Land Use Map Research Articles

U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia.

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000-2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000-2019), and year-on-year results were highly correlated (94-97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting.

Analisis Ketersediaan dan Kebutuhan Air Sub DAS Rupit Menggunakan Remote Sensing dan GIS

The water balance of a river basin is crucial for estimating water availability to meet current and future domestic and economic water demand and supply. Parameters such as rainfall, temperature, evapotranspiration, deficit, and surplus soil water content determine the water availability of a river basin (DAS), influenced by factors like land use, soil type, seasonal variations, and other environmental factors. This research aims to estimate the water balance of the Rupit sub-watershed using the FJ Mock model. The Sentinel 1 image of the area was processed to produce a land use map. GIS modeling generated maps of rainfall, land use, soil, slope, morphometry catchment, excess moisture, evapotranspiration, and surface run-off. The analysis reveals that the highest rainfall and evapotranspiration occur in January, while other months average around 56 - 66 mm per month. Excess water during the wet months (November-March) leads to flooding, damaging infrastructure such as residential buildings, roads, and bridges. Conversely, April-October experiences water shortages. The annual weighted watershed deficit is 46.6 mm, with almost no surplus detected. Rainfall run-off simulations demonstrate significant and coherent watershed responses to rainfall patterns. Due to low rainfall and soil moisture levels from preceding months, March exhibits the highest discharge at 15.35 m3/s, with a run-off coefficient of 0.863.

Pemanfaatan Citra Satelit Untuk Mengidentifikasi Perubahan Bentang Lahan

This study explores the use of satellite imagery in identifying and monitoring landscape change in the context of sustainable development and natural resource management. The research method used is literature study, by collecting, reviewing and analyzing various sources of information relevant to the research topic. The results of the analysis show that satellite imagery, especially from the Landsat satellite, plays an important role in mapping land use changes over a long period of time, which is useful for monitoring and analyzing land change. Various techniques and methods, such as manual digitization, use of GIS, and guided classification methods, are used to identify landscape changes. Factors such as sensor technology, image processing, ground truth data, and the complexity of the study environment influence the precision and accuracy of analysis results. The conclusion of this study is that the use of satellite imagery in managing and monitoring landscape change provides significant benefits in the context of sustainable development, but also requires a long-term and inclusive approach to ensure the use of this technology can take place effectively and sustainably.

Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution

We developed a high-resolution machine learning based surrogate model to identify a robust land-use future for Australia which meets multiple UN Sustainable Development Goals. We compared machine learning models with different architectures to pick the best performing model considering the data type, accuracy metrics, ability to handle uncertainty and computational overhead requirement. The surrogate model, called ML-LUTO Spatial, was trained on the Land-Use Trade-Offs (version 1.0) model of Australian agricultural land system sustainability. Using the surrogate model, we generated projections of land-use futures at 1.1 km resolution with 95% classification accuracy, and which far surpassed the computational benchmarks of the original model. This efficiency enabled the generation of numerous SDG-compliant (SDGs 2, 6, 7, 13, 15) future land-use maps on a standard laptop, a task previously dependent upon high-performance computing clusters. Combining these projections, we derived a single, robust land-use future and quantified the uncertainty. Our findings indicate that while agricultural land-use remains dominant in all Australian regions, extensive carbon plantings were identified in Queensland and environmental plantings played a role across the study area, reflecting a growing urgency for offsetting greenhouse gas emissions and the restoration of ecosystems to support biodiversity across Australia to meet the 2050 Sustainable Development Goals.

Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco

Recently, the earth’s climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model’s ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies.

Changes in Land Use and Cover and Their Environmental Impacts in the Cerrado of Mato Grosso Do Sul, Brazil

In Brazilian regional landscapes, the Cerrado has one of the richest flora among the savannas in the world, with a high level of endemism; however, many plant species are threatened with extinction as a consequence of spatio-temporal changes in land use and cover. This study aimed to analyze changes in land use and cover in the upper course of the Ceroula stream basin, located in the Cerrado of Mato Grosso do Sul, Brazil, based on maps of land use and cover in 1985 and 2022, the normalized difference vegetation index (NDVI), precipitation data, and fieldwork. The results indicated that in 1985, forest vegetation was replaced by pasture, and in 2022, in addition to pasture, there was the introduction of soybean monoculture with corn in the off-season, influenced by the international commodities market. These land use and cover alterations, without adequate management and in the absence of conservation practices, led to environmental impacts, such as accelerated linear erosive processes (rill, ravine, and gully). The results may help provide important insights into the dynamics of land use and cover, the consequences of the lack of conservation practices, and the environmental impacts in the Cerrado of Mato Grosso do Sul, contributing to better understanding of the environmental challenges faced in the region and the need to provide subsidies for the development of sustainable management strategies.

Detection of Urban Growth in Taiz City, Yemen, Between 1981 and 2022 by Using Google Earth™ and Geographical Information System Data

Rapid urbanization brought serious socio-economic and environmental problems in many cities around the world, particularly in developing countries like Yemen. Urban "sprawl", a synonym of rapid and unplanned urban growth, is considered an obstacle for sustainable development, which causes serious long- and short-term problems, such as extreme infrastructure expenses, economic inefficiency, environmental destruction, and social instability. This is the case of Taiz City, the third largest city in Yemen, which is facing a huge challenge to manage the spatial extension of its built-up area at the expense of the reduction of natural resources and environment. This study aims to investigate urban growth process in Taiz City, focusing on urban sprawl and agricultural land-use change during 1981–2022. The data used are high resolution geospatial data, which are the topographic map for 1981 and Google Earth (GE) images for 2003, 2012, and 2022. The methodology adopted was manual digitizing using Geographic Information System (GIS) software to extract urban land use features from the images of the different dates. Four land-use maps were produced and used for calculation the urban sprawl and land-use changes. The urban expansion rate and patterns were also identified in three phases: 1981–2003, 2003–2012, and 2012–2022. The results show that the built-up area increased six times from 6.2 km2 in 1981 to 34 km2 in 2022, while the agriculture area decreased by 77% from 18 km2 in 1981 to 4.2 km2 in 2022. The urban sprawl in Taiz covered about 75% of its municipal area and extended to the agricultural and rural areas. The sprawl index (USI) is measured to be 5.4%, indicating a high degree of sprawl in Taiz. There is no open space left for future development, and the water resources are at risk of population and depletion. The study demonstrated that informal and haphazard expansion must be controlled, a development strategy should be prepared, so that sustainable urban growth can be achieved. The results of this study could be used as a decision support tool for urban management activities. The vector layers produced by this study could provide the required input data for the future urban modeling of the city.

Land Use-Land Cover Changes and Associated Drivers: A Case of Salooni Block of Chamba

Land Use-Land Cover (LULC) changes are the anthropogenic alteration of the natural landscape. The LULC changes have been generated by a variety of factors, including natural, demographic, and economic ones, which put pressure on the land and had a variety of environmental effects. The transformation in Landscape is affecting the livelihoods of people in numerous ways. Therefore, it is crucial to understand the patterns in land use in order to determine how land should be developed, managed, and planned in order to understand the way it will be utilised in the future. In the present study, Salooni Block of Chamba district, Himachal Pradesh was subjected to a first-ever attempt at land-use change detection for the years 2002 and 2022. Satellite imageries of Landsat-7 Enhanced Thematic Mapper (ETM+) Landsat-8 Operational Land Imager (OLI) were used for landuse maps of 2002 and 2022 respectively. The classification was completed using a supervised classification technique and the USGS Land use Classification Scheme at Level I classes, including Agricultural Land, Barren Land, Built-up, Forest, Pasture Land, Snow Cover and Waterbody. Furthermore, fifty people aged 50 and above were interviewed to understand the local factors responsible for LU/LC change. The result indicates a rise in the forest cover, which now accounts for 48.2% of the total area. Since 2002, there has been a 2.3-fold rise in built-up land, which is a glaring indication of the pressure on land. The total area of agricultural and pasture land is decreasing, while the percentage of barren land has increased by 2%. To preserve the sustainability and food security of the ecologically vulnerable environment, stringent land use planning needs to be enforced in the mountainous region. The results of the LU/LC dynamics discussed above can be used to establish policies and strategies for restoring and enhancing the sustainability of the environment as well as the carrying capacity of the land.

Participatory mapping in exploratory scenario planning: Necessity or luxury?

Exploratory scenarios based on participatory approaches proved helpful in dealing with uncertainty and complexity in urban and transport systems. Such scenarios usually create coherent and manageable narratives that capture broad patterns and trends at global scales. However, significant difficulties exist in bridging exploratory scenario narratives with spatial and local realities, which is crucial for guiding policymaking in urban, regional, and mobility planning. This paper explores whether and how exploratory scenario narratives and participatory mapping processes can establish a reciprocal relationship, wherein narrative-based maps contribute to spatially shaping scenario narratives. It uses an experimental approach involving three participatory workshops with experts and local stakeholders specifically tailored to the mapping of land use and transportation-related scenario narratives for the year 2050 in the Henares Corridor (Madrid, Spain). The spatial transformations mapped during workshops have indicated that participatory mapping processes have an impact on scenario narratives in different ways, enabling the identification of three categories of mapping episodes with different meanings: (i) complementing narratives, (ii) expanding narratives, and (iii) modifying narratives. The paper closes by examining the implications of the obtained findings for policymaking, including a reflection on the research’s limitations and potential for application in real-world planning processes.

Spatial Transformation Analysis in Menganti Subdistrict: A Study of Peri-Urban Area Typologies in the Face of Urban Expansion

The swift urbanization of Indonesian cities, notably Surabaya, has spurred population growth and a heightened demand for land, leading to the expansion of urban areas into peri-urban regions like Menganti District. This district, bordering Surabaya, experiences significant land-use change shaped by the city’s expansion, making it a compelling area for analysis due to its complex dynamics. This study aims to analyze Menganti’s peri-urban spatial transformation and its typology based on the land-use triangle continuum theory. Using indicators like land-use proportions, building density, and educational and healthcare facility distribution, the analysis delineates four zones: Urban fringe, Urban-Rural fringe, Rural-Urban fringe, and Rural-fringe zones. The analysis further involves comparing land-use maps from 2017 and 2022, revealing that Urban fringe and Rural fringe zones primarily witness residential land use growth, while Urban-Rural fringe and Rural-Urban fringe display a mix of residential, industrial, commercial, and warehouse land use growth. Analyzing factors influencing spatial transformation in Menganti’s peri-urban area through Delphi and AHP techniques underscores elements like trend of land-use change, road networks, trade facilities, distance from the district center and Surabaya city, and land prices as pivotal influences. Understanding these dynamics aids in comprehending the evolving landscape and guiding sustainable development strategies in peri-urban regions experiencing urban expansion.

Application of land-use change model for supporting land-use planning in An Bien district, Kien Giang province, Vietnam

Forecasting land-use change is a critical step in assisting land-use planning, especially for coastal areas that are influenced by many economic and natural environmental factors. This study aims to estimate land use changes based for An Bien district, Mekong Delta based on multi-criteria assessment of factors affecting land use change. The model has been calibrated with the 2015 land-use map and verified with the 2020 map. The results of land-use change analysis in An Bien district were carried out according two scenarios: (i) forecasting the area under present conditions and (ii) forecasting land use area in the case of economic benefits and suitable environmental conditions for aquaculture production. Scenario 1 showed that the results will not change much compared to the current situation, in which rice-shrimp plays a leading role locality, however Scenario 2 showed that shrimp area will rapidly rise. In general, the simulation results allow users to explore future planning possibilities and serve as a basis for selecting appropriate land-use types.

Intelligent technologies and their transformative role in modern agriculture: A comparative approach

The escalating global demand for food, propelled by a burgeoning population and the unpredictable shifts in climatic conditions, presents a challenge that traditional plant breeding alone struggles to address. In response to this pressing need, the infusion of intelligent technologies emerges as a pivotal solution, poised not only to boost production but also to meet the burgeoning demand. This transformative approach encompasses a spectrum of cutting-edge tools, including Remote Sensing and GIS, Aeroponics, Drone Technology, Biotechnology, Artificial Intelligence, Machine Learning, and, ultimately, Robotics. The synergistic integration of these technologies will enhance agricultural monitoring by facilitating precise crop surveillance, early detection and mitigation of diseases and pests, optimization of water resources, accurate mapping of land use and crop types, comprehensive environmental monitoring, real-time weather and climate tracking, efficient nutrient management, precise irrigation and spraying practices, reliable yield prediction, advanced demand forecasting, genetic analysis, and informed decision-making processes. The amalgamation of intelligent technologies with modern plant breeding methodologies signifies a significant advancement towards achieving more efficient and sustainable agricultural practices. This convergence not only addresses the immediate need for increased food production but also sets the stage for a resilient and future-ready agricultural landscape. In this era of integration, we witness the harmonious coexistence of tradition and innovation, paving the way for a more abundant and secure agricultural future.

Utilization of Remote Sensing and Geographic Information System to Identify Buffer Zones Area at Plumpang Fuel Depot

Utilization of Remote Sensing and Geographic Information System to Identify Buffer Zones Area at Plumpang Fuel Depot

Flood Risk Assessment of the Middle Reach of the Helmand River, Afghanistan

The middle reach of the Helmand River is prone to floods, and flood hazard analysis is necessary to manage the flood risk. As the study site is a less-developed basin where available geographical and hydrological data are limited, we must utilize available limited data to conduct the flood hazard analysis. A frequency analysis of the annual peak flow rate was performed using available hydrological data for making representative flood hydrographs. A land-use map is derived by classifying recent Landsat images. Digital elevation model derived by Advanced Land Observing Satellite, roughness coefficient correlated with the land-use map, and the representative flood hydrographs are fed into hydraulic model HEC-RAS 2D. Flood inundation area, depth, and velocity for different flood return periods are estimated. Based on local hazard intensity and vulnerability, risk maps for evacuation and vulnerability of crops on the floodplain are obtained. Spatial flood risk characteristics differ at the upstream and downstream reaches because of distinct floodplain topography. Flood countermeasures for each reach are proposed based on the spatial characteristics of the flood risk. To provide risk information for residents within the region, the flood hazard, risk, and damage maps derived in this study are published as a web map application and a mobile native application.

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

Growing coastal tourism: Can biomonitoring provide insights into the health of coastal ecosystems?

Coastal tourism's surge raises concerns for Morocco's Agadir marine environment, notably with Taghazout Bay's impact. Our study assesses Taghazout's health, employing a comprehensive approach. Our study evaluates Taghazout's health, adopting a comprehensive approach covering physicochemical, microbiological aspects, macrobenthic fauna, metal pollution, and biomarkers in D. trunculus mollusks. Seawater quality aligns with Moroccan standards, indicating good bathing water. The intertidal zone hosts ten species, dominated by D. trunculus. Biomarker responses in D. trunculus suggest chemical stress. Land-use maps expose significant changes driven by the Taghazout Bay project, impacting approximately 37.99 % of the landscape. Construction activities notably encroached upon the Arganeraie and the coastal zone, creating a stark contrast from 2003. These findings form a crucial database for future studies, contributing significantly to environmental management and sustainable development, aiding informed decision-making and effective coastal ecosystem preservation strategies.

Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes

Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variation in pest ecology, but also from the widespread use of categorical land-use maps that do not adequately characterize habitat-availability for pests. We used 1163 field-year observations of Lygus hesperus (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely-sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because L. hesperus often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that L. hesperus densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.

Using mixed-method analytical historical ecology to map land use and land cover change for ecocultural restoration in the Klamath River Basin (Northern California)

Ecocultural restoration involves the reciprocal repair of ecosystems and revitalization of cultural practices to enhance their mutual resilience to natural and anthropogenic disturbances and climate change stressors. Resilient ecocultural systems are adapted to retain structure and function in the face of disturbances that remain within historical ranges of severity. To assist in ecocultural restoration and management, understanding how a system has historically responded to different types of disturbances is therefore invaluable in understanding how social-ecological resilience can be maintained in the face of future stressors and disturbances. However, records of disturbances and ecocultural responses can be limited for certain landscapes and human communities. In this methods paper, we demonstrate a mixed-method process for integrating oral history, field-based knowledge, archival information, and historical and contemporary aerial images to gain insight into the changes on the Klamath River in Northern California from the 1940s through 2020. We georegistered historical imagery, quantified changes between land cover classes, and contextualized these classifications with qualitative assessments of changes in larger surrounding areas. By synthesizing these data sources with field measurements, mining and other land survey maps, timber management plans, fire and flood histories, and interviews with members of the Karuk Tribe, we were able to reconstruct the land use and land cover change histories at five sites. We noted that recovery of canopy cover from fire and logging practices was faster than for flood, which was faster than recovery from mining, consistent with the relative severity of likely soil disturbance. By combining different sources of information with complementary strengths, we were able to provide managers with site-specific information on recovery from different types of disturbance. Though this approach was labor-intensive, with emerging tools for supervised classification of high-resolution imagery, mixed-method analytical historical ecology could be applied more broadly, supporting ecocultural restoration on a larger scale.

Estimation of greenhouse gas emissions from rice and annual upland crops in Red River Delta of Vietnam using the denitrification–decomposition model

Abstract This study used the denitrification–decomposition (DNDC) model and ArcGIS 10.1 to calculate and quantify the greenhouse gas (GHG) potential from rice and annual upland crops in the Red River Delta of Vietnam. GHG emissions were monitored, analyzed, and calculated at experimental sites. The operating mechanism, sensitivity analysis of the parameters, calibration, and verification of the DNDC model for the GHG emission calculation were studied and performed, and a set of parameters was built. A good correlation between actual and simulated values was shown. From the data set of meteorological stations in and around the Red River Delta, the current land-use map, the topographic and soil map, a complex map of meteorology–soil–land use was built. Each unit of this map contains complete information about climate, soil, and crops as input data for modeling GHG emissions from crop production. From the spatial analysis and collected input data, GHG emissions were measured and calculated for the cultivated field of the Red River Delta (annual rice and upland crops) using the DNDC model. The model's outputs were used to build thematic maps on the distribution of global warming potential (CH4, N2O) for each unit of the complex map of climate, soil, and crops.

Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure

Smallholder farms are major contributors to agricultural production, food security, and socio-economic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lower-cost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.