Fakultät Agrarwissenschaften

Permanent URI for this communityhttps://hohpublica.uni-hohenheim.de/handle/123456789/9

Die Fakultät entwickelt in Lehre und Forschung nachhaltige Produktionstechniken der Agrar- und Ernährungswirtschaft. Sie erarbeitet Beiträge für den ländlichen Raum und zum Verbraucher-, Tier- und Umweltschutz.

Homepage: https://agrar.uni-hohenheim.de/

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Evaluating topsoil salinity via geophysical methods in rice production systems in the Vietnam Mekong Delta
(2023) Nguyen, Van Hong; Germer, Jörn; Asch, Folkard
The Vietnam Mekong Delta (VMD) is threatened by increasing saltwater intrusion due to diminishing freshwater availability, land subsidence, and climate change induced sea level rise. Through irrigation, saltwater can accumulate in the rice fields and decrease rice production. The study aims at evaluating topsoil salinity and examining a potential link between topsoil salinity and rice production systems in a case study in the Tra Vinh province of the VMD. For this, we applied two geophysical methods, namely, 3D electrical resistivity tomography (ARES II) and electromagnetic induction (EM38‐MK2). 3D ARES II measurements with different electrode spacings were compared with EM38‐MK2 topsoil measurements to evaluate their respective potential for monitoring topsoil salinity on an agricultural scale and the relationship between land‐use types and topsoil salinity. Results show that EM38‐MK2 is a rapid and powerful tool for obtaining high‐resolution topsoil salinity maps for rice fields. With ARES II data, 3D maps up to 40 m depth can be created, but compared with EM38‐MK2 topsoil maps, topsoil salinity was underestimated due to limitations in resolution. Salt contamination of above 300 mS m−1 was found in some double‐cropped rice fields, whereas in triple‐cropped rice fields salinity was below 200 mS m−1. Results clearly show a relation between topsoil salinity and proximity to the saline water sources; however, a clear link between rice production and topsoil salinity could not be established. The study proved that geophysical methods are useful tools for assessing and monitoring topsoil salinity at agricultural fields scale in the VMD.
An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems
(2024) Ojwang, Gordon O.; Ogutu, Joseph O.; Said, Mohammed Y.; Ojwala, Merceline A.; Kifugo, Shem C.; Verones, Francesca; Graae, Bente J.; Buitenwerf, Robert; Olff, Han
Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

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