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

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Münchener Straße 20, 82234 Weßling, Germany
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Department of Remote Sensing, University of Würzburg, Oswald-Külpe-Str. 86, 97074 Würzburg, Germany
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Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
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Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, 53113 Bonn, Germany
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Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, P.O. Box 62000 Nairobi, Kenya
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1057; https://doi.org/10.3390/rs12071057
Received: 29 January 2020 / Revised: 20 March 2020 / Accepted: 21 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Wetland Landscape Change Mapping Using Remote Sensing)
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. View Full-Text
Keywords: land-use/land-cover change; robust change vector analysis; Kilombero; wetland; food production; random forest; multitemporal metrics; Landsat; post-classification comparison land-use/land-cover change; robust change vector analysis; Kilombero; wetland; food production; random forest; multitemporal metrics; Landsat; post-classification comparison
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MDPI and ACS Style

Thonfeld, F.; Steinbach, S.; Muro, J.; Kirimi, F. Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis. Remote Sens. 2020, 12, 1057.

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