards in post-disaster rebuilding, governments often first revise existing hazard maps to update land use plans and regulations. This sequence assumes that the disaster event immediately improves knowledge of the hazard. To learn from an actual case, we document PASIGALA’s rebuilding process following the 2018 Central Sulawesi earthquake. We reviewed public documents in-depth, assessed coastal hazards with new information, and reflected on our field observations. We documented the 3.5-year situation in detail and developed recovery narratives. We also found that the actual post-disaster development does not fully incorporate the planned goals of hazard risk reduction. Reasons include: i) the need to create a hazard map before knowing the hazard’s mechanism; ii) the scale of hazard mapping does not correspond to that of individual building parcels; iii) residents, out of necessity, restart their lives in the prohibited areas, and iv) relocation plans do not attract affected residents when rebuilding their lives. Governments may create simplified hazard maps to facilitate timely rebuilding, but this overlooks nuanced problems residents face, further complicating their situation. Although the hazard maps show the region’s potential hazards, the next disaster could be different. We conclude the current practice of hazard map-based rebuilding needs more deliberation.

Impacts of E-mutation: A Study on Service Recipients’ Perception

Land management has gained the utmost importance in the development arena worldwide in recent years. Land-related targets and indicators were incorporated in 5 goals, 8 targets, and 12 indicators of SDG. Different studies have identified the conventional land management system as a reason for inefficient land management in Bangladesh. The government of Bangladesh has taken some initiatives to digitise the land management system. Since 2018, the Land Administration has launched an E-mutation system to update land records. The objective of this research is to investigate the impacts of E-mutation on the service recipients and their perception about it. The research covers 03 circles in Savar Upazila, Union Land Offices, and their service recipients. The target respondents of the study are mutation applicants, Assistant Commissioner (Land), and Union Land Assistant Officers. Both quantitative and qualitative data are used to validate the results of this research. The study reveals that the impact of E mutation on the service recipients is positive, being pleased with the E-mutation system; as it reduces time, cost, and visit (TCV). In addition, it reduces the nuisance by brokers and ensures better service quality. However, nearly four- fifths of the service recipients are dependent to apply for E-mutation due to a lack of knowledge of land-related documents and poor ICT skills. This paper would aid the policymakers to devise further strategies for efficient land management in Bangladesh.

Implementation of climate change mitigation initiatives in land management measures and in the context of strategic environmental assessment

The paper considers the possibilities of implementing climate change mitigation initiatives in environmental land management measures, since land management itself is, by definition, a set of socio-economic and environmental measures. It is proposed to update land management projects providing ecological and economic substantiation of crop rotation and landscaping within the framework of decarbonization as an element of the European Green Deal with calculations of carbon sequestration from the atmosphere, depending on the crops provided for in crop rotations. The implementation of climate change mitigation initiatives is also proposed as part of the strategic environmental assessment of land management documentation in Ukraine, in particular, to assess planning decisions for the Climate and Air Quality criterion in the context of assessing changes in CO₂ release/absorption as one of the greenhouse gases that affect climate change. In the context of The European Green Deal, the article examines approaches to assessing greenhouse gas absorption (decarbonization) which can be implemented in Ukraine both within the framework of strategic environmental assessment of land management documentation and in environmental measures for land management, since the principles of land management include ensuring the priority of environmental safety requirements, protection of land resources and restoration of soil fertility, productivity of agricultural land, and establishment of a regime for environmental protection, health, recreation, and historical and cultural purposes.

Challenges in automatic forest change reporting through land cover mapping

AbstractUp-to-date knowledge about changes in forest resources and their spatial distribution is essential for sustainable forest management. Therefore, monitoring of forest evolution is increasingly demanded by national and international agencies to design forestry policies and to track their progress. Annually updated land cover maps based on open access satellite imagery may serve as a primary tool for monitoring forest surface evolution over time. Spatially detailed information about forest change might be obtained by comparing land cover maps over time. This study aims to better understand the processes underlying pixels whose land cover changes from 1 year’s map to the next and to understand why errors occur. In this study, two annual land cover maps were produced using Sentinel-2 images and afterwards they were compared. The comparison was performed in terms of total surface occupied in each map by each of the classes (net comparison) and in terms of spatial agreement, comparing the results pixel to pixel. The study was performed for the entire region of Galicia (in the Northwest of Spain) for the years 2019 and 2020. Land cover maps obtained had overall accuracies of 82 and 85 per cent. Differences in the total surface of change were encountered when performing the net comparison and spatial agreement comparison. The detailed analysis performed in this study helps to better understand the processes underlying the maps’ discrepancies revealing the processes leading to wrongly identified forest changes. Future studies could aim to integrate this knowledge into the monitoring system to improve the ultimate usability of land cover maps to retrieve information about forest changes.

Application of Sentinel-2 Satellite Data to Map Forest Cover in Southeast Sri Lanka through the Random Forest Classifier

Sentinel-2 satellite data has been used for forest cover monitoring for almost five years. Mapping with Sentinel data will be a cost-effective solution for Sri Lanka, where the lack of updated land cover maps with high spatial resolution is a significant challenge in the land resource management of the country. A study area of about 5,000 km2 located in southeast Sri Lanka was selected for this study. Agricultural lands, forests including Yala national park, and villages with perennial crops make up the region. A Level-2A Sentinel-2 image with less than 10 percent cloud cover was used in the European Space Agency's (ESA) SNAP software version 8.0.0 for image processing and the forest cover of the study area was mapped through the Random Forest classifier (RFC). Normalized Difference Vegetation Index (NDVI) is also calculated as a Sentinel product to support RFC output. For RFC, ground truth data were collected through the reference of Google Earth high-resolution data. The classification accuracy was assessed using the Google Earth image as the reference dataset. Furthermore, RFC results were compared with NVDI greenness values. The classification accuracy was calculated using a confusion matrix (error matrix) through randomly selected 100 sample points. The overall accuracy of the land cover map was 85 percent, with a 96 percent accuracy for forest cover identification. The study found RFC as an effective method to isolate forest cover in Sri Lanka.KEYWORDS: Sentinel-2, Random Forest Classifier, Land cover classification, Land cover mapping, Normalized Difference Vegetation Index.

Coastal-urban meteorology: A sensitivity study using the WRF-urban model

The air quality of a city can be assessed by applying a computational modelling approach. However, one of the most critical issues faced while running air quality models is based on the meteorological model setup, which can significantly affect simulations over a coastal urban area with complex terrain. Thus, this study evaluates the performance of the Weather Research and Forecasting (WRF) model using urban canopy model schemes considering three different model heights (4, 10, and 20 m), and updated land use and cover data (ESA-CCI). The one-year simulation results, based on the observed surface parameters (wind speed and direction, temperature, humidity, and precipitation) over the Metropolitan Region of Vitória (MRV), Southeast of Brazil, showed that the WRF model systematically overpredicted temperature and wind speed, and underestimated humidity, especially during the autumn and winter seasons. However, a significant improvement was obtained using the building effect parameterisation (BEP) formulation to emulate meteorological variables over urban surfaces, especially for wind speed. For temperature and relative humidity, the use of BEP did not show appreciable improvement because the BULK scheme (the control experiment) had a similar performance. The land-use dataset changes showed minimal impact on the representation of urban surfaces; however, significant differences were observed in natural/vegetated areas. The different land covers impacted the surface meteorological parameters, such as temperature and precipitation. However, due to the low coverage of monitoring stations in the countryside area, it was insufficient to determine whether the performance of the WRF model is favoured by the land-use update.