The Mojana region in Colombia is one of the most complex inland water deltas in the world as it receives inflows from important rivers in the country, including the Cauca river. The current climate change affects this unique natural environment and alters the water balance. During the latest “El Niño-Southern Oscillation” (ENSO) cold phase (La Niña) several floods occurred. The flood happened on 2 September 2021 was the longest event ever recorded in the Mojana region and severely impacted on local agricultural systems (crops, livestock-raising areas, etc.). However, whether and how these systems have recovered and been brought back to prior production level is unknown. Given that the recent past trend suggests that in near future other and more frequent flood events may happen, an analysis of the recovery process may unveil strategies for resilience. Because the region is isolated and poorly accessible, we analysed the whole collections of Earth Observation data (optical and SAR) from the Landsat missions, Sentinel-1 and Sentinel-2 in the period 1999- 2024. The time series analysis highlighted major transformations in the agricultural systems in the area affected by the flooding. The data were processed with machine learning (ML) for mapping affected areas, recovery assessment, and analyse of how surfaces evolved temporally. Cloud denoising of optical data with ML is essential in tropical countries, in order to contrast the low availability of cloud-free optical data. From a technical point of view, one of the conclusions is that the use of ML in dual-polarization SAR data helps to achieve better land classifications and contributes to improve accuracy and mapping products.
Pontederia crassipes, commonly known as water hyacinth (WH), is a highly invasive aquatic weed and caused significant ecological and economic impact across the world. Remediation action includes manual monitoring and removal which are often time consuming and expensive. This paper proposes the use of multi-temporal multi-spectral drone imagery for WH mapping and monitoring in Patancheru Lake, Hyderabad, India. The data collection was done in two steps: 1) multi-spectral drone imagery and 2) ground optical image capturing through an Android mobile application. Data was collected in regular interval starting from January 2021. Spectral bands were used to produce the WH detection and mapping. We compare spectral signature of clean and infested water for five different sites inside the lake. Multitemporal water quality samples of these sites were also collected together with drone data to analyse the effect of WH infestation on those parameters. The multispectral data was processed using an unsupervised machine learning classifier named expectation maximisation (EM) clustering to create a segmentation map indicating WH, water and other regions.
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