Annual Land Cover Research Articles

Rapid urbanization and global warming significantly impact tidal dynamics in the Pearl River Estuary, China

Due to the dual impacts of anthropogenic disturbances and climate change, tidal hydrodynamics of estuaries have been intensively investigated for their potential to increase flood risk and devastate coastal habitats and human communities. Climate change leads to rises in sea levels, which can amplify regional tides and increase tidal ranges. This study takes the estuarine area of the Pearl River (PRE), one of the fastest-developed estuaries in China, as an example to explore the effects of land cover changes and sea level rise on tidal dynamics. Annual mean tidal levels were collected at 26 major hydrological stations from five major rivers/waterways. To better understand the temporal changes in tidal levels, we analyzed the trends and periodic patterns through linear regression and Complex Morlet wavelet analyses. For the main Pearl River outlet, where conversion of agricultural land to urban use had been primarily taking place, the tidal level changes were significantly affected by increased built-up areas and decreased cultivated land areas. On the other hand, in the middle to upper reaches of the Xi River and Shunde-Hengmen Waterway, we observed decreased tidal levels with decreases in the agricultural lands. No significant correlations were found between annual mean tidal levels and land cover changes downstream of the main rivers. Instead, the tidal level changes at locations close to the coastline were more related to sea level rise, with an average of 0.002 m per year increase. This study highlighted the importance of human activity and sea level rise impacts on tidal dynamics. A systematic understanding of the tidal changes in the estuary is expected to help enhance mechanisms of estuarine hydrology, which could serve as the basis for improving the management and practice of coastal regions in coping with climate change and estuary protection.

The Kabul River Basin - the source of the Naglu and other reservoirs

The article is devoted to the assessment and study of the Kabul river basin fed by 7 existing (Jabal-Saraj, Karga, Mahipar, Naglu, Sarubi, and Darunta) and numerous planned (including Gulbahor, Baghdara, Sarubi II, Kunar I, Kunar II, Shatut) reservoirs. The article discusses the Kabul basin's annual precipitation, land cover, and runoff. In addition, it sheds light on soil erosion and the concentration of river and reservoir sedimentation in the Kabul River Basin. Recommendations are also given for solving the problem of soil erosion and the siltation of reservoirs. There is very little data on the Kabul River and its basin due to the lack of detailed hydrological data. Most of the hydrological and meteorological posts did not function or were destroyed during the crisis in the country for about two decades, which led to little or no information on the river's hydrology during this period. Because of this problem, many organizations working in basin water management and designing new hydraulic structures face difficulties in properly planning and designing new hydraulic structures and reservoirs. As a result, most researchers, planners, and designers use old data with a limited observation period, which is insufficient for adequate design and planning of hydraulic structures and other basin development projects. Due to four decades of unrest in the country, the watersheds of the Kabul Basin have been used without any rules or management, resulting in land degradation and watershed erosion. Three main problems, namely overgrazing in rangelands, rain-fed fields, and deforestation, have caused land degradation in the basin. In addition, the situation has been aggravated by the cutting of bushes for fuel in hilly and semi-hilly areas. The basin is overpopulated, and there are many industries in the major cities of the basin, which contributes to water pollution in the Kabul river. Despite water availability, due to a lack of proper planning, the major cities in the basin cannot use the basin's water for domestic needs. In cities like Kabul and Jalalabad, people mainly use groundwater, resulting in the continuous sinking of the groundwater level. For the proper management and development of the Kabul river basin, much work needs to be done, which may yield promising results for developing the country's economy and improving the environment in the Kabul basin region.

ASSESSMENT OF WETLAND DEGRADATION CONDITION AND TREND BASED ON LAND USE/COVER IN WAMI-RUVU RIVER BASIN USING MULTI-TEMPORAL LANDSAT IMAGES

The understanding of wetlands’ distribution and their level of susceptibility is important to enhance management and conservation efforts. The study aimed to map wetlands and assess their distribution pattern and their degradation based on the LULC by the years 2000, 2010 and 2020. Wetland types i.e., marshes, ponds, lakes, fens, rivers, floodplains, swamps, and open water bodies with other land use such as agriculture land, built-up and natural vegetation land were classified and mapped using random forest algorithm in Arc Map. Multi-temporal Landsat satellite data including thematic Mapper Landsat (+ETM) and Operational Land Imager (OLI) images were used for LULC mapping. The random forest classifier technique is used to analyse the satellite imagery for detecting the LULC changes during the whole study period. To assess the wetland area degradation three aspects were used in this study for the analysis; wetland area change rate, annual land cover change and annual land cover change rate. The study found about -34.41Km2/y of wetlands potentially are degraded annually due to human and natural stressors in 20 years of our study however the basin experienced a higher rate of wetland degradation in the second epoch of 2010-2020 where the basin experience -42.8Km2/y degradation which results to the total loss of -687.75 km2 total coverage of wetland area in the basin. This information could be used to improve wetland planning and management by wetland managers and other stakeholders.

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.

Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions.

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Generating annual high resolution land cover products for 28 metropolises in China based on a deep super-resolution mapping network using Landsat imagery

ABSTRACT High resolution of global land cover dynamic is indicative for understanding the influence of anthropogenic activity on environmental change. However, most of the land cover products are based on Landsat image that only has 30 m resolution, which is insufficient to distinguish the heterogenous urban structure; while very high spatial resolution image usually has low temporal resolution, which is difficult to monitor the urban dynamic. Deep-learning-driven super-resolution mapping is a prevailing way of achieving very-high-resolution land cover dynamic products in aspect of alleviating the mixed pixel problem of Landsat image. However, two limitations are obvious: 1) the fixed grid of kernel during the upsampling process favors spatial homogeneity and suppresses the learning of spatial heterogeneity of urban composition and 2) geometric or radiation variation over large spatial and long temporal extent in remote sensing images makes the super-resolution mapping approach difficult to transfer for application. Here, we attempt to solve these two limitations: 1) a progressive edge-guided super-resolution architecture is designed to allow nonuniformed kernel specific at the low-confidence edge region and intensify the learning of heterogenous compositions’ patterns and 2) an alternating optimization strategy is designed to minimize the resultant entropy and modulate the classification hyperplane to accommodate to the manifold of the discrepant region. Validation experiments are investigated based on a fine-grained and large-extent super-resolution (FLAS) dataset constructed in this study, and it is found that our approach remarkably enhances rich detailed patterns of heterogenous region and outperforms other state-of-the-art algorithms. Besides, we applied DETNet to the large spatial extent of 28 metropolises in China (>40,000 km2) and the large temporal extent of continuous 21-year (2000–2020) in Wuhan city to examine transferability. From the land cover areas variation, we find that the expansion rate of cropland is faster than the urban expansion over the past 10 years, which are gradually becoming the principal source for the encroachment of forest and lakes. From detailed urban dynamic reflected by the 21-year products, we find that urban-villages between the old city zone and the outer high-tech development zone are gradually disappeared. The captured dynamic is consistence with the urban-village renovation policy during this period, which is meant to redistribute the spatial configuration of the city for a more sustainable urban structure. We believe that the proposed method can facilitate a seamless and fine-grained observation system that can fill the weakness of the existing land cover activities and provide a brand-new insight into the urban dynamic and its underlying mechanism.

Natural climate solutions in Indonesia: wetlands are the key to achieve Indonesia’s national climate commitment

Indonesia offers a dramatic opportunity to contribute to tackling climate change by deploying natural climate solutions (NCS), increasing carbon sequestration and storage through the protection, improved management, and restoration of drylands, peatlands, and mangrove ecosystems. Here, we estimate Indonesia’s NCS mitigation opportunity for the first time using national datasets. We calculated the maximum NCS mitigation potential extent using datasets of annual national land cover, peat soil, and critical lands. We collated a national emissions factor database for each pathway, calculated from a meta-analysis, recent publications from our team, and available literature. The maximum NCS mitigation potential in 2030 is 1.3 ± 0.04 GtCO2e yr−1, based on the historical baseline period from 2009–2019. This maximum NCS potential is double Indonesia’s nationally determined contribution (NDC) target from the forestry and other land use sector. Of this potential opportunity, 77% comes from wetland ecosystems. Peatlands have the largest NCS mitigation potential (960 ± 15.4 MtCO2e yr−1 or 71.5 MgCO2e ha−1 yr−1) among all other ecosystems. Mangroves provide a smaller total potential (41.1 ± 1.4 MtCO2e yr−1) but have a much higher mitigation density (12.2 MgCO2e ha−1 yr−1) compared to dryland ecosystems (2.9 MgCO2e ha−1 yr−1). Therefore, protecting, managing, and restoring Indonesia’s wetlands is key to achieving the country’s emissions reduction target by 2030. The results of this study can be used to inform conservation programs and national climate policy to prioritize wetlands and other land sector initiatives to fulfill Indonesia’s NDC by 2030, while simultaneously providing additional co-benefits and contributing to COVID-19 recovery and economic sustainability.

Wavelet-based spatiotemporal analyses of climate and vegetation for the Athabasca river basin in Canada

Monitoring spatiotemporal changes in climate and vegetation coverage are crucial for various purposes, including water, hazard, and agricultural management. Climate has an impact on vegetation, however, studying their relationship is challenging. We implemented the Least-Squares Wavelet (LSWAVE) software for investigating trend, coherency, and time lag estimation between climate and vegetation time series. We utilized Normalized Difference Vegetation Index (NDVI) time series provided by the Terra satellite and hybrid climate time series. We found that the seasonal cycles of climate and NDVI are coherent with time delay. For the entire Athabasca River Basin (ARB), the most coherent component was the annual cycle with 84% annual coherency between vegetation and temperature and 46% between vegetation and precipitation. The annual cycles of temperature and precipitation led the ones in vegetation by about two and three weeks, respectively. Relatively lower coherency was observed in the mountainous region (upper ARB) and higher coherency in the middle ARB. From the cross-spectrograms, a clear time delay pattern was observed between the annual cycles of climate and vegetation since 2000 but not for other high-frequency seasonal cycles. The results also highlighted the advantages of LSWAVE algorithms over traditional algorithms, such as linear regression and correlation. Furthermore, we analyzed the annual land use and land cover data provided by the Terra and Aqua satellites and discussed their linkage with the climate and NDVI results.

Development of Flood Hazard Index (FHI) of the Kelantan River Catchment Using Geographic Information System (GIS) Based Analytical Hierarchy Process (AHP)

Kelantan has been facing several cases of catastrophic flooding, causing significant damage to this area. Heavy monsoon rainfall is believed to trigger those floods. This study aims to identify and classify the flood occurrence using the Kelantan River catchment’s flood hazard index (FHI) based on the analytical hierarchy process (AHP). This study developed the FHI using the AHP based on spatial analysis in the geographic information system (GIS) environment. Six physical parameters were selected: annual rainfall, slope, river density, land use and land cover (LULC); elevation; and soil permeability. According to the AHP model, the annual rainfall was the first ranked parameter in terms of importance weight score. Moreover, Tanah Merah and Jeli were the high-risk areas for floods. The present study suggests that the GIS-based AHP method can be highly effective for mapping flood hazards and benefit flood management decision-making.

Disentangling the landscape during armed conflicts and postpeace agreements: Clues from Colombia's Andes-Amazon region.

The link between remote sensing and armed conflict processes has been evaluated through discrete landscape representations, deforestation, and static land cover maps. Yet, the landscape is dynamic-not discrete, and recognizing its evolution through armed conflict processes provides better-informed management and a more profound understanding of landscape dynamics. We must create continuous variables that provide compelling landscape representations that account for armed conflict processes as a driver of land cover and land-use change. Here, we present the advancements in monitoring landscape changes in Colombia from subannual forest change and annual land cover maps to elucidate illicit land use and habitat connectivity status. This evolution delivers critical elements to understanding the consequences of armed conflict processes on the environment. Integr Environ Assess Manag 2023;19:355-359. © 2022 SETAC.

Cerrado deforestation threatens regional climate and water availability for agriculture and ecosystems.

The Brazilian Cerrado is one of the most biodiverse savannas in the world, yet 46% of its original cover has been cleared to make way for crops and pastures. These extensive land-use transitions (LUTs) are expected to influence regional climate by reducing evapotranspiration (ET), increasing land surface temperature (LST), and ultimately reducing precipitation. Here, we quantify the impacts of LUTs on ET and LST in the Cerrado by combining MODIS satellite data with annual land use and land cover maps from 2006 to 2019. We performed regression analyses to quantify the effects of six common LUTs on ET and LST across the entire gradient of Cerrado landscapes. Results indicate that clearing forests for cropland or pasture increased average LST by ~3.5°C and reduced mean annual ET by 44% and 39%, respectively. Transitions from woody savannas to cropland or pasture increased average LST by 1.9°C and reduced mean annual ET by 27% and 21%, respectively. Converting native grasslands to cropland or pasture increased average LST by 0.9 and 0.6°C, respectively. Conversely, grassland-to-pasture transitions increased mean annual ET by 15%. To date, land changes have caused a 10% reduction in water recycled to the atmosphere annually and a 0.9°C increase in average LST across the biome, compared to the historic baseline under native vegetation. Global climate changes from increased atmospheric greenhouse gas concentrations will only exacerbate these effects. Considering potential future scenarios, we found that abandoning deforestation control policies or allowing legal deforestation to continue (at least 28.4Mha) would further reduce yearly ET (by -9% and -3%, respectively) and increase average LST (by +0.7 and +0.3°C, respectively) by 2050. In contrast, policies encouraging zero deforestation and restoration of the 5.2Mha of illegally deforested areas would partially offset the warming and drying impacts of land-use change.

Two decades of land cover mapping in the Río de la Plata grassland region: The MapBiomas Pampa initiative

The Río de la Plata Grasslands (RPG) region is the largest area of the temperate humid and sub-humid grasslands biome in South America and one of the largest in the world. The region is located on fertile soils, generally very suitable for agricultural development, so it is undergoing an intense land cover change process. Our knowledge of these changes remains incomplete. Most regional-scale studies have been conducted over specific periods, limited subsets of the RGP, coarse resolution and, in general, used land cover classes that are not readily compatible. In this work we described and analyzed the land cover changes in the entire RPG region for the first two decades of the 21st century, especially those related to grasslands loss. We generated annual land cover maps with 30-m resolution that discriminate between 8 categories: native woody formation, forest plantation, swampy areas and flooded grassland, grassland, farming, non-vegetated area, water and non-observed. The map series was evaluated for the years 2001 and 2018 using a completely independent dataset, selected by stratified randomized sampling. Overall accuracy was 73.5% and 77.8% for 2001 and 2018, respectively, with user and producer accuracies that varied between classes and years. In 20 years, RPG region lost, at least, 2.4 million ha of grassland (9% of the remaining grassland area in 2001). Most of these losses are concentrated in Brazil and Uruguay and are associated with new agricultural or forestry areas that increased by 5% and 100%, respectively. Our maps allow a comprehensive understanding of the transformation processes that RPG are undergoing and provide the context on which to explore a large set of hypotheses related to ecosystem structure and functioning. It will also contribute to improving decision-making at both the regional and national levels.

Development of the LCMAP annual land cover product across Hawaiʻi

• Generated several training datasets by integrating existing datasets. • Produced annual land cover maps for the Hawaiian Islands at 30-m resolution over 20 years with different training datasets and Change detection and classification (CCDC) algorithm. • Summarized the effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types. • Revealed annual land cover changes in Hawaiʻi. Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at https://doi.org/10.5066/P91E8M23 .

Synergies between environmental degradation and climate variation on malaria re-emergence in southern Venezuela: a spatiotemporal modelling study

Environmental degradation facilitates the emergence of vector-borne diseases, such as malaria, through changes in the ecological landscape that increase human-vector contacts and that expand vector habitats. However, the modifying effects of environmental degradation on climate-disease relationships have not been well explored. Here, we investigate the rapid re-emergence of malaria in a transmission hotspot in southern Venezuela and explore the synergistic effects of environmental degradation, specifically gold-mining activity, and climate variation. In this spatiotemporal modelling study of the 46 parishes of the state of Bolívar, southeast Venezuela, we used data from the Venezuelan Ministry of Health including population data and monthly cases of Plasmodium falciparum malaria and Plasmodium vivax malaria between 1996 and 2016. We estimated mean precipitation and temperature using the ERA5-Land dataset and used monthly anomalies in sea-surface temperature as an indicator of El Niño events between 1996 and 2016. The location of suspected mining sites in Bolívar in 2009, 2017, and 2018 were sourced from the Amazon Geo-Referenced Socio-Environmental Information Network. We estimated measures of cumulative forest loss and urban development by km2 using annual land cover maps from the European Space Agency Climate Change Initiative between 1996 and 2016. We modelled monthly cases of P falciparum and P vivax malaria using a Bayesian hierarchical mixed model framework. We quantified the variation explained by mining activity before exploring the modifying effects of environmental degradation on climate-malaria relationships. We observed a 27% reduction in the additional unexplained spatial variation in incidence of P falciparum malaria and a 23% reduction in P vivax malaria when mining was included in our models. The effect of temperature on malaria was greater in high mining areas than low mining areas, and the P falciparum malaria effect size at temperatures of 26·5°C (2·4 cases per 1000 people [95% CI 1·78-3·06]) was twice as high as the effect in low mining areas (1 case per 1000 people [0·68-1·49]). We show that mining activity in southern Venezuela is associated with hotspots of malaria transmission. Increased temperatures exacerbated malaria transmission in mining areas, highlighting the need to consider how environmental degradation modulates climate effect on disease risk, which is especially important in areas subjected to rapidly rising temperatures and land-use change globally. Our findings have implications for the progress towards malaria elimination in the Latin American region. Our findings are also important for effectively targeting timely treatment programmes and vector-control activities in mining areas with high rates of malaria transmission. Biotechnology and Biological Sciences Research Council, Royal Society, US National Institutes of Health, and Global Challenges Research Fund. For the Spanish translation of the abstract see Supplementary Materials section.

Time-series land cover mapping and urban expansion analysis using OpenStreetMap data and remote sensing big data: A case study of Guangdong-Hong Kong-Macao Greater Bay Area, China

• Optimized training samples from OpenStreetMap facilitate large-scale land cover mapping. • Sample optimization and transfer, and consistency modification strategies improve mapping results. • The proposed workflow achieves satisfactory performance and is transferable to other regions. Accurate land cover mapping provides important scientific support for ecological environment protection and sustainable urban development. However, given the high expense of acquiring training samples and the difficulty of fully utilizing remote sensing big data, large-scale time-series land cover mapping remains to be a challenge. To address the issue, we proposed a novel time-series large-scale mapping approach that obtains high-quality training samples from OpenStreetMap volunteered data and transfers them for land cover mapping in historical years. Relying on the data archived on the Google Earth Engine platform, we constructed a discriminating feature set that contains spectrum, texture, and backscatter coefficient, among others. Taking the Guangdong-Hong Kong-Macao Greater Bay Area as a case area, the annual land cover maps after spatio-temporal consistency modification from 1986 to 2021 were obtained. The validation samples proved that the derived land cover classification results have high accuracy. We verified the reliability and superiority of the proposed approach by comparing our results with existing land cover products. The proposed mapping approach owns great transferability, and the mapping results provide a valid decision-making basis for urban planning.

Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America

The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.

Global occupation of wetland by artificial impervious surface area expansion and its impact on ecosystem service value for 2001–2018

Wetland is one of the most critical ecosystems with various functions, it produces the highest ecosystem services value (ESV) per unit area among all land types, thus, its decline has become a global concern in recent years. Since the 21st century, many developing countries have been continuously encroaching on wetlands due to rapid economic and population growth, resulting in the replacement of a large number of wetlands by cities, and how to reconcile urban expansion and wetland conservation has become a significant problem that needs to be considered. In this context, we mapped the spatial distribution of wetlands occupied by the expansion of global artificial impervious surface area (ISA) for 2001–2018 and discussed its temporal and spatial change characteristics at the global, continental, and national scales using the annual maps of global annual impervious area data set and global dynamic land cover data set based on Google Earth Engine platform, the loss of ESV due to wetland occupation was also measured at each spatial scale level. The results show that: (1) A total of 1301.24 km2 of wetlands were occupied by ISA expansion globally for 2001–2018, and their interannual variation showed an upward trend for 2001–2015, then began to decline rapidly since 2016. The occupation results in a global loss of US$3.46 billion in ecosystem service value; (2) Asia has the highest loss of ESV due to occupation of wetlands by ISA expansion, 624.18 km2 of occupation of wetlands resulted in a loss of US$1.66 billion in ESV, making it the continent with the largest area of occupied wetlands. Taking into account the different sizes of the continents, Oceania is the continent where ISA expansion occupies the wetlands most, while South America is the continent where it is relatively least severe; (3) China, the USA and Russia account for 50.9 % of global ESV loss due to wetland occupation by ISA expansion, a total of 663.95 km2 of wetlands were occupied. Although countries have made great efforts to protect wetland resources and deal with urban land expansion, our results show that the problem of wetlands occupied by global urban land expansion is still severe.

Patterns and drivers of recent land cover change on two trailing-edge forest landscapes

Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3 % and 0.8 % from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (Pinus edulis, Pinus monophylla, and/or Juniperus spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (Pinus ponderosa and Quercus spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.

Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries

Abandoned cropland may lead to a series of issues regarding the environment, ecology, and food security. In hilly areas, cropland is prone to be abandoned due to scattered planting, relatively fewer sunlight hours, and a lower agricultural input–output ratio. Furthermore, the impact of abandoned rainfed cropland differs from abandoned irrigated cropland; thus, the corresponding land strategies vary accordingly. Unfortunately, monitoring abandoned cropland is still an enormous challenge in hilly areas. In this study, a new approach was proposed by (1) improving the availability of Sentinel-1 and Sentinel-2 images by a series of processes, (2) obtaining training samples from multisource data overlay analysis and timeseries viewer tool, (3) mapping annual land cover from all available Sentinel-1 and Sentinel-2 images, training samples, and the random forest classifier, and (4) mapping the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas by assessing land-cover trajectories along with time. The result showed that rainfed cropland had lower F1 scores (0.759 to 0.8) compared to that irrigated cropland (0.836 to 0.879). High overall accuracies of around 0.90 were achieved, with the kappa values ranging from 0.851 to 0.862, which outperformed the existing products in accuracy and spatial detail. Our study provides a reference for extracting the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland in hilly areas.

Assessment of the effect of landowner type on deforestation in the Brazilian Legal Amazon using remote sensing data

SummaryThe Brazilian Legal Amazon contains important reservoirs of forest that are threatened by stakeholders’ behaviour. The Cadastro Ambiental Rural (CAR; Rural Environmental Registry) database was used to define the limits and classes of landowner according to private property size. For each class, we identified the annual forest-cover/cover-loss profile at 2-year intervals from 2000 to 2020 based on Brazilian Annual Land Use and Land Cover Mapping Project (MapBiomas) and Program for Deforestation Monitoring (PRODES) data. The analysis revealed that very large landowners dominate the CAR-registered area and that deforestation is influenced by landowner type. The cumulative contributions to deforestation were 2 916 245.96, 1 234 216.79, 2 871 400.36, 2 805 058.62 and 2 637 485.60 ha for very large landowners, large landowners, medium landowners, small landowners and very small landowners, respectively. Very large landowners (1.7% of the total number of properties) had more forest on their properties but caused the greatest amount of deforestation in total, often associated with agribusiness. Small and very small landowners were more numerous (21.0% and 68.3% of the total number of properties, respectively), but they owned a small total area and contributed less to total deforestation. Property size and landownership asymmetry should be considered in deforestation control policies and commitments to the Sustainable Development Goals agenda.