Annual Land Cover Research Articles

An annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022

We present detailed annual land cover maps for the Baltic Sea region, spanning more than two decades (2000–2022). The maps provide information on eighteen land cover (LC) classes, including eight general LC types, eight major crop types and grassland, and two peat bog-related classes. Our maps represent the first homogenized annual dataset for the region and address gaps in current land use and land cover products, such as a lack of detail on crop sequences and peat bog exploitation. To create the maps, we used annual multi-temporal remote sensing data combined with a data encoding structure and deep learning classification. We obtained the training data from publicly available open datasets. The maps were validated using independent field survey data from the Land Use/Cover Area Frame Survey (LUCAS) and expert annotations from high-resolution imagery. The quantitative and qualitative results of the maps provide a reliable data source for monitoring agricultural transformations, peat bog exploitation, and restoration activities in the Baltic Sea region and its surrounding countries.

India's flood risk assessment and mapping with multi-criteria decision analysis and GIS integration

ABSTRACT India's diverse geography poses significant flood risks, addressed in this study through the geographic information system and multi-criteria decision analysis. This comprehensive flood risk assessment considers seven parameters: mean annual precipitation, elevation, slope, drainage density (DD), land use and land cover, proximity to roads, and distance to rivers. The findings indicate that flood vulnerability is primarily influenced by rainfall, elevation, and slope, with DD, land use, and proximity to roads and rivers also playing crucial roles. Experts weighed these factors to create a thorough flood risk map using the normalized rank index and normalized weight index, categorizing areas into five risk levels: very high, high, moderate, low, and very low. The study reveals that 3.40% of the area is at very high risk, 32.65% at high risk, 39.72% at moderate risk, 20.97% at low risk, and 3.25% at very low risk. These results highlight how human and natural factors interact to influence flood risk, with vulnerable areas characterized by low elevations, steep slopes, high drainage densities, and proximity to rivers or roads. The findings provide valuable insights for policymakers, scientists, and local authorities to develop strategies to mitigate flood losses across India's varied landscapes.

Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved.

Mapping coastal wetland changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat time series and national wetland inventories

The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing reflectance with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The algorithm uses data and tools within the Google Earth Engine platform, making it accessible and scalable. The average overall accuracy is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsatderived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the new products’ severe change magnitude matches that of the existing US inventory and the moderate change magnitude matches that of the Northeastern Coast product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.

Assessment of land restoration from optical satellite image time-series

Abstract. Ecosystem conservation and restoration is recognized by the international community as a key strategy for human well-being as well as our planet’s health. Assessing the efficiency of actions implemented in this context is essential, as their application on the ground may prove challenging. In the present paper, we present a methodology for characterising vegetation regeneration. This methodology has been developed and tested at the scale of the Sao Paulo state. It is based on two successive steps. Firstly, annual land cover maps are produced with breakpoints detection and Random Forest classification. This process is performed on biophysical variables derived from more than 10 000 optical images (2016 – 2021 included) and 4774 reference data. Secondly, we applied an expert-based rules algorithm to derive land regeneration map from annual land cover time series. The distinction is made between natural and anthropogenic regeneration. The accuracy assessment shows an overall accuracy of more than 80% for both the annual land cover maps and the regeneration map.

Anthropogenic threats in the border zone of the Amazon Forest: a case study of land use and land cover in RESEX Ciriaco, Maranhão, Brazil

The state of Maranhão is located in northeastern Brazil and covers three biomes, the Amazon Forest, Cerrado (savannah), and Caatinga (semi-arid), and an endemic ecosystem known as the Mata de Cocais (coconut forest). This study aimed to identify the land use and land cover (LU/LC) in RESEX Ciriaco, located in the border zone of the Amazon Forest of Maranhão, where occurs the Mata de Cocais. The database used in this study was obtained from the Brazilian Annual Land Use and Land Cover Mapping Project (Mapbiomas). The LU/LC in RESEX Ciriaco was carried out across 8,106.7 ha and a buffer zone with a 3 km radius was adopted. A map representing the class changes in the study area was created for the years 1985, 1992, 1999, 2006, 2013, 2020, and 2021. Despite the findings indicate a troubling particularly driven by the presence of pasturelands, the results demonstrate that RESEX Ciriaco has fulfilled its function of guaranteeing the sustainable use of natural resources without deforestation. However, the advancement of livestock and agriculture around RESEX Ciriaco poses a risk to the ecosystem. Creating a buffer zone for RESEX Ciriaco is vital for stopping the advancement of anthropogenic activities in the conservation unit.

The neglect of the change in inundation area leads to overestimation of carbon emission in cascade reservoirs

Reservoir carbon emissions reflect greenhouse gases emitted by flooded land post-construction. However, pre-construction flooded land has been overlooked in previous assessments. Utilizing annual land cover data from 1990 to 2022 and pertinent parameters of cascade reservoirs in the Lancang River (LCR), we calculated the actual flooded areas of these reservoirs. Subsequently, the Tier1 model was employed to estimate the carbon emissions during the reservoir's life cycle and the annual carbon emissions from newly flooded land during construction. Our findings indicate that the LCR cascade reservoir's carbon emission throughout its life cycle is 4.324Tg CO2eq (1.818–8.879Tg CO2eq). When compared with previous results, our estimated figures (0.496–2.106 g CO2eq/(kw·h)) fall below the global hydroelectric carbon footprint's average threshold range (IPCC: 4–14 g CO2eq/(kW·h)). This implies that the previously estimated carbon emissions from the reservoir may be inflated due to the flooded land prior to reservoir construction. Notably, the nutrient state of the water body predominantly governs reservoir carbon emissions. This research sheds light on the intricacies of carbon emissions from cascade reservoirs and underscores the importance of accurately delineating reservoir boundaries and managing nutrient inputs to mitigate carbon emissions.

Remote Sensing Mapping and Analysis of Spatiotemporal Patterns of Land Use and Cover Change in the Helong Region of the Loess Plateau Region (1986–2020)

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management.

Big Data-driven MLOps workflow for annual high-resolution land cover classification models

Developing an annual and global high-resolution land cover map is one of the most ambitious tasks in remote sensing, with increasing importance due to the continual rise in validated data and satellite imagery. The success of land cover classification models largely hinges on the data quality, coupled with the application of Big Data techniques and distributed computing. This is essential for efficiently processing the extensive volume of available satellite data. However, maintaining the lifecycle of several annual Machine Learning models presents a complex challenge. The rise of Machine Learning Operations offers an opportunity to automate the maintenance of these models, a feature particularly beneficial in systems that require generating new models each year alongside the continuous integration of validated data. This article details the development of an end-to-end MLOps workflow, meticulously integrating land cover classification models that employ Big Data strategies for processing large-scale, high-resolution spatial data. The workflow is designed within a Kubernetes environment, achieving on-demand auto-scaling, distributed computing, and load balancing. This integration demonstrates the practicality and efficiency of managing and deploying models that treat satellite imagery in an automated, scalable framework, thus marking a significant advancement in remote sensing and MLOps.

Fire is associated with forest degradation and economic land concessions, but not land conversion in the rapidly transforming Cambodian landscape

Abstract Cambodia is beset by high deforestation rates and fire frequencies. From 2001–2020, forest cover declined by 34%, and cropland and rubber expanded by 31,401 km2 and 4,530 km2, respectively; meanwhile nearly 8.7 million fires burned 41.6% of the land. Fires may be associated with deliberate land change, stable land management systems, and unplanned forest degradation. We integrated annual land cover and monthly burned area data from 2001–2020 to test three hypotheses. First, if fire is used during land conversion, there would be a detectable pulse in fire activity in the year immediately prior to, and the year of land cover change. Our temporal analysis found statistically elevated burn rates in five of 16 transition tests, however, they were typically <1% outside the predicted range, indicating no practical significance in the association of fire with land cover change. Second, fire prevalence would be lower in land managed for conservation or agricultural production than unclassified or unmanaged land. persistent forest and shrubland burned at significantly higher rates than expected, cropland burned significantly less, burning was almost absent from rubber plantations, and forests in protected areas (PAs) exhibited lower total burn percentages than unclassified land or industrial economic land concessions (ELCs), thus supporting the fire-land management hypothesis. However, total burned area in ELCs was greater than unclassified land for several persistent land cover classes, suggesting that local landowners may prevent fire in commercially valuable private agricultural land, but there may be no such prevention measures in industrial ELCs. Anti-fire policy enforcement in ELCs should be enhanced. Third, forests that converted into (degraded) shrublands would experience increasing fire frequency in the years leading up to transition, a hypothesis we found strong support for through our temporal analysis. Cambodia’s 2013 Law on Fire Prevention and Fire Fighting deputizes the public to enforce its provision, however given that the vast majority of fires occur in persistent forest in state-owned land, collective action problems are likely to hinder its effective implementation.

Using Radiometric and Categorical Change to Create High-Accuracy Maps of Historical Land Cover Change in Watersheds of the Great Lakes Basin

Great Lakes Basin landscapes are undergoing rapid land cover and land use (LCLU) change. The goal for this study was to identify changes in land cover occurring in the Great Lakes Basin over three time periods to provide insights into historical land cover changes occurring on a bi-national watershed scale. To quantify potential impacts of anthropogenic changes on important yet vulnerable Great Lakes Wetland ecosystems, the historical changes in land cover over time are assessed via remote sensing. The goal is to better understand legacy effects on current conditions, including wetland gain and loss and the impacts of upland ecosystems on wetland health and water quality. Three key time periods with respect to Great Lakes water level changes and coastal wetland plant invasions were mapped using Landsat-derived land cover maps: 1985, 1995, and 2010. To address change between the three time periods of interest, we incorporate both radiometric and categorical change analysis and open-source tools available for assessing time series data including LandTrendr and TimeSync. Results include maps of annual land cover transition from 1985 to 1995 and 1995 to 2010 basin-wide and by ecoregion and an assessment of the magnitude and direction of change by land cover type. Basin-wide validated change results show approximately 776,854 ha of land changed from c.1980–1995 and approximately 998,400 ha of land changed from c.1995–2010. Both time periods displayed large net decreases in both deciduous forest and agricultural land and net increases in suburban cover. Change by ecoregion is reviewed in this study with many of the change types in central plains showing change in and out of agriculture and suburban land covers, the mixed wood plain ecoregion consisted of a mixture of agricultural, suburban, and forestry changes, and all top five change types in the mixed wood shield consisted of various stages of the forestry cycle for both time periods. In comparison with previous LCLU change studies, overall change products showed similar trends. The discussion reviews why, while most changes had accuracies better than 84%, accuracies found for change from urban to other classes and from other classes to agriculture were lower due to unique aspects of change in these classes which are not relevant for most change analyses applications. The study found a consistent loss in the deciduous forest area for much of the time studied, which is shown to influence the aquatic nitrogen implicated in the expansion of the invasive plant Phragmites australis in the Great Lakes Basin. This underscores the importance of LCLU maps, which allow for the quantification of historical land change in the watersheds of the Great Lakes where invasive species are expanding.

Environmental drivers behind the exceptional increase in cyanobacterial blooms in Okavango Delta, Botswana

The Okavango Delta region in Botswana experienced exceptionally intense landscape-wide cyanobacterial harmful algal blooms (CyanoHABs) in 2020. In this study, the drivers behind CyanoHABs were determined from thirteen independent environmental variables, including vegetation indices, climate and meteorological parameters, and landscape variables. Annual Land Use Land Cover (LULC) maps were created from 2017 to 2020, with ∼89% accuracy to compute landscape variables such as LULC change. Generalized Additive Models (GAM) and Structural Equation Models (SEM) were used to determine the most important drivers behind the CyanoHABs. Normalized Difference Chlorophyll Index (NDCI) and Green Line Height (GLH) algorithms served as proxies for chlorophyll-a (green algae) and phycocyanin (cyanobacteria) concentrations. GAM models showed that seven out of the thirteen variables explained 89.9% of the variance for GLH. The models showcased that climate variables, including monthly precipitation (8.8%) and Palmer Severity Drought Index- PDSI (3.2%), along with landscape variables such as changes in Wetlands area (7.5%), and Normalized Difference Vegetation Index (NDVI) (5.4%) were the determining drivers behind the increased cyanobacterial activity within the Delta. Both PDSI and NDVI showed negative correlations with GLH, indicating that increased drought conditions could have led to large increases in toxic CyanoHAB activity within the region. This study provides new information about environmental drivers which can help monitor and predict regions at risk of future severe CyanoHABs outbreaks in the Okavango Delta, Botswana, and other similar data-scarce and ecologically sensitive areas in Africa.Plain Language Summary: The waters of the Okavango Delta in Northern Botswana experienced an exceptional increase in toxic cyanobacterial activity in recent years. Cyanobacterial blooms have been shown to affect local communities and wildlife in the past. To determine the drivers behind this increased bloom activity, we analyzed the effects of thirteen independent environmental variables using two different statistical models. Within this research, we focused on vegetation indices, meteorological, and landscape variables, as previous studies have shown their effect on cyanobacterial activity in other parts of the world. While driver determination for cyanobacteria has been done before, the environmental conditions most important for cyanobacterial growth can be specific to the geographic setting of a study site. The statistical analysis indicated that the increases in cyanobacterial bloom activity within the region were mainly driven by persistent drier conditions. To our knowledge, this is the first study to determine the driving factors behind cyanobacterial activity in this region of the world. Our findings will help to predict and monitor areas at risk of future severe cyanobacterial blooms in the Okavango Delta and other similar African ecosystems.

Land Cover Disaggregated Fire Occurrence and Particulate Matter2.5 Relationship in the Mekong Region: A Comprehensive Study

Air pollution has become an increasing concern in the Mekong region due to seasonal vegetative burning triggered by related anthropogenic activities and climate change. While the assumption of a correlation between agriculture burning and air pollution is a common postulation, little evidence exists on the association between fire incidents and air pollution concentrations. The current study explores the relationship between satellite-derived fire occurrence, land surface characteristics, and particulate matter 2.5 (PM2.5) concentrations for the five Lower Mekong countries, namely Cambodia, Laos, Myanmar, Thailand, and Vietnam, in an effort to gain new insights into fire distributions related to air quality. Publicly available daily active fire hotspots from the VIIRS satellite instrument, annual land cover products from the MODIS satellite, and mean monthly ground-level PM2.5 estimates from the V5.GL.04 database were analyzed in two relational assessments; first, the distribution of VIIRS active fire counts and fire radiative power (FRP) temporally and spatially and secondly, the correlations between the monthly VIIRS active fire counts, cumulative monthly FRP and mean monthly PM2.5 estimates per country and land cover type. The results suggest a statistically significant positive correlation between monthly fire counts, cumulative FRP, and PM2.5 estimates for each country, which differ based on land cover. The strongest correlation between monthly fire incidences and PM2.5 estimates was found in the case of Myanmar. For all countries combined, fires detected in forests displayed the highest correlation with monthly PM2.5 estimates. This study demonstrates the use of the VIIRS active fire product and provides important insights into temporal and spatial fire distributions as baseline information for fire prevention and mitigation strategies in the Mekong region.

Trend Analysis of MODIS Land Surface Temperature and Land Cover in Central Italy

Land Surface Temperature (LST) is an important climate factor for understanding the relationship between the land surface and atmosphere. Furthermore, LST is linked to soil moisture and evapotranspiration, which can potentially alter the severity and regime of wildfires, landslide-triggering precipitation thresholds, and others. In this paper, the monthly daytime and nighttime LST products of Moderate Resolution Imaging Spectroradiometer (MODIS) are employed for the period 2000–2023 in order to find areas that have been cooling or warming in a region of great interest in Central Italy, due to its complex geological and geomorphological settings and its recent seismic sequences and landslide events. The annual MODIS land cover images for 2001–2022 are also utilized to investigate the interconnection between LST and land cover change. The results of the non-parametric Mann–Kendall trend test and its associated Sen’s slope reveal a significant nighttime warming trend in the region, particularly in July, linked to forest and woodland expansion. Grasslands toward the coastline with low elevation (less than 500 m a.s.l.) have experienced significant heat waves during the summer, with an LST of more than 35 °C. A significant negative correlation between the elevation and LST is observed for each calendar month. In particular, the daytime and nighttime LST have more than 80% correlation with elevation during winter and summer, respectively. In addition, nighttime warming and gradual drainage are noticed in Lake Campotosto. The results of this study could be useful for wildfire and landslide susceptibility analyses and hazard management.

Remotely Sensed Changes in Qinghai–Tibet Plateau Wetland Ecosystems and Their Response to Drought

Wetland ecosystems in the Qinghai–Tibet Plateau are pivotal for global ecology and regional sustainability. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai–Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest (RF) method for annual long-term land cover classification. Standardized precipitation evapotranspiration indices (SPEI3, SPEI6, SPEI9, and SPEI12) on different time scales were used to assess meteorological drought conditions. We employed a Pearson correlation analysis to examine the relationship between wetland changes and various SPEI scales. The BFASAT method was used to evaluate the impact of SPEI12 trends on the wetlands, while a cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our conclusion is as follows: The wetlands, including lake, glacier, and marsh wetlands, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990–1997, 1998–2007, and 2008–2020), featuring extensive conversions between wetlands and other types, notably the conversion from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. There were significant negative correlations between the SPEI at various scales and the total wetland area and types. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, and WP) exhibited varying degrees of resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai–Tibet Plateau.

Multi-decadal floodplain classification and trend analysis in the Upper Columbia River valley, British Columbia

Abstract. Floodplain wetland ecosystems experience significant seasonal water fluctuation over the year, resulting in a dynamic hydroperiod, with a range of vegetation community responses. This paper assesses trends and changes in land cover and hydroclimatological variables, including air temperature, river discharge, and water level in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada. A land cover classification time series from 1984 to 2022 was generated from the Landsat image archive using a random forest algorithm. Peak river flow timing, duration, and anomalies were examined to evaluate temporal coincidence with observed land cover trends. The land cover classifier used to segment changes in wetland area and open water performed well (kappa of 0.82). Over the last 4 decades, observed river discharge and air temperature have increased, precipitation has decreased, the timing of peak flow is earlier, and the flow duration has been reduced. The frequency of both high-discharge events and dry years have increased, indicating a shift towards more extreme floodplain flow behavior. These hydrometeorological changes are associated with a shift in the timing of snowmelt, from April to mid-May, and with seasonal changes in the vegetative communities over the 39-year period. Thus, woody shrubs (+6 % to +12 %) have expanded as they gradually replaced marsh and wet-meadow land covers with a reduction in open-water area. This suggests that increasing temperatures have already impacted the regional hydrology, wetland hydroperiod, and floodplain land cover in the Upper Columbia River valley. Overall, there is substantial variation in seasonal and annual land cover, reflecting the dynamic nature of floodplain wetlands, but the results show that the wetlands are drying out with increasing areas of woody/shrub habitat and loss of aquatic habitat. The results suggest that floodplain wetlands, particularly marsh and open-water habitats, are vulnerable to climatic and hydrological changes that could further reduce their areal extent in the future.

MULTISOURCE AND MULTITEMPORAL LAND COVER MAPPING OF GREATER LUZON ISLAND USING GOOGLE EARTH ENGINE

Abstract. A variety of research endeavors and practical applications necessitate the use of land cover maps. These maps are valuable for tasks such as change detection, forest monitoring, urban expansion monitoring, natural resource mapping, catering to diverse user requirements. While satellite sensors offer essential data for comprehending spatial and temporal variations in land cover, relying on a single satellite system can be limiting, especially considering the potential hindrance of cloud cover in the case of optical sensors. To enhance temporal frequency, it becomes essential to utilize multiple satellite systems, albeit requiring harmonization to ensure consistent outcomes. This study presents a large-scale annual land cover mapping which utilizes harmonized Landsat-8 and Sentinel-2 satellite imagery, in conjunction with supplementary data, and a machine learning algorithm. In addition, the use of powerful computational processing platforms such as Google Earth Engine and Google Colaboratory is now a requirement to manage big geospatial data as well as to run different algorithms for processing and analysis.

Urbanization exacerbated the rapid growth of summer cooling demands in China from 1980 to 2023

Annual land cover data and daily air temperature data in China from 1980 to 2023 were obtained to attribute the urbanization effect on cooling degree days (CDDs). Significant growth in both extreme daily CDDs and total summer CDDs was observed throughout China, excluding the Tibet Plateau. Urban stations exhibited a substantial increase, with extreme daily CDDs and total summer CDDs rising at rates of 0.46 °C·10 yr⁻¹ and 54.32 °C·10 yr⁻¹, respectively, which were ∼1.5 times higher than those observed at rural stations. The mean trends of extreme daily ΔCDDs and total summer ΔCDDs across 218 urban-rural station pairs were 0.19 °C·10 yr⁻¹, and 21.37 °C·10 yr⁻¹. Urban-rural urbanization ratio differences (ΔURs) exhibited significant correlations with both extreme daily ΔCDDs (r=0.85) and total summer ΔCDDs (r=0.89), attributing 72 %∼79 % of the increases to urbanization. For 66 % and 43 % of the urban-rural station pairs, growing speeds of extreme daily ΔCDDs and total summer ΔCDDs surpassed 0.2 °C·10 yr⁻¹ and 20 °C·10 yr⁻¹, respectively. After surpassing the 44 % threshold in ΔURs, the growth of both extreme daily ΔCDDs and total summer ΔCDDs accelerated with increasing ΔURs. The study emphasizes an increasing summer cooling demand in Chinese cities, necessitating prompt attention from urban planners amidst global warming and urbanization challenges.

Monitoring of Cropland Abandonment and Land Reclamation in the Farming–Pastoral Zone of Northern China

The farming–pastoral zone in northern China is one of the most ecologically sensitive areas globally, having experienced extensive cropland abandonment and land reclamation over decades, primarily influenced by policy adjustment and global warming. However, the spatiotemporal patterns and suitability of long-term cropland change remain poorly understood. Using the annual China land cover dataset (CLCD), we provide a cropland abandonment and land reclamation mapping approach based on actual land use processes (rather than land cover conditions) to investigate spatiotemporal features of abandonment and reclamation and evaluate the rationality. Our findings show that: (1) Returning farmland to forest and grassland has been a clear trend in the study area over the past 30 years. Specifically, cropland use has undergone three phases of change, i.e., cropland contraction and expansion alternately (before 2000), followed by substantial abandonment (after 2000), and low-intensity reclamation (after 2010). (2) In the last decade, the intensity of the abandonment of cropland with high and moderate suitability is low. The rate of abandonment decreased, while the intensity of land reclamation was relatively high. The rate of the reclamation increased, and the spatial distribution of cropland tended to be reasonable. Our study emphasizes the importance of monitoring actual cropland changes based on land use processes, and this method can be effectively extended to regional or global long-term cropland monitoring.

The expansion and remaining suitable areas of global oil palm plantations

Abstract Non-technical summary Oil palm has been criticized for being an environmentally unfriendly oil crop. In recent decades, oil palm plantations have extended into conservation landscapes, causing severe environmental damage and harming biodiversity. Nevertheless, oil palm remains a highly productive oil crop from which most of the world's vegetable oil is produced. Therefore, measuring the environmental impact of oil palm plantations and identifying suitable land to support its sustainable development is crucial. Technical summary To meet the rising global palm oil demand sustainably, we tracked annual land cover changes in oil palm plantation and mapped areas worldwide suitable for sustainable oil palm cultivation. From 1982 to 2019, 3.6 Mha of forests were converted to oil palm plantations. Despite a recent decline in overall conversion, the shift from forest to oil palm plantations has become increasingly more common over the last decade, rising from 14.1 to 34.5% between 2009 and 2019. During 1982–2019, 2.23 Mha of peatland and 0.1 Mha of protected areas were converted for oil palm plantations. The potential sustainable land amounts to 103.5–317.9 Mha (Asia: 44.6–105.1 Mha, Africa: 34.7–96.4 Mha, and Latin America: 35.2–116.5 Mha). Future oil palm expansion is anticipated to take place in countries like Brazil, Nigeria, Colombia, Indonesia, Ivory Coast, the Democratic Republic of the Congo, and Ghana, where more sustainable land is available for cultivation. Malaysia, on the other hand, is about to exceed the area of sustainable cultivation, and further expansion is not recommended. These findings can advance our understanding of the environmentally damaging impacts of oil palm and enhance the feasibility of sustainable oil palm development. Social media summary How should suitable land be chosen for the establishment of oil palm plantations to support the sustainable development of the oil palm plantation industry?