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Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

Remote sensing (RS) of biophysical variables plays a vital role in providing the information necessary for understanding spatio-temporal dynamics in ecosystems. The hybrid approach to retrieve biophysical variables from RS by combining Machine Learning (ML) algorithms with surrogate data generated by Radiative Transfer Models (RTM). The susceptibility of the ill-posed solutions to noise currently constrains further application of hybrid approaches. Here, we explored how noise affects the performance of ML algorithms for biophysical trait retrieval. We focused on synthetic Sentinel-2 (S2) data generated using the PROSAIL RTM and four commonly applied ML algorithms: Gaussian Processes (GPR), Random Forests (RFR), and Artificial Neural Networks (ANN) and Multi-task Neural Networks (MTN). After identifying which biophysical variables can be retrieved from S2 using a Global Sensitivity Analysis, we evaluated the performance loss of each algorithm using the Mean Absolute Percentage Error (MAPE) with increasing noise levels. We found that, for S2 data, Carotenoid concentrations are uniquely dependent on band 2, Chlorophyll is almost exclusively dependent on the visible ranges, and Leaf Area Index, water, and dry matter contents are mostly dependent on infrared bands. Without added noise, GPR was the best algorithm (<0.05%), followed by the MTN (<3%) and ANN (<5%), with the RFR performing very poorly (<50%). The addition of noise critically affected the performance of all algorithms (>20%) even at low levels of added noise (≈5%). Overall, both neural networks performed significantly better than GPR and RFR when noise was added with the MTN being slightly better when compared to the ANN. Our results imply that the performance of the commonly used algorithms in hybrid-RTM inversion are pervasively sensitive to noise. The implication is that more advanced models or approaches are necessary to minimize the impact of noise to improve near real-time and accurate RS monitoring of biophysical trait retrieval.

In Western Europe the fate of biodiversity is intimately linked to agricultural land use. A driving force behind biodiversity decline is the gradual conversion of Europe's traditional integrated rural landscapes of nature and agriculture into monofunctional units of production. With these developments, semi-natural landscape elements have increasingly disappeared from agricultural landscapes. A growing body of research, however, underlines the importance of semi-natural habitats in agricultural landscapes for biodiversity conservation, habitat connectivity, and ecosystem services. On the local scale, considerable variation between the relative area of landscape elements on individual farms can be observed. Farm management decisions are presumed to be important determinants for the composition of agricultural landscapes and the services provided to society. By bringing together data from farmer interviews and aerial photographic imagery, this paper analyzes the predictive validity of farm management characteristics to understand the distribution of landscape elements on farmland parcels. The farm management parameters included in the study are relevant to current dominant trends in the Dutch agricultural sector; intensification, scale enlargement, diversification, and gradual termination of farming activities. Scale enlargement and migratory processes are found to be important predictors. The results of the Dutch case study provide insights in the threats and opportunities for the conservation of semi-natural habitat in agricultural landscapes. The findings present an empirical contribution to the debate on sustainable management of agriculture's green infrastructure and, in broader perspective, the objective to reconcile agricultural production with the urging need of biodiversity conservation in Europe's spatially contested countryside.

Mangrove forests provide vital ecosystem services. The increasing threats to mangrove forest extent and fragmentation can be monitored from space. Accurate spatially explicit quantification of key vegetation characteristics of mangroves, such as leaf area index (LAI), would further advance our monitoring efforts to assess ecosystem health and functioning. Here, we investigated the potential of radiative transfer models (RTM), combined with active learning (AL), to estimate LAI from Sentinel-2 spectral reflectance in the mangrove-dominated region of Ngoc Hien, Vietnam. We validated the retrieval of LAI estimates against insitu measurements based on hemispherical photography and compared against red-edge NDVI and the Sentinel Application Platform (SNAP) biophysical processor. Our results highlight the performance of physics-based machine learning using Gaussian processes regression (GPR) in combination with AL for the estimation of mangrove LAI. Our AL-driven hybrid GPR model substantially outperformed SNAP (R 2 = 0.77 and 0.44 respectively) as well as the red-edge NDVI approach. Comparing two canopy RTMs, the highest accuracy was achieved by PROSAIL (RMSE = 0.13 m 2 .m −2 , NRMSE = 9.57%, MAE = 0.1 m 2 .m −2 ). The successful retrieval of mangrove LAI from Sentinel-2 can overcome extensive reliance on scarce in-situ measurements for training seen in other approaches and present a more scalable applicability by relying on the universal principles of physics in combination with uncertainty estimates. AL-based GPR models using RTM simulations allow us to adapt the genericity of RTMs to the peculiarities of distinct ecosystems such as mangrove forests with limited ancillary data. These findings bode potential for retrieving a wider range of vegetation variables to quantify large-scale mangrove ecosystem dynamics in space and time.

The complex relationship between human mobility and global climate change remains contested. In this viewpoint, the themes of human mobility, adaptation and climate change are explored from a political ecology perspective. A framework of political ecology of human mobility in relation to climate change is applied to the context of Vietnam's Mekong Delta (MKD). The Vietnamese government, popular media and academic studies often present the MKD in dystopian ways in which there is sometimes no more place for poor and landless farmers as a direct result of climate change. In 2019 and 2020, the MKD faced one of its most severe droughts in recent history largely tied to upstream hydropower development. In this viewpoint article, we contend that future studies can no longer establish a direct and causal relationship between climate change and human mobility, especially in light of these recent events. The underlying drivers as well as the broader context, which are shaped by political economy, market structures and forces, power relations, government policy, geopolitics, and transboundary water issues deserve a more prominent role in the analysis of human mobility patterns in the MKD and beyond.