Developing up-to-date technologies for understanding crop development is essential for improving agricultural monitoring and reducing uncertainties in production estimates. This study presents a proof of concept for multi-platform, open-source tools designed to extract phenological metrics from Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series over maize cultivation areas in the Brazilian Cerrado. Three tools were developed: (i) QPhenoCrops, a QGIS plugin; (ii) PyPhenoCrops, a Jupyter Notebook-based processing chain; and (iii) GEEPhenoCrops, a Google Earth Engine script. The workflows were standardized using mean and median aggregation, along with Savitzky–Golay and Double Moving Average (DMA3 and DMA5) smoothing methods. They were tested under two maximum cloud-cover thresholds: 10% and 60%. All environments successfully reconstructed maize temporal dynamics and extracted phenological metrics. Validation against field observations showed greater stability for the Start of Season than for the End of Season across processing configurations. Under the 60% cloud-cover threshold, the QGIS STAC workflow with DMA5 achieved differences of only +1 day for the Start of Season and +6 days for the End of Season relative to sowing and harvest dates, respectively. Under the 10% threshold, the best combination of aggregation and smoothing methods was Mean and DMA5 in QGIS Local mode, resulting in differences of −1 and −14 days for SOS and EOS, respectively. Overall, the results demonstrate the feasibility of reproducible, multi-platform phenological analysis while highlighting the influence of data availability and processing configuration on metric extraction.
Coastal flooding poses a major threat to urbanized coastlines, where extreme marine forcing interacts with complex coastal morphology and concentrated human activities. This study investigates storm-induced marine flooding along the Sidi Moussa-Salé coastline, Morocco, using the two-dimensional Iber hydrodynamic model. Simulations were conducted for the January 2014 and February 2017 storm events using atmospheric, wave, water-level, topographic, and bathymetric data. The results reveal a spatially heterogeneous response, with the 2014 event producing a maximum inland inundation extent of approximately 0.20 km and a flooded area of 0.20 km2, compared with 0.15 km and 0.10 km2, respectively, in 2017. The simulated inundation patterns highlight the influence of coastal morphology, localized depressions, and preferential flow pathways on flood propagation. Spatial exposure mapping indicates an overlap between flood-prone areas and coastal roads, residential and commercial zones, recreational facilities, and other public assets. Field observations provide qualitative contextual evidence of storm-related impacts, although the absence of surveyed flood boundaries and measured water levels limits quantitative model validation. Future simulations, based on the February 2017 storm forcing and prescribed sea-level-rise increments, indicate an expanded inundation footprint under the examined scenarios. Under RCP8.5-2100, the maximum inland extent reaches 0.42 km and the flooded area 0.32 km2, compared with 0.15 km and 0.10 km2 under the 2017 baseline. These results constitute conditional sensitivity assessments rather than deterministic forecasts, given the uncertainties in model inputs and the omission of future changes in storm forcing and coastal morphology. The study provides a site-specific basis for identifying potentially exposed coastal sectors and supporting hazard-informed coastal planning, while highlighting the need for improved observational validation and systematic uncertainty assessment.
Restoration planning in tropical Andean headwaters is rarely tested against the climatic futures it must operate in, and rarely distinguishes what a strategy could achieve from what could actually be implemented. We modelled land-cover scenarios for the Molino River catchment (2018 ha, 2400–3600 m a.s.l., Cauca, Colombia) to evaluate a co-designed riparian restoration strategy. A first-order Markov chain, calibrated on transitions observed between 2000 and 2004 and 2023–2024, was coupled with a multinomial logistic model using bioclimatic predictors to allocate change spatially. The calibrated model reached an overall accuracy of 0.807 and an area under the receiver operating characteristic curve of 0.912, values that spatial autocorrelation renders optimistic. Under two contrasting Shared Socioeconomic Pathways, baseline projections both showed páramo contraction and expansion of the productive mosaic. The strategy, represented through transition multipliers at three adoption levels, produced gross forest gains near 280 ha at medium adoption under both pathways, robust to perturbing every multiplier by 20 per cent. A slope, drainage and riparian-buffer feasibility index reduced these to 11.4 and 9.8 ha, around 4 per cent. Operational feasibility, rather than the emissions pathway, appears to limit delivery here.
Hydroclimatic variability and land-use and land-cover (LULC) change were investigated in the Ribeira de Iguape River Basin, Brazil, using precipitation records from 14 gauges and mean, maximum and minimum streamflow from 10 stations within 1960–2024, alongside annual LULC maps for 1985–2024. Hamed–Rao Mann–Kendall tests, Sen’s slopes and change-point analyses were complemented by temporal-dependence, interstation-consistency and false-discovery-rate diagnostics. After adjustment, seven precipitation stations exhibited increasing annual trends and one showed a decrease. The largest positive slopes occurred at Balsa do Cerro Azul (+101.5 mm decade−1), Guanhanha (+98.3 mm decade−1) and Capela da Ribeira (+90.1 mm decade−1). However, pronounced departures from regional reference series around 1981 at two neighbouring gauges preclude an unequivocal climatic interpretation. Streamflow responses were more localised. Declining maximum discharge at Juquiá (−16.2 m3 s−1 decade−1) was the only annual streamflow trend retained after adjustment, without a comparable downstream decline over common years. Urban and mining areas expanded, whereas forest cover initially declined and subsequently recovered partially, remaining below its 1985 extent. Land-cover proportions extracted within ten independently DEM-delineated upstream catchments yielded 228 estimable contemporaneous LULC–discharge correlations. Although 21 were nominally significant, none remained significant after Benjamini–Hochberg adjustment within the corresponding discharge-metric families or across the pooled set. These findings reveal spatially differentiated hydroclimatic and landscape trajectories, without establishing a causal contribution of LULC change to observed streamflow variability.
Land-use/land-cover (LULC) change affects the balance between settlements, agricultural land, and natural ecosystems, making its assessment important for land-use planning in mountainous regions. This study integrates Random Forest (RF) classification and Cellular Automata–Artificial Neural Network (CA-ANN) modeling to assess historical LULC change in Sarkaghat Tehsil, Himachal Pradesh, and generate baseline projections for 2031 and 2046. Landsat imagery from 2016, 2021, and 2026 was classified into six classes: Forest Land (FL), Agricultural Land (AL), Built-up Land (BUL), Barren Land (BL), Water Body (WB), and Grass Land (GL). Overall classification accuracy was 95%, 96%, and 93%, respectively, with Kappa coefficients of 0.91, 0.94, and 0.89. The 2026 map had the lowest accuracy; the lower agreement was particularly evident for Grass Land, which had a user’s accuracy of 0.83. Historical comparisons from 2016 to 2026 showed increases in BUL (0.46%), BL (0.04%), and GL (0.16%), and decreases in AL (0.45%) and FL (0.21%); WB changed negligibly. The CA–ANN simulation of 2026, evaluated against the independently classified 2026 map, achieved an overall accuracy of 97.898% and a Kappa coefficient of 0.965. Relative to 2016, the 2031 baseline projection indicates changes of FL 0.00%, AL −0.37%, BUL +0.27%, BL −0.03%, WB 0.00%, and GL +0.13%. For 2046, the corresponding changes are FL −0.01%, AL −0.37%, BUL +0.27%, BL −0.03%, WB 0.00%, and GL +0.14%. The projections are conditional on stable historical transition relationships and should be interpreted as baseline scenarios rather than certain forecasts. The integrated workflow provides a locally evaluated basis for assessing land transitions and informing land-use planning in Sarkaghat Tehsil.
