Monitoring Land Cover changes in the tropical high forests using multi-temporal remote sensing and spatial analysis techniques

Lossou, Etse; Owusu-Prempeh, Nat; Agyemang, Godwin

doi:10.1016/j.rsase.2019.100264

Cited by 20 publications

(20 citation statements)

References 3 publications

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“…The highest number of samples were taken from bamboo land cover class in order to increase the classi cation accuracy. For 1985 and 2001 imageries, classi cation was undertaken with the help of high resolution Google Earth images, knowledge of elders, NDVI maps and color visualization and interpretation of the raw images (Lossou et al 2019). For classifying 2019 Landsat imagery, eld survey, NDVI maps and Google Earth datasets were used to collect training samples (Fig 3).…”

Section: Image Pre-processingmentioning

confidence: 99%

Spatio-temporal bamboo forest dynamics in the Lower Beles River Basin, north-western Ethiopia

Abebe

Minale

Teketay

2021

Remote Sensing Applications: Society and Environment

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Introduction: Bamboo forests, which are an integral part of the eco-system and an important source of socio-economic life for rural communities in the vast savannah lowlands of Ethiopia, are experiencing signi cant changes. Therefore, examining bamboo forest cover changes and identifying responsible drivers for the changes are of the greatest importance for sustainable management of these useful resources. This study was intended to examine the spatiotemporal bamboo forest dynamics in the Lower Beles River Basin, north-western Ethiopia.Method: A combination of pixel-based hybrid classi cation techniques and Normalized Difference Vegetation Index (NDVI) was employed to analyze bamboo forest cover changes from 1985 -2019. Focus group discussions, questionnaire, key informant interview and observation were used to identify the drivers for bamboo forest cover change.Results: The study ndings indicate that bamboo has experienced signi cant spatio-temporal change over the study period (1985 -2019) in the study District.In the base year (1985), bamboo covered 5.1% (5,277.1 ha) of the study area. Signi cant decline in bamboo forest had occurred in 2001 when the bamboo forests suffered the greatest devastation and shirked to 1.6%. In 2019, the bamboo has been rehabilitated from degradation and increased signi cantly.However, the net change over the study periods was negative where the bamboo forests declined by 0.8% (831.14 ha). Conclusion:In conclusion, the observed changes in bamboo forest cover was driven by an interplay of multiple factors. Agricultural land expansion, wild re, free grazing, lack of regulatory mechanisms and improper harvesting and expansion of settlement areas were the top ve drivers respectively while con ict, mass owering and tenure contributed for the change. Therefore, the bamboo forests deserve great attention, and the results from this study imply the need for the concerted efforts of stakeholders for sustainable management, utilization and conservation of the bamboo resources.

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Section: Image Pre-processingmentioning

confidence: 99%

Spatio-temporal bamboo forest dynamics in the Lower Beles River Basin, north-western Ethiopia

Abebe

Minale

Teketay

2021

Remote Sensing Applications: Society and Environment

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“…NDVI, EVI, and reflectance data are still the three main types of satellite data for forest mapping, making full use of the implied phenological information in NDVI and EVI is important [46]. In the development of remote sensing mapping, it has been shown that adding other data sources such as radar data, hyperspectral data, and so on, can provide new possibilities for surface coverage mapping [47][48][49][50].…”

Section: Challenges and Prospect Of Forest Mapping At Large Scalementioning

confidence: 99%

Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

et al. 2021

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Accurate and up-to-date forest monitoring plays a significant role in the country’s society and economy. Many open-access global forest datasets can be used to analyze the forest profile of countries around the world. However, discrepancies exist among these forest datasets due to their specific classification systems, methodologies, and remote sensing data sources, which makes end-users difficult to select an appropriate dataset in different regions. This study aims to explore the accuracy, consistency, and discrepancies of eight widely-used forest datasets in Myanmar, including Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020. Firstly, accuracy assessment is conducted by using 934 forest and non-forest samples with four different years. Then, spatial consistency of these eight datasets is compared in area and spatial distribution. Finally, the factors influencing the spatial consistency are analyzed from the aspects of terrain and climate. The results indicate that in Myanmar the forest area derived from GlobeLand30 has the best accuracy, followed by FROM-GLC10 and FROM-GLC2017. The eight datasets differ in spatial detail, with the mountains of northern Myanmar having the highest consistency and the seaward areas of southwestern Myanmar having the highest inconsistency, such as Rakhine and the Ayeyarwady. In addition, it is found that the spatial consistency of the eight datasets is closely related to the terrain and climate. The highest consistency among the eight datasets is found in the range of 1000–3500 m above sea level and 26°–35° slope. In the subtropical highland climate (Cwb) zone, the percentage of complete consistency among the eight datasets is as high as 60.62%, which is the highest consistency among the six climatic zones in Myanmar. Therefore, forest mapping in Myanmar should devote more effort to low topography, seaward areas such as border states like Rakhine, Irrawaddy, Yangon, and Mon. This is because these areas have complex and diverse landscape types and are prone to confusion between forest types (e.g., grassland, shrub, and cropland). The approach can also be applied to other countries, which will help scholars to select the most suitable forest datasets in different regions for analysis, thus providing recommendations for relevant forest policies and planning in different countries.

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“…Gaps in satellite coverage often result in information misinterpretations or incomplete characterization of ecological variables. However, modern developments have incorporated algorithms that remove or reduce cloud cover interference from the satellite images to provide clearer data (Lossou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A Structural Equation Modeling Approach to Disentangling Regional-Scale Landscape Dynamics in Ghana

Peeling

Singh

Judge

2022

Front. Environ. Sci.

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Land cover (LC) change is an integrative indicator of changes in ecosystems due to anthropogenic or natural forcings. There is a significant interest in the investigation of spatio-temporal patterns of LC transitions, and the causes and consequences thereof. While the advent of satellite remote sensing techniques have enhanced our ability to track and measure LC changes across the globe, significant gaps remain in disentangling specific factors that influence, or in certain cases, are influenced by, LC change. This study aims to investigate the relative influence of regional-scale bioclimatology and local-scale anthropogenic factors in driving LC and environmental change in Ghana. This analysis builds upon previous research in the region that has highlighted multiple drivers of LC change in the region, especially via drivers such as deforestation, urbanization, and agricultural expansion. It used regional-scale remotely sensed, demographic, and environmental data for Ghana across 20 years and developed path models on causal factors influencing LC transitions in Ghana. A two-step process is utilized wherein causal linkages from an exploratory factor analysis (EFA) are constrained with literature-based theoretical constructs to implement a regional-scale partial least squares path model (PLSPM). The PLSPM reveals complex interrelationships among drivers of LC change that vary across the geography of Ghana. The model suggests strong effects of local urban expansion on deforestation and vegetation losses in urban and peri-urban areas. Losses of vegetation are in turn related to increases in local heating patterns indicative of urban heat island effects. Direct effects of heat islands are however masked by strong latitudinal gradients in climatological factors. The models confirm that decreases in vegetation cover results in increased land surface albedo that is indirectly related to urban and population expansion. These empirically-estimated causal linkages provide insights into complex spatio-temporal variations in potential drivers of LC change. We expect these models and spatial data products to form the basis for detailed investigations into the mechanistic underpinnings of land cover dynamics across Ghana. These analyses are aimed at building a template for methods that can be utilized to holistically design spatially-disaggregated strategies for sustainable development across Ghana.

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Monitoring Land Cover changes in the tropical high forests using multi-temporal remote sensing and spatial analysis techniques

Cited by 20 publications

References 3 publications

Spatio-temporal bamboo forest dynamics in the Lower Beles River Basin, north-western Ethiopia

Spatio-temporal bamboo forest dynamics in the Lower Beles River Basin, north-western Ethiopia

Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

A Structural Equation Modeling Approach to Disentangling Regional-Scale Landscape Dynamics in Ghana

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