Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa

Kirimi, Fridah; Thiong’o, Kuria; Gabiri, Geofrey; Diekkrüger, Bernd; Thonfeld, Frank

doi:10.1117/1.jrs.12.026027

Cited by 11 publications

(5 citation statements)

References 45 publications

(46 reference statements)

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“…The end result provided the spatiotemporal patterns of LUC in the study area. Figure 2 shows the land use change (LUC) mapping methodology flow chart employed in this study as modified from [46]. (ii) Socioeconomic survey methods, namely review of relevant literature from both published and unpublished sources to generate secondary data.…”

Section: General Methodological Approachmentioning

confidence: 99%

“…Seki et al [45] assessed land cover changes (1990, 1998, and 2011) in the Kibasira Swamp in the Kilombero valley, revealing an increase in agricultural land that affected trends of biodiversity. Kirimi et al [46] assessed seasonal land cover changes in the Kilombero floodplain to understand how land cover reflects the impact of water balance components. Based on these studies, the driving forces behind the observed land use changes and their relationship to LUC in KVFP have rarely been analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Land Use Change Trends and Their Driving Forces in the Kilombero Valley Floodplain, Southeastern Tanzania

2019

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Land use change (LUC) driven by human activities and natural factors has resulted in the global loss of native biodiversity and the alteration of ecological processes and services across different ecosystems. It is thus necessary to analyze the trends and driving factors that influence land use changes. In this study, moderate resolution Landsat images were freely downloaded from the United States Geological Survey (USGS) archives, analyzed using the random forest (RF) algorithm and mapped in ArcGIS 10.2 software to examine the LUC trends from 1990 to 2016 in the Kilombero valley floodplain (KVFP), Tanzania. Participatory rural appraisals (PRA) and household questionnaire surveys were used to assess the potential drivers of LUC. The results show that, from 1990 to 2016, the agricultural land and grassland increased by 11.3% and 13.3%, respectively, while the floodplain wetland area decreased from 4.6% in 1990 to 0.9% in 2016. Based on a questionnaire survey, the intensification of human activities was identified as the proximate driver while population growth, a growing market demand and price incentives for agricultural and forest products coupled with improved infrastructure and biophysical factors such as soil properties, climate variability and terrain characteristics were identified as the underlying drivers of LUC. However, there is interplay among these factors acting simultaneously as well as differently that influence land use changes. Based on these findings, future sustainable land management strategies should include the introduction of the alternative environmentally friendly sources of livelihood, such as beekeeping, the promotion of community participation and education on the importance of sustainable wetland management.

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Section: General Methodological Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land Use Change Trends and Their Driving Forces in the Kilombero Valley Floodplain, Southeastern Tanzania

2019

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“…Catchment-wide studies are rare, in particular in East Africa. LULC change in the Kilombero valley was addressed in several case studies [6,12,[17][18][19][20][21][22][23][24][25][26]. Only a few of these studies explored the whole catchment and only few provided an exhaustive evaluation of the generated LULC change maps.…”

Section: Introductionmentioning

confidence: 99%

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

Thonfeld

Steinbach²,

Muro

et al. 2020

Remote Sensing

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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.

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“…Using standardized precipitation index in the east coast of Tanzania, Hassan et al [5] also found that this region has recently been experiencing more than one drought episode per year. Because TDD shows an average of 2 to 6 months, and the dry period in the Kilombero catchment is usually between June and October [21,[53][54][55], this implies that sometimes drought spells might coincide with dry periods (as shown in Figure 4d).…”

Section: Findings On Drought Characteristicsmentioning

confidence: 99%

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

Wambura

Dietrich

2020

Water

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Understanding spatial drought characteristics is vital for planning adaptation and mitigation measures in river catchments. In many parts of the world, spatial drought information is not available due to lack of adequate evenly distributed data for spatial drought analyses. This study elucidates a spatial drought analysis in a data-scarce tropical catchment using remote sensing actual evapotranspiration (ET) and potential evapotranspiration (PET) data. Firstly, the time series of 690 images of remotely sensed ET and PET between the years 2000 and 2014 were spatially analyzed using the evapotranspiration deficit index (ETDI) approach to obtain ETDIs in the Kilombero River catchment (Tanzania). Then, spatio-temporal patterns of ETDIs were used to characterize drought frequency, total drought durations, total drought severity, and drought intensity. The frequency, durations, severity, and intensity of drought increased from the year 2000 towards 2014, causing substantial drought changes in the catchment. However, drought intensity revealed that those changes were mainly from no drought and mild drought to moderate drought. Between the years 2000 and 2014, no-drought areas and mild drought areas declined from 10% to 0% and from 42% to 19%, respectively, whereas moderate drought areas increased from 47% to 81% of the catchment size. Those changes of drought conditions were partly attributed to anthropogenic land cover change, especially in the southwest grasslands, and were partly attributed to meteorological factors in other parts of the catchment. This information is crucial for further land cover change and climate change investigations, as well as planning water and land resources in the Kilombero River catchment. Moreover, the study also demonstrates the potential of using publicly available remote sensing ET products and the ETDI approach for spatially characterizing drought in ungauged regions.

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Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa

Cited by 11 publications

References 45 publications

Land Use Change Trends and Their Driving Forces in the Kilombero Valley Floodplain, Southeastern Tanzania

Land Use Change Trends and Their Driving Forces in the Kilombero Valley Floodplain, Southeastern Tanzania

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

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

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