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Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.

The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

The objective of this study is to provide a thorough assessment of soil erosion in the Hirshabelle state from 2020 to 2023, utilizing the Revised Universal Soil Loss Equation (RUSLE) and advanced geospatial technologies, particularly Google Earth Engine, to guide sustainable land management strategies. The study integrates multiple datasets, including CHIRPS for rainfall measurement, MODIS for land use analysis, and a digital elevation model for slope calculation, to offer a comprehensive understanding of the factors contributing to soil erosion. The rainfall erosivity (R) factor is calculated using CHIRPS data, while the soil erodibility (K-factor) is derived from the soil dataset. The topographic condition (LS-factor) is computed using the digital elevation model, and the cover-management (C) and support practice (P) factors are determined from the NDVI and land use data, respectively. The findings reveal considerable spatial variation in soil erosion across the Hirshabelle state. The results are categorized into five levels based on the severity of soil loss: very low (<5), low (5-10), moderate (10-20), high (20-40), and very high (≥40). While areas classified under “very low” soil loss are dominant, indicating relatively stable soils, regions under “very high” soil loss signal potential land degradation and the need for immediate intervention. Furthermore, the study revealed the intricate interplay of slope, vegetation, and land use in influencing soil erosion. Areas with steeper slopes and less vegetation were more susceptible to soil loss, emphasizing the need for targeted soil conservation measures in these regions. The land use factor played a crucial role, with certain land uses contributing more to soil erosion than others.

Paddy fields play very important environmental roles in food security, water resource management, biodiversity conservation, and climate change. Therefore, reliable broad-scale paddy field maps are essential for understanding these issues related to rice and paddy fields. Here, we propose a novel paddy field mapping method that uses Sentinel-1 synthetic aperture radar (SAR) time series that are robust for cloud cover, supplemented by Sentinel-2 optical images that are more reliable than SAR data for extracting irrigated paddy fields. Paddy fields were provisionally specified by using the Sentinel-1 SAR data and a conventional decision tree method. Then, an additional mask using water and vegetation indexes based on Sentinel-2 optical images was overlaid to remove non-paddy field areas. We used the proposed method to develop a paddy field map for Japan in 2018 with a 30 m spatial resolution. The producer’s accuracy of this map (92.4%) for non-paddy reference agricultural fields was much higher than that of a map developed by the conventional method (57.0%) using only Sentinel-1 data. Our proposed method also reproduced paddy field areas at the prefecture scale better than existing paddy field maps developed by a remote sensing approach.

As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.

Soil loss, also known as erosion, is an irreversible natural phenomenon that affects the topsoil of the Earth’s surface. It reduces soil fertility and water availability, and initiates geohazards, leading to negative environmental consequences. A research study was conducted in part of the Musi River sub-basin, a tributary of the Krishna River basin in India, which is undergoing a lot of changes due to anthropogenic factors. The novelty of this study lies in the integration of the RUSLE (Revised Universal Soil Loss Equation) model with advanced Geographical Information System (GIS) techniques to evaluate soil erosion and sediment yield in the basin. Leveraging the capabilities of the Google Earth Engine platform, the study employs the CART (Classification and Regression Trees) machine learning algorithm to generate the LULC (Land Use Land Cover) map, crucial for accurate C factor estimation. This innovative approach improves the precision of erosion modeling by seamlessly integrating GIS, machine learning, and remote sensing technologies. The analysis reveals that the LULC map has a total accuracy of 89.6% and a kappa coefficient of 0.86. The analysis also shows that the agriculture class dominates the research area with 51.4%. The results reveal that 95.6% of the research area has very low soil erosion of 0-1 ton/ha/ year, and 60.8% of the area has low sediment yield of 0-1 ton.ha-1.y-1. As the study area consists of major towns and cities, and the agricultural area is being converted to open plots (barren lands for developmental activities), erosion may increase in the future. The findings of this study may be used by managers and legislators to suggest soil conservation laws to expedite development projects.

Examining the influence of land use/land cover transformation on meteorological variables has become imperative for maintaining long-term climate sustainability. Rapid growth and haphazard expansion have caused the conversion of prime agricultural land into a built-up area. This study used multitemporal Landsat data to analyze land use/land cover (LULC) changes, and Terra Climate monthly data to examine the impact of land transformation on precipitation, minimum and maximum temperature, wind speed, and soil moisture in the Aurangabad district of Maharashtra state in India during 1999–2019. Multiple linear regression and correlation analysis were performed to determine the association among LULC classes and climatic variables. This study revealed rapid urbanization in the study area over the years. The built-up area, water bodies, and barren lands have recorded a steep rise, while the agricultural area has decreased in the district. Drastic changes were observed in the climatic variables over the years. The precipitation and wind speed have shown decreasing trends during the study period. A positive relationship between soil moisture and agricultural land was found through a correlation analysis. Conspicuous findings about the positive relationship between the agricultural land and maximum temperature need further investigation. A multiple linear regression analysis demonstrated a negative relationship between the built-up area and precipitation. The intensity of the precipitation has reduced as a consequence of the developmental activities in the study area. Moreover, a positive relationship was observed between the built-up area and maximum temperature. Thus, this study calls for policy implications to formulate a futuristic land-use plan considering climate change projection in the district.

Forests are an important natural resource and are instrumental in sustaining environmental sustainability. Burning biomass in forests results in greenhouse gas emissions, many of which are long-lived. Precise and consistent broad-scale monitoring of fire intensity is a valuable tool for analyzing climate and ecological changes related to fire. Remote sensing and geographic information systems provide an opportunity to improve current practice’s accuracy and performance. Spectral indices techniques such as normalized burn ratio (NBR) have been used to identify burned areas utilizing satellite data, which aid in distinguishing burnt areas using their standard spectral responses. For this research, we created a split-panel web-based Google Earth Engine app for the geo-visualization of the region severely affected by forest fire using Sentinel 2 weekly composites. Then, we classified the burn severity in areas affected by forest fires in Wolgan Valley, New South Wales, Australia, and the surrounding area through Difference Normalized Burn Ratio (dNBR). The result revealed that the region’s burnt area increased to 6731 sq. km in December. We also assessed the impact of long-term rainfall and land surface temperature (LST) trends over the study region to justify such incidents. We further estimated the effect of such incidents on air quality by analyzing the changes in the column number density of carbon monoxide and nitrogen oxides. The result showed a significant increase of about 272% for Carbon monoxide and 45% for nitrogen oxides. We conclude that, despite fieldwork constraints, the usage of different NBR and web-based application platforms may be highly useful for forest management to consider the propagation of fire regimes.