An Assessment of Land Use/Land Cover Changes in a Section of Niger Delta, Nigeria

Abbas, Idowu Innocent

doi:10.5923/j.fs.20120206.02

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…This has led to continuous changes in both land-cover and land-use patterns of the region. It has also been shown that expansion in the oil exploration and exploitation activities had increased pressure with heavy impact on the natural ecosystems as a result of oil spill incidences (Abbas 2012). Records have shown that most of the oil pollution from oil operations in the regions has remained in the environment for many years without clean-up and yet to recover the lost natural vegetation (UNEP 2011).…”

Section: Land Use and Land Covermentioning

confidence: 99%

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

Adamu

Tansey

Ogutu

2016

International Journal of Remote Sensing

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The aim of this article is to investigate and test the influence of oil spill volume and time gap (number of days between oil spill events and image acquisition date) on normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). This was carried out to determine the effect of these factors on vegetation condition affected by the oil spill. Based on regression analysis, it was shown that increase in the volume of oil spill resulted in increased deterioration of vegetation condition (estimated using NDVI and NDWI) in the study site. The study also tested how the length of time gap between the oil spill and image acquisition date influences the detectability of impacts of oil spill on vegetation. The results showed that the length of time between image acquisition and oil spill influenced the detectability of impacts of oil spill on vegetation condition. The longer the time between the date of image acquisition and the oil spill event, the lower the detectability of impacts of oil spill on vegetation condition. The NDVI seemed to produce better results than the NDWI. In conclusion, time and volume of oil spill can be important factors influencing the detection of pollution using vegetation indices (VIs) in an oil-polluted environment.ARTICLE HISTORY

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Section: Land Use and Land Covermentioning

confidence: 99%

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

Adamu

Tansey

Ogutu

2016

International Journal of Remote Sensing

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“…The Niger Delta also has large oil and gas deposits which have been heavily exploited for decades [48], contributing substantially to mangrove forest loss [14]. The expansion of the oil industry typically involves the creation of canals for exploration and access, causing contamination of freshwater systems with seawater and destroying local ecosystems [49]. In addition, since the 1970's there have been multiple unrecovered oil spills [49], causing widespread damage to the mangrove forest [48].…”

Section: Niger Delta Nigeriamentioning

confidence: 99%

“…The expansion of the oil industry typically involves the creation of canals for exploration and access, causing contamination of freshwater systems with seawater and destroying local ecosystems [49]. In addition, since the 1970's there have been multiple unrecovered oil spills [49], causing widespread damage to the mangrove forest [48]. Estimates suggest that between 9 and 13 million barrels of oil have been spilled in the Niger Delta since 1958 [50].…”

Section: Niger Delta Nigeriamentioning

confidence: 99%

“…Despite the ecological significance of the Niger Delta, mangroves and the potential negative effects of oil and timber exploitation, the region is generally under-studied. While several studies have been carried out which utilise remote sensing [12,33,48,49], these have been limited to comparisons between single images, offering change analysis on a five to ten year scale. This limitation can be attributed to the high cloud cover in the region, which makes it difficult to find suitable images for comparison (Figure 3).…”

Section: Niger Delta Nigeriamentioning

confidence: 99%

“…They reported an overall classification accuracy of 81.6%, however, accuracy for the mangrove classes was only 35-64%. Other studies in this region either do not report accuracy (e.g., [33,49] or only look at change rather than specific classes [12]. Previous studies have also been limited to using only single images, often decades apart (e.g., [12,33,48,49]) and no analysis was found using satellite imagery beyond 2013.…”

Section: Niger Delta Nigeriamentioning

confidence: 99%

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Evaluation of the Continuous Monitoring of Land Disturbance Algorithm for Large-Scale Mangrove Classification

et al. 2021

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Mangrove forests are of high biological, economic, and ecological importance globally. Growing within the intertidal zone, they are particularly vulnerable to the effects of climate change in addition to being threatened on local scales by over-exploitation and aquaculture expansion. Long-term monitoring of global mangrove populations is therefore highly important to understanding the impact of these threats. However, data availability from satellites is often limited due to cloud cover. This problem can be mitigated using a season-trend modelling approach such as Continuous Monitoring of Land Disturbance (COLD). COLD operates by using every available observation on a pixel-wise basis, removing the need for whole cloud free images. The approach can be used to better classify land cover by taking into account the underlying seasonal variability, and can also be used to extrapolate between data points to obtain more accurate long term trends. To demonstrate the utility of COLD for global mangrove monitoring, we applied it to five study sites chosen to represent a range of mangrove species, forest types, and quantities of available data. The COLD classifier was trained on the Global Mangrove Watch 2010 dataset and applied to 30 years of Landsat data for each site. By increasing the period between model updates, COLD was successfully applied to all five sites (2253 scenes) in less than four days. The method achieved an overall accuracy of 92% with a User’s accuracy of 77% and a Dice score of 0.84 for the mangrove class. The lowest User’s accuracy was for North Kalimantan (49.9%) due to confusion with mangrove palms. However, the method performed extremely well for the Niger Delta from the 2000s onwards (93.6%) despite the absence of any Landsat 5 data. Observation of trends in mangrove extent over time suggests that the method was able to accurately capture changes in extent caused by the 2014/15 mangrove die-back event in the Gulf of Carpentaria and highlighted a net loss of mangroves in the Matang Forest Reserve over the last two decades, despite ongoing management. COLD is therefore a promising methodology for global, long-term monitoring of mangrove extent and trends.

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Changing landscapes: habitat monitoring and land transformation in a long-time used Mediterranean coastal wetland

Tomaselli

Mantino

Tarantino

et al. 2022

Wetlands Ecol Manage

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Zone Umide della Capitanata, located in the northeastern part of the Apulia Region, is one of the most extensive coastal wetlands of the Italian peninsula and one of the largest components of the Mediterranean wetland system. Despite its high ecological importance, this site has been undergoing a variety of pressures intensified in recent decades. This study analyzes and evaluates the changes occurred in this area between 2010 and 2020. Land cover and habitat maps were performed by photointerpretation and on-site surveys, and classified according to the FAO-LCCS and EUNIS taxonomies, respectively. To focus on local dynamics, four subset areas were analyzed separately. A set of landscape metrics was computed to analyze the landscape structure. The anthropogenic pressures affecting the study area were described through the Driving Forces-Pressures-State-Impact-Response (DPSIR) conceptual framework. Numerous changes were identified, deriving from both finest thematic redefinition and anthropogenic pressures. Both class conversions and class modifications were identified and quantified by means of transition matrices. Most of the observed conversions were borne by classes belonging to saltmarshes and to coastal dune systems. In particular, landscape configuration of coastal dune classes was well highlighted by a set of specific landscape metrics. Agriculture practices and changes in water flow pattern turned out to be the main driving forces exerting pressures on these natural systems. Significant differences were found between the four subsets under analysis, thus indicating that different management strategies lead to different levels of conservation.

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An Assessment of Land Use/Land Cover Changes in a Section of Niger Delta, Nigeria

Cited by 15 publications

References 5 publications

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

Evaluation of the Continuous Monitoring of Land Disturbance Algorithm for Large-Scale Mangrove Classification

Changing landscapes: habitat monitoring and land transformation in a long-time used Mediterranean coastal wetland

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