Remote sensing based assessment of land cover and soil moisture in the Kilombero floodplain in Tanzania

Wetlands provide important ecological, biological, and social-economic services that are critical for human existence. The increasing demand for food, arable land shortage and changing climate conditions in East Africa have created a paradigm shift from upland cultivation to wetland use due to their year-round soil water availability. However, there is need to control and manage the activities within the wetlands to ensure sustainable use while negating any negative effects caused by these activities. This is implemented through the decisions made by the land managers within the wetlands. Providing the users of the wetlands with scientific knowledge acts as a support tool for policy-making geared towards the sustainable use of the wetlands. The overall research contains two main components: First, the need for timely land cover maps at a reasonable scale, and secondly, the assessment of soil moisture as a major contributor to agricultural production. The objectives of the study were to generate land cover maps from multi-sensor optical datasets and to assess the performance of single-polarized Sentinel-1 Gray Level Co-occurrence Matrix (GLCM) texture and Principal Component Analysis (PCA) features by applying multiple classification algorithms in a floodplain in the Kilombero catchment. Furthermore, soil moisture spatial-temporal patterns over three hydrological zones was assessed, estimation of soil moisture from radar data and generation of soil moisture products from global products was investigated. The correlation of the merged products to Normalized Difference Vegetation Index (NDVI) measures was also investigated. RapidEye, Sentinel-2 and Landsat images were used in determining the areal extents of four major land cover classes namely vegetated, bare, water and built up. The acquisition period of the images ranges from August 2013 to June 2015 for the RapidEye images, December 2015 to August 2016 for the Sentinel-2 images and 2013 to 2016 Landsat-8 images were included in the land cover time series dynamic study. However, the major challenge arising was cloud coverage and hence Sentinel-1 images were tested in the application of Synthetic Aperture Radar (SAR) in wetland mapping. Variograms were used in spatial-temporal assessment of soil moisture data collected from three hydrological zones, riparian, middle and fringe. A roughness parameter was derived from a semi-empirical model. Soil moisture was retrieved from TerraSAR-X and RadarSAT-2 with the retrieved roughness parameter as an input in a linear regression equation. Triple collocation was applied in error assessment of the global soil moisture products prior to development of a merged product. Cross-correlation was applied in relating NDVI to soil moisture. Optical data (RapidEye, Landsat-8, and Sentinel-2) generated land cover maps used in assessing the land cover dynamics over time. The land cover ratios were related to depth to groundwater. As the depth to groundwater reduced in June the bare land coverage was 45-57% while that of vegetation was 34-47%. In December when the depth to groundwater was highest, bare land coverage was 62-69% while that of the vegetated area was 27-25%. This indicates that depth of groundwater and vegetation coverage responds to seasonality. During the dry season, 68-81% of the total vegetation class is within the riparian zone. In the classification of the SAR images, the overall accuracies for the single polarized VV images ranged from 54-76%, 60-81% and 61-80% for Random Forest (RF), Neural Network (NN) and Support Vector Machine (SVM) respectively. GLCM features had overall accuracies of 64-86%, 65-88% and 65-86% for RF, NN, and SVM respectively. PCA derived images had similar overall accuracies of 68-92% for NN, RF, and SVM respectively. The PCA images had the highest overall accuracy for the entire time series indicating that reduction in the number of texture features to layers containing the maximum variance improves the accuracy. The standard deviation of soil moisture was noted to increase with increasing soil moisture. Soil texture plays a key role in soil moisture retention. The riparian fields had a high water content explained by the high clay and organic matter content. A roughness parameter was derived and utilized in the retrieval of soil moisture from SAR resulting to R2 of 0.88- 0.92 between observed and simulated soil moisture values from co-polarized RadarSAT-2 HH and TerraSAR-X HH and VV. Merged soil moisture product from FEWSNET Land Data Assimilation System_NOAH (FLDAS_NOAH), ECMWF Re-Analysis Interim (ERA-Interim) and Soil Moisture and Ocean Salinity (SMOS) and FLDAS_Variable Infiltration Capacity (VIC), ERA-Interim and SMOS had similar patterns attributed to FLDAS_NOAH and FLDAS_VIC forced by the same precipitation product (RFE). Cross-correlation of Moderate-resolution Imaging Spectrometer (MODIS) NDVI and the merged soil moisture products revealed a 2-month lag of NDVI. Hence, the relationship is useful in determining the Start of Season from soil moisture products. In conclusion, the successful land cover mapping of the study area demonstrated the use of satellite imagery for wetland characterization. The vast coverage and frequent acquisitions of optical and microwave remotely sensed data additionally make the approaches transferable to other locations and allow for mapping at larger scales. Soil moisture assessment from point data revealed varied soil moisture patterns whereas global remotely sensed and modeled products rather provide complementary information about growing conditions, and hence a situational assessment tool of potential of physical availability dimension of food security. This study forms a baseline upon which additional monitoring and assessment of the Kilombero wetland ecosystem can be performed with the current results marked as a reference. Moreover, the study serves as a demonstration case of remote sensing based approaches for land cover and soil moisture mapping, whose results are useful to stakeholders to aid in the implementation of adapted production techniques for yield optimization while minimizing the unsustainable use of the natural resources.Feuchtgebiete erbringen wichtige ökologische, biologische und sozial-ökonomische Dienstleistungen, welche entscheidend für das menschliche Dasein sind. Der steigende Bedarf an Nahrung, der Mangel an landwirtschaftlichen Nutzflächen und die Veränderung der klimatischen Bedingungen in Ostafrika haben zu einem Paradigmenwechsel vom Anbau im Hochland hin zur Nutzung von Feuchtgebieten geführt. Allerdings sind Kontrolle und Management der Aktivitäten in Feuchtgebieten notwendig, um die nachhaltige Nutzung zu sichern und negative Effekte dieser Aktivitäten zu vermeiden. Die Implementierung erfolgt durch die Landverwalter in den Feuchtgebieten. Den Nutzern von Feuchtgebieten wissenschaftliche Erkenntnisse bereitzustellen dient als Hilfsmittel zur politischen Entscheidungsfindung für die nachhaltige Feuchtgebietsnutzung. Die Forschung im Rahmen der Dissertation beinhaltet zwei Hauptkomponenten: erstens den Bedarf an aktuellen Landbedeckungskarten auf einer angemessenen Skalenebene und zweitens die Erfassung der Bodenfeuchte als wichtiger Einflussfaktor auf die landwirtschaftliche Produktion. Das Ziel der Untersuchung war, Landbedeckungskarten auf Grundlage von multisensorischen optischen Daten zu erstellen und die Eignung der Textur der einfach polarisierten Sentinel-1 Grauwertmatrix (GLCM) sowie der einer Hauptkomponentenanalyse (PCA) bei Anwendung unterschiedlicher Klassifikationsalgorithmen zu beurteilen. Des Weiteren wurden raum-zeitliche Bodenfeuchtemuster über drei hydrologische Zonen hinweg modelliert, die Bodenfeuchte aus Radardaten abgeleitet sowie die Erstellung von Bodenfeuchteprodukten auf Basis von globalen Produkten untersucht. Die Korrelation der Bodenfeuchteprodukte mit dem Normalisierten Differenzierten Vegetationsindex (NDVI) wurde ebenfalls analysiert. RapidEye, Sentinel-2 und Landsat Bilder wurden genutzt um die räumliche Ausdehnung der vier Hauptklassen (Vegetation, freiliegender Boden, Wasser und Bebauung) der Landbedeckung zu ermitteln. Für die Zeitreihenanalyse der der Landbedeckungsdynamik wurden RapidEye-Daten von August 2013 bis Juni 2015, Sentinel-2-Bilder von Dezember 2015 bis August 2016 und Landsat-8-Bilder von 2013 bis 2016 verwendet. Die größte Herausforderung war jedoch die Wolkenbedeckung, weshalb die Anwendung von Synthetic Aperture Radar (SAR) für die Feuchtgebietskartierung getestet wurde. Die gemessene Bodenfeuchte wurde mittels Variogrammen für die drei hydrologischen Zonen (Uferzone, Mitte und Randgebiete) raum-zeitlich interpoliert. Ein Rauhigkeitsparameter wurde aus einem semi-empirischen Modell hergeleitet. Die Bodenfeuchte wurde aus TerraSAR-X und RadarSAT-2- Bildern unter Verwendung des Rauhigkeitsparameters als Eingangsgröße in einer linearen Regression abgeleitet. Vor der Zusammenführung der Produkte wurde das globale Bodenfeuchteprodukt mithilfe von dreifacher Kollokation auf Fehler überprüft. Die Kreuzkorrelation zwischen NDVI und Bodenfeuchte wurde berechnet. Optische Daten (RapidEye, Landsat-8 und Sentinel-2) wurden genutzt, um die zeitliche Dynamik der Landbedeckung zu bestimmen. Die Landbedeckungsverhältnisse wurde mit der Höhe des Grundwasserspiegels korreliert. Ein hoher Grundwasserstand im Juni resultierte in 45-57% unbedecktem Boden, während der Anteil der Vegetation 34-47% betrug. Im Dezember, als der Grundwasserspiegel seinen Tiefststand hatte, erhöhte sich der Anteil des freiliegenden Bodens auf 62-69% und der Anteil der Vegetation verringerte sich auf 27-25%. Das zeigt, dass Grundwasserspiegel und Vegetation saisonalen Schwankungen unterworfen sind. Während der Trockenzeit liegen 68-81% der gesamten als Vegetation klassifizierten Fläche innerhalb der Uferzone. In der Klassifikation der SAR-Bilder liegt die Gesamtgenauigkeit der einfach polarisierten VV-Bilder im Rahmen von 54-76%, 60-81% und 61-80%, entsprechend für Random Forest (RF), Neuronale Netze (NN) und Support Vector Machine (SVM). Die GLCM ergab eine Gesamtgenauigkeit von 64-86%, 65-88% und 65-86% für RF, NN und SVM. Die über eine PCA abgeleiteten Bilder erreichten eine ähnliche Genauigkeit von 68-92% für NN, RF und SVM. Die PCA-Bilder weisen die höchste Gesamtgenauigkeit der gesamten Zeitreihe auf, was darauf hinweist, dass eine Reduktion von Textureigenschaften auf Layer der maximalen Varianz enthalten, die Genauigkeit erhöht. Die Standardabweichung der Bodenfeuchte stieg mit zunehmender Bodenfeuchte. Die Bodentextur spielt dabei eine Schlüsselrolle für das Wasserhaltevermögen des Bodens. Die Uferzone wies einen hohen Wassergehalt auf, was durch den hohen Anteil von Ton und Humus zu erklären ist. Die beobachteten und simulierten Bodenfeuchtewerte von co-polarisierten RadarSAT-2 HH, TerraSAR-X HH und VV Daten korrelieren mit einem R2 von 0.88 - 0.92. Die zusammengesetzten globalen Bodenfeuchteprodukte von FLDAS_NOAH, ERA-Interim sowie SMOS und FLDAS_VIC, ERA-Interim und SMOS zeigen ähnliche Muster wie FLDAS_NOAH und FLDAS_VIC, was über die Verwendung desselben Niederschlagsproduktes (RFE) zu erklären ist. Die Kreuzkorrelation von MODIS NDVI und den zusammengeführten Bodenfeuchteprodukten ergab eine zeitliche Verzögerung des NDVI von zwei Monaten. Dieser Zusammenhang kann daher bei der Bestimmung des Saisonbeginns aus Bodenfeuchtigkeitsprodukten nützlich sein. Zusammengefasst hat die Studie gezeigt, wie Satellitenbilder zur Charakterisierung von Wetlands genutzt werden können. Die große Abdeckung und häufige Aufnahme der optischen und Mikrowellen-Fernerkundungsdaten ermöglichen darüber hinaus die Übertragung der Ansätze auf weitere Gebiete und Kartierung auf größeren Skalen. Die Punktmessungen zeigen kleinräumige Muster der Bodenfeuchte, während globale Fernerkundungsprodukte und Modelle Informationen über die Wachstumsbedingungen liefern und somit ein Bewertungsinstrument der Ernährungssicherheit darstellen können. Weiterhin bildet die Studie eine Basis, auf der ein weitergehendes Monitoring und eine Bewertung des Feuchtgebietsökosystems durchgeführt werden kann. Sie ist ein Beispiel für fernerkundungsbasierte Ansätze zur Landbedeckungs- und Bodenfeuchtekartierung; ihre Ergebnisse sind nützlich, um Akteuren bei der Implementierung von Produktionstechniken zu unterstützen, welche die Erträge maximieren und gleichzeitig die nicht nachhaltige Nutzung der natürlichen Ressourcen minimieren

Land use/land cover maps 1974, 1994, 2004, 2014 of the Kilombero catchment, Tanzania

The Kilombero catchment in Tanzania is an important area of recent development in East Africa. It harbors a large floodplain wetland, put under protection and designated a Ramsar site. Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In the underlying study, we assessed LULC changes of the Kilombero catchment in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics of Landsat data that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show a) only PCC changes, b) only spectral changes that do not affect the classification of a pixel, c) both types of change, or d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. Half of the Kilombero floodplain was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. This dataset contains LULC maps for 1974, 1994, 2004, and 2014 and class-specific per-pixel classification probabilities for each map. We also provide the data points used for the study. These data points were collected during field campaigns and complemented by interpreted high-spatial resolution Google Earth images following a systematic random sampling. For appropriate map display we provide QGIS style files associated with the outputs. This research was conducted at the University of Bonn, Germany, within the GlobE - Wetlands in East Africa project, which was funded by the German Federal Ministry of Education and Research (FKZ: 031A250 A-H) with additional funding provided by the German Federal Ministry of Economic Cooperation and Development

Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis

Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment

Spatial Distribution of Soil Hydrological Properties in the Kilombero Floodplain, Tanzania

Analysis and interpretation of soil properties dynamics is a keystone in understanding the hydrologic responses and yield potential of floodplain wetlands. This study characterizes the distribution and spatial trends of selected soil physical properties in the Kilombero floodplain, Tanzania. A total of 76 composite soil samples were taken from 0 to 20 cm and 20 to 40 cm depth in a regular grid design across three hydrological zones, related to flooding intensity defined as fringe, middle, and riparian during the rainy season of 2015. The samples were analyzed for soil texture, bulk density, organic carbon, and saturated hydraulic conductivity. Seasonal soil moisture content was monitored at depths of 10, 20, 30, and 40 cm, using 17 frequency domain reflectometry profile probes type PR2, installed at each hydrological zone for 18 months (March 2015–August 2016). Data were subjected to classical statistical and geostatistical analyses. Results showed significant (p < 0.05) differences in bulk density, texture, soil organic carbon (SOC), and saturated hydraulic conductivity (Ksat) across the hydrological zones. Bulk density showed a clear increasing trend towards the fringe zone. Mean Ksat was highest at the riparian zone (69.15 cm·d−1), and clay was higher in the riparian (20.3%) and middle (28.7%) zones, whereas fringe had the highest percentage of sand (33.7–35.9%). Geostatistical spatial results indicated that bulk density, silt, and SOC at 0–20 cm had intermediate dependence, whereas other soil properties at both depths had high spatial dependence. Soil moisture content showed a significant (p < 0.05) difference across the hydrological zones. The riparian zone retained the highest soil moisture content compared to the middle and fringe zone. The temporal soil moisture pattern corresponded to rainfall seasonality and at the riparian zone, soil moisture exhibited a convex shape of sloping curve, whereas a concave sloping curve for topsoil and for the middle zone at the subsoil was observed during the start of the dry season. Our results are seen to contribute to a better understanding of the spatial distribution of soil properties and as a reference for soil and water management planning in the floodplain

In Memoriam: Gunter Menz

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