Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data

Al‐Hamdan, Mohammad Z.; Oduor, Phoebe; Flores, Africa; Kotikot, Susan M.; Mugo, Robinson; Ababu, Jaffer; Farah, H.

doi:10.1016/j.jag.2017.04.007

Cited by 31 publications

(15 citation statements)

References 31 publications

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“…We extrapolated the land cover zones where we collected the data and delimited five management zones (MZ): MZ A, MZ B, MZ C, MZ D, and MZ E. The numbers (1)(2)(3)(4) in the management zone names represent the municipalities that administer the forest. The zones that fall into the same category and present the same characteristics were assigned the same numbering ( Figure 7).…”

Section: Forest Management Zones For Miombo In Huambo Provincementioning

confidence: 99%

“…Although abundant information regarding certain aspects of African forests is available, including forest cover change [4][5][6][7][8], forest gains, and forest management guidelines, there is little information available on forest stand structures and dynamics. There is a need to strengthen and integrate national institutions to increase the capacity for countries in Africa to enforce forest laws and control the drivers of deforestation and forest degradation.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Patterns of Trees Species in Miombo Forest and Management Perspectives for Sustainable Production—Case Study in Huambo Province, Angola

Chiteculo

Surový

2018

Forests

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This paper aims to assess important forest parameters, including tree density, diameter at breast height (DBH), and age distribution, investigate the dynamic growth of miombo tree species, and employ this information to design a management framework in miombo forests. The delineation of the management zones was based on unsupervised land cover classification that began with three zones where ground data was collected and increased to five zones. Eighteen circular plots (25.2 m radius) were randomly distributed over the study areas to assess the current situation and potential growth patterns for each species. The patterns of the six most representative tree species of miombo in Angola were described and we used KORFiT 2.4 software to fit data and develop growth curves for at least three miombo species. Growth function fitness was evaluated by root mean squared error (RMSE), coefficient of determination (R 2 ), significance of the parameters (p < 0.05), and Akaike's information criterion (AIC). The diameter distribution of miombo tree species resembled a typical distribution of uneven-aged forest stands; higher DBH classes had lower abundances of tree species. Logistic and Gompertz growth functions were the best fits for miombo tree species. Brachystegia spiciformis Benth., is suggested as a potential species for timber management in the region because they displayed high growth potential to more quickly reach an assumed minimum DBH of 20 cm. This study concluded that miombo forest stands present an irregular structure in which DBH distribution illustrated only two patterns: many small stems and a bimodal forest structure.

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Section: Forest Management Zones For Miombo In Huambo Provincementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Patterns of Trees Species in Miombo Forest and Management Perspectives for Sustainable Production—Case Study in Huambo Province, Angola

Chiteculo

Surový

2018

Forests

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“…(17) The use of satellite images requires geometric and orthometric correction for the interpretation and digitalization of the images. (18,21) Maps with different themes such as land-use, cadastral, digital, ecological zoning, topographic, and forest maps, as well as land-use category statistics, can be used for geometric and orthometric correction.…”

Section: Methods Of Extracting Spatial Information By Using Satellite mentioning

confidence: 99%

Analysis of Land Use, Land-use Change, and Forestry (LULUCF) and Construction of Statistics in Korea

Yu¹,

Shin²,

Ahn³

2019

Sensors and Materials

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In this study, we analyze land use, land-use change, and forestry (LULUCF) in Korea, referring to the Intergovernmental Panel on Climate Change (IPCC) Good Practice Guidance (GPG) (2003), and propose a method for the systematic construction of statistics for LULUCF. Land-use changes across six categories, including settlement, forest, cropland, grassland, wetland, and other land types, were analyzed using digital topographic, land cover, and land zone maps from the Korea Land Information System (KLIS) using Landsat TM satellite images as primary data. The results of this study indicate that a classification system for each LULUCF category should be established to improve the statistical accuracy of this data and spatial big data applications for land-use analysis should be developed. Furthermore, a method is proposed for the systematic construction of statistics for LULUCF to determine the Korean national greenhouse gas inventory. An explanation of the recommended approach, including gathering satellite image data, LULUCF classifications, and establishing land-use change statistics, is presented. To fulfill the emission reduction targets set in the Paris Agreement, these greenhouse gas emission statistics are essential for different sectors to establish the diverse policies and action plans required to be effective. This will make the national-level monitoring of LULUCF more efficient, leading to a significant reduction in greenhouse gas emission.

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“…MODIS normal differential vegetation index (NDVI) time series data for 2005 and 2015 (h25v05 and h26v05) are derived from the National Aeronautics and Space Administration Earth Observing System at a spatial resolution of 250 m with 15‐day intervals (Al‐Hamdan et al, ). The reprojection (from sinusoidal projection to WGS84/Albers projection), format conversion (from HDF to GeoTIFF), and images mosaic for the above datasets are conducted with the tool of MODIS Reprojection Tool.…”

Section: Methodsmentioning

confidence: 99%

Dynamic monitoring of soil erosion in the upper Minjiang catchment using an improved soil loss equation based on remote sensing and geographic information system

et al. 2018

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The upper Minjiang catchment has suffered intensive soil erosion due to frequent geologic hazards and its fragile ecosystem zone with steep slopes. This study fully considers the topographic features and surface cover and introduces improved methods of K, LS, and C to establish a soil loss equation for the upper Minjiang catchment based on remote sensing and geographic information system. Spatial–temporal change patterns of soil erosion intensity and its driving mechanisms are then analyzed and discussed. Results show that (a) the soil erosion modulus of the upper Minjiang catchment for 2005 and 2015 were 1,577.29 t km−2 a−1 and 1,619.77 t km−2 a−1, both of which belong to a level of mild erosion. The slight and mild erosion zones covered the largest area and were widely distributed in the northern part of the study area, whereas zones of intensive and severe erosion were mostly concentrated in Wenchuan County and the lower reaches of the Heishui and Zagunao Rivers. (b) During 2005–2015, changes in erosion intensity showed a trend of “overall stability, local deterioration.” Zones of mild, moderate, and intensive increase were mainly concentrated in the Longmen Mountain Fault Zone, such as southern Maoxian and western Wenchuan Counties. (c) Returning cultivated land to forest and grasslands greatly reduced the erosion intensity and erosion amount, whereas geologic hazards aggravated the soil erosion condition. Zones with a slope of <35° had a positive relationship with soil erosion intensity; these areas are crucial control areas for soil preservation and containing soil loss. In addition, grassland is more effective in conserving soil and water than forestland in the upper Minjiang catchment in areas of steep terrain. These results provide an important reference for estimating soil loss intensity in southwest mountainous regions of China, particularly in the Hengduan Mountains, and greatly contribute to the planning of soil and water conservation.

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Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data

Cited by 31 publications

References 31 publications

Dynamic Patterns of Trees Species in Miombo Forest and Management Perspectives for Sustainable Production—Case Study in Huambo Province, Angola

Dynamic Patterns of Trees Species in Miombo Forest and Management Perspectives for Sustainable Production—Case Study in Huambo Province, Angola

Analysis of Land Use, Land-use Change, and Forestry (LULUCF) and Construction of Statistics in Korea

Dynamic monitoring of soil erosion in the upper Minjiang catchment using an improved soil loss equation based on remote sensing and geographic information system

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