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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

Thonfeld, Frank and Steinbach, Stefanie and Muro, Javier and Kirimi, Fridah (2020) Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis. Remote Sensing, 12 (7), p. 1057. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs12071057 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/12/7/1057

Abstract

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.

Item URL in elib:https://elib.dlr.de/135395/
Document Type:Article
Title:Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Thonfeld, FrankFrank.Thonfeld (at) dlr.dehttps://orcid.org/0000-0002-3371-7206
Steinbach, StefanieITC, University of TwenteUNSPECIFIED
Muro, JavierUNSPECIFIEDUNSPECIFIED
Kirimi, FridahUNSPECIFIEDUNSPECIFIED
Date:25 March 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI :10.3390/rs12071057
Page Range:p. 1057
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:land-use/land-cover change; robust change vector analysis; Kilombero; wetland; food production; random forest; multitemporal metrics; Landsat; post-classification comparison
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Kraus, Dr. Tanja
Deposited On:24 Jun 2020 11:16
Last Modified:26 Jun 2020 14:27

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