Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet, Bikash; Vodacek, Anthony

doi:10.3390/rs70606683

Cited by 40 publications

(26 citation statements)

References 86 publications

(105 reference statements)

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“…However, the heavy cloudiness problem in the tropical rainforests and mountain areas of the country [18,26] made it nearly impossible to retrieve the dynamic of NDVI and land cover change with satellite data for a whole year. Using the NDVI values from the dry season, when the cloudiness problem is less serious, would risk a biased C factor.…”

Section: Discussionmentioning

confidence: 99%

“…The western part of Rwanda is rarely cloud-free [26]. The radiometric normalization was performed on these images for atmospheric correction using the Environment for Visualizing Images ENVI software Version 5.1 (Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation, Boulder, CO, USA).…”

Section: Land Cover and Land Use Map Of Rwandamentioning

confidence: 99%

“…The radiometric normalization was performed on these images for atmospheric correction using the Environment for Visualizing Images (ENVI) software Version 5.1 (Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation, Boulder, CO, USA). The complete mosaic cloud-free image was obtained using the method of the cloud-shadow mask and the gap filling of masked areas with the data from the other recent images [26] acquired within the same year. The LCLU map of Rwanda was obtained using the maximum likelihood classification (MLC) algorithm developed in the ENVI software.…”

Section: Land Cover and Land Use Map Of Rwandamentioning

confidence: 99%

“…The results are expressed in an error matrix, which shows the overall and class accuracies and the number of ground truth points within each class. Kappa coefficient is a good indicator of accuracy that measures the overall agreement between reference data and the classified thematic map [26,35]. The results of performing a Kappa analysis is a Khat statistic (an estimate of Kappa) (Equation (2)) [36,37].…”

Section: Land Cover and Land Use Map Of Rwandamentioning

confidence: 99%

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Extent of Cropland and Related Soil Erosion Risk in Rwanda

et al. 2016

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Land conversion to cropland is one of the major causes of severe soil erosion in Africa. This study assesses the current cropland extent and the related soil erosion risk in Rwanda, a country that experienced the most rapid population growth and cropland expansion in Africa over the last decade. The land cover land use (LCLU) map of Rwanda in 2015 was developed using Landsat-8 imagery. Based on the obtained LCLU map and the spatial datasets of precipitation, soil properties and elevation, the soil erosion rate of Rwanda was assessed at 30-m spatial resolution, using the Revised Universal Soil Loss Equation (RUSLE) model. According to the results, the mean soil erosion rate was 250 t¨ha´1¨a´1 over the entire country, with a total soil loss rate of approximately 595 million tons per year. The mean soil erosion rate over cropland, which occupied 56% of the national land area, was estimated at 421 t¨ha´1¨a´1 and was responsible for about 95% of the national soil loss. About 24% of the croplands in Rwanda had a soil erosion rate larger than 300 t¨ha´1¨a´1, indicating their unsuitability for cultivation. With a mean soil erosion rate of 1642 t¨ha´1¨a´1, these unsuitable croplands were responsible for 90% of the national soil loss. Most of the unsuitable croplands are distributed in the Congo Nile Ridge, Volcanic Range mountain areas in the west and the Buberuka highlands in the north, regions characterized by steep slopes (>30%) and strong rainfall. Soil conservation practices, such as the terracing cultivation method, are paramount to preserve the soil. According to our assessment, terracing alone could reduce the mean cropland soil erosion rate and the national soil loss by 79% and 75%, respectively. After terracing, only a small proportion of 7.6% of the current croplands would still be exposed to extreme soil erosion with a rate >300 t¨ha´1¨a´1. These irremediable cropland areas should be returned to mountain forest to foster environmental sustainability or further sustainable alternative erosion control techniques may be applied, such as applying Vetiver Eco-engineering Technology due to its economical soil erosion control and stabilization of steep slopes and the construction of erosion control dams to absorb and break down excess runoff from unusually intense storms in various parts of the watersheds.

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Section: Discussionmentioning

confidence: 99%

Section: Land Cover and Land Use Map Of Rwandamentioning

confidence: 99%

Section: Land Cover and Land Use Map Of Rwandamentioning

confidence: 99%

Section: Land Cover and Land Use Map Of Rwandamentioning

confidence: 99%

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Extent of Cropland and Related Soil Erosion Risk in Rwanda

et al. 2016

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“…Some of the remote sensing efforts include the analysis of land cover change dynamics in Kigali, Rwanda (Akinyemi et al, 2016) using Landsat images and an intensity analysis framework. Besides, Basnet and Vodacek (2015) carried out a land cover tracking study in the cloud prone Lake Kivu region by combining Landsat imagery and ancillary data.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

Mugiraneza

Haas

Ban

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Mapping urbanization and ensuing environmental impacts using satellite data combined with landscape metrics has become a hot research topic. The objectives of the study are to analyze the spatio-temporal evolution of urbanization patterns of Kigali, Rwanda over the last three decades (from 1984 to 2015) using multitemporal Landsat data and to assess the associated environmental impact using landscape metrics. Landsat images, Normalized Difference Vegetation Index (NDVI), Grey Level Co-occurrence Matrix (GLCM) variance texture and digital elevation model (DEM) data were classified using a support vector machine (SVM). Eight landscape indices were derived from classified images for urbanization environment impact assessment. Seven land cover classes were derived with an overall accuracy exceeding 88% with Kappa Coefficients around 0.8. As most prominent changes, cropland was reduced considerably in favour of built-up areas that increased from 2,349 ha to 11,579 ha between 1984 and 2015. During those 31 years, the increased number of patches in most land cover classes illustrated landscape fragmentation, especially for forest. The landscape configuration indices demonstrate that in general the land cover pattern remained stable for cropland but it was highly changed in built-up areas. Satellite-based analysis and quantification of urbanization and its effects using landscape metrics are found to be interesting for grassroots and provide a cost-effective method for urban information production. This information can be used for e.g. potential design and implementation of early warning systems that cater for urbanization effects.

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High flexibility in diet and ranging patterns in two golden monkey (Cercopithecus mitis kandti) populations in Rwanda

Tuyisingize

Eckardt

Caillaud

et al. 2021

American J Primatol

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Many primates exhibit behavioral flexibility which allows them to adapt to environmental change and different habitat types. The golden monkey (Cercopithecus mitis kandti) is a little-studied endangered primate subspecies endemic to the Virunga massif and the Gishwati forest in central Africa. In the Virunga massif, golden monkeys are mainly found in the bamboo forest, while in the Gishwati forest they live in mixed tropical montane forest. Here we describe and compare the diet of golden monkeys in both fragments. Over 24 consecutive months from January 2017 we used scan sampling to record feeding and ranging behavior of two Virunga groups and one Gishwati group totaling ca. 240 individuals. We also examined the phenology of bamboo and fruit trees, key seasonal food plant species for the monkeys. Golden monkeys fed on more than 100 plant species. The Virunga groups were mostly folivorous (between 72.8% and 87.16% of the diet) and fed mostly on young bamboo leaves and bamboo shoots, while 48.69% of the diet of the Gishwati group consisted of fruit from 22 different tree and shrub species. Bamboo shoots and fruit are seasonally available foods and were consumed regularly throughout the period when they were available. Despite being the smallest of the three study groups, the Gishwati group had a larger home range area (150.07 ha) compared to both Virunga groups (25.24 and 91.3 ha), likely driven by the differences in availability and distribution of fruit and bamboo in the habitats. Like other blue monkey subspecies, golden monkeys appear to have a flexible dietary strategy enabling them to adjust diet and ranging behavior to local habitats and available food resources.Additional studies and continuing conservation efforts are needed to better understand how variation in feeding and ranging ecology affects reproduction, population growth, and carrying capacity.

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Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Cited by 40 publications

References 86 publications

Extent of Cropland and Related Soil Erosion Risk in Rwanda

Extent of Cropland and Related Soil Erosion Risk in Rwanda

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

High flexibility in diet and ranging patterns in two golden monkey (Cercopithecus mitis kandti) populations in Rwanda

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