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Monitoring ecosystems at regional or continental scales is paramount for biodiversity conservation, climate change mitigation, and sustainable land management. Effective monitoring requires satellite imagery with both high spatial resolution and high temporal resolution. However, there is currently no single, freely available data source that fulfills these needs. A seamless fusion of data from the Sentinel-3 and Sentinel-2 optical sensors could meet these monitoring requirements as Sentinel-2 observes at the required spatial resolution (10 m) while Sentinel-3 observes at the required temporal resolution (daily). We introduce the Efficient Fusion Algorithm across Spatio-Temporal scales (EFAST), which interpolates Sentinel-2 data into smooth time series (both spatially and temporally). This interpolation is informed by Sentinel-3's temporal profile such that the phenological changes occurring between two Sentinel-2 acquisitions at a 10 m resolution are assumed to mirror those observed at Sentinel-3's resolution. The EFAST consists of a weighted sum of Sentinel-2 images (weighted by a distance-to-clouds score) coupled with a phenological correction derived from Sentinel-3. We validate the capacity of our method to reconstruct the phenological profile at a 10 m resolution over one rangeland area and one irrigated cropland area. The EFAST outperforms classical interpolation techniques over both rangeland (−72% in the mean absolute error, MAE) and agricultural areas (−43% MAE); it presents a performance comparable to the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (+5% MAE in both test areas) while being 140 times faster. The computational efficiency of our approach and its temporal smoothing enable the creation of seamless and high-resolution phenology products on a regional to continental scale. [ABSTRACT FROM AUTHOR]

In this article, we shine a spotlight on approaches to research ecosystem service trade-offs and critically assess their representation of relevant social dynamics. Although studies linking ecosystem services and human well-being have provided theoretical insights into social and ecological trade-offs, we argue that ecosystem services research has paid insufficient attention to "social feedbacks," people's cognitive and behavioral responses to change. We demonstrate that augmenting ecosystem services research with environmental justice approaches (exploring perceptions of the distribution of costs and benefits, decision making procedures, and recognition of different values and identities) can more effectively capture important responses to ecosystem governance. Spatial analysis of land use change, mixed-method assessment of multidimensional well-being, and qualitative environmental justice research were applied in three villages adjacent to Nam Et-Phou Louey National Protected Area in northern Laos. Spatial analysis showed that, from 2006 to 2015, forest clearance for cultivation remained stable within the protected area. Well-being assessment revealed the local population benefited from rapidly increasing incomes, asset ownership, and reduced poverty during that time. In combination, spatial and well-being analyses paint a picture of limited trade-offs, despite growing incentives to exploit protected land and resources through cash crops and high-value forest products. In contrast, results from environmental justice research revealed profound trade-offs between conservation and local practices, and highlight governance deficiencies relating to procedure and recognition. Consequently, formal protected area rules were perceived to be illegitimate by many and actively undermined, for example through negotiated access with alternative authorities. We conclude that although well-being research provides an essential foundation to understand diverse attachments to natural resources, the addition of environmental justice research can reveal local perceptions and social feedbacks critical to ecosystem service trade-offs, and highlight pathways to reconcile them through satisfying stakeholders' diverse, dynamic objectives. [ABSTRACT FROM AUTHOR]

Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions. [ABSTRACT FROM AUTHOR]

The recent rise in global demand for natural rubber ( Hevea brasiliensis ) has led to expansive areas of natural forest being transformed into monoculture plantations. This paper explores the utility of annual Landsat time series for monitoring forest disturbance and the role of natural rubber in mainland Southeast Asia from 2000 to 2012. A region on the Cambodian–Vietnamese border was chosen for this study considering four primary questions: 1) how accurately can annual Landsat time series map the location and timing of forest disturbances in evergreen and seasonal tropical forests, 2) are there cross-border differences in frontier and non-frontier forest disturbance rates between Cambodia and Vietnam, 3) what proportion of disturbances in frontier and non-frontier forests can be accounted for by the impact of rubber plantations, and 4) is there a relationship between global market prices for natural rubber and the annual rate of frontier forest clearing for rubber plantations on both sides of the border. We used LandTrendr (Landsat-based detection of trends in disturbance and recovery) for temporal segmentation of the Landsat time series and disturbance mapping. Our results show that this approach can provide accurate forest disturbance maps but that accuracy is affected by forest type. Highest accuracies were found in evergreen forest (91%), with lower accuracies in mixed (82%) and dry-deciduous forest types (86%). Our final map considering all forest types yielded an overall accuracy of 86%. Forest disturbance rates were generally higher on the Cambodian side of the border. Frontier forest disturbance rates averaged 3.8%/year in Cambodia compared to 2.5%/year in Vietnam. Conversion to rubber was the dominant form of frontier forest change in both countries (42% in Cambodia and 84% in Vietnam). Non-frontier forest disturbances averaged 4.0% and 2.5% in Cambodia and Vietnam, respectively, with most disturbances likewise linked with rubber plantations. Although rates of frontier forest disturbance differed in both countries, they each displayed similar correlations between disturbance rates related to rubber plantation expansion and price fluctuations of natural rubber. This suggests links between localized land cover/use change and international market forces, irrespective of differing political and socioeconomic backgrounds. Our study underlines the value of using dense Landsat time series when exploring the dynamics of human-induced land cover change. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectroradiometer (MODIS) has been supplying a continuous data stream since 2000, lending to detailed time series analysis of the global terrestrial environment. This paper explores a quality anomaly present in the tropics relating to the aerosol quantity flag in the daily MODIS surface reflectance products (MOD09 series) and the 16-day Vegetation Index (VI) composite products (MOD13 series) derived from the daily observations. While the anomaly is to some extent a known issue reported by the MODIS Land Quality Assessment group, very little is known about the scale of the issue, the nature and patterns of its occurrence, and potential consequences for data analysis, which explains why it is not adequately recognized throughout the literature. Two tropical regions were used to explore the anomaly and demonstrate the effects it has on the quality of selected MODIS products–one in the South American Amazon, the other in mainland Southeast Asia. The origins of the anomaly are described qualitatively in detail, and quantitative estimates of affected evergreen forest area in the MOD13A1 time series are made using blue band thresholding. The anomaly originates in the 1 km State dataset, whereby, under certain conditions, high aerosol quantity pixels are given a low aerosol quantity label, resulting in poor quality pixels with "good" quality labels. MODIS users are advised to investigate whether this anomaly has significant implications for their respective analysis and to consider the effects it may have on past studies. [ABSTRACT FROM AUTHOR]

Shifting cultivation has long been a major livelihood for people in the miombo woodlands of southern, central and eastern Africa. However, increasing deforestation and forest degradation throughout the region are resulting in growing pressure on traditional shifting agricultural systems. Indeed, agricultural intensification and expansion itself is considered the primary cause of miombo deterioration, which is driven by both endogenous and exogenous variables operating at various scales. On the basis of data collected in the 1990s and 2010 from two villages in Northern Province, Zambia and two in the Rukwa Region, Tanzania, the paper will document the transition of shifting cultivation towards more intensive land use practices. It is argued that the main drivers influencing miombo degradation, and thereby the transition process of traditional shifting cultivation practices, have been a growing population, government policies, and an increasing commercialization/market integration. Questionnaires, focus group meetings, and in-depth interviews reveal that despite the breakdown of the traditional shifting cultivation practices, a general improvement of livelihoods has taken place. This has happened through adaptation and diversification in both agricultural practices and livelihood activities. However, it is also seen that because of the often rapidly changing external factors (market conditions and policies), life in the shifting cultivation communities involves a continual shift of emphasis among a variety of livelihood strategies. [ABSTRACT FROM AUTHOR]

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300-3000 km² or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales. [ABSTRACT FROM AUTHOR]

As a way to understand vegetation changes, trend analysis on NDVI (normalized difference vegetation index) time series data have been widely performed at regional to global scales. However, most long-term NDVI datasets are based upon multiple sensor systems and unsuccessful corrections related to sensor shifts potentially introduce substantial uncertainties and artifacts in the analysis of trends. The temporal consistency of NDVI datasets should therefore be evaluated before performing trend analysis to obtain reliable results. In this study we analyze the temporal consistency of multi-sensor NDVI time series by analyzing the co-occurrence between breaks in the NDVI time series and sensor shifts from GIMMS3g (Global Inventory Modeling and Mapping Studies 3rd generation), VIP3 (Vegetation Index and Phenology version 3), LTDR4 (Long Term Data Record version 4) and SPOT-VGT (Système Pour l'Observation de la Terre VEGETATION). Single sensor time series from MODIS (MODerate Resolution Imaging Spectroradiometer) Terra and Aqua are used as reference datasets. The global land surface is divided into six regions according to the world humidity zones and averaged NDVI time series in each region are analyzed separately using a multiple structural change detection approach. We find that artifacts exist in the VIP3 and LTDR4 NDVI datasets with an abrupt shift detected in all humidity zones coinciding with the shift from NOAA-9 to NOAA-11 in 1988 and that orbital drift effects are evident in arid regions, potentially introducing uncertainties in NDVI trend analysis. Platform/sensor change from VGT-1 to VGT-2 is found to cause a significant positive break in the SPOT-VGT NDVI time series. Potential artifacts exist in humid, dry-subhumid, semi-arid and hyper-arid regions of GIMMS3g NDVI, whereas no signs of artifacts are found in the arid region. Although temporal consistency throughout all examined datasets increases after 2000 due to the usage of advanced platforms and sensors, variations in NDVI values from 2010 to 2011 still result in different trends at global and regional scales. [ABSTRACT FROM AUTHOR]

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting. [ABSTRACT FROM AUTHOR]