Land Use Land Cover Dynamics of Oba Hills Forest Reserve, Nigeria, Employing Multispectral Imagery and GIS

Land use Land cover (LULC) has undergone progressive changes worldwide over the years. However, there is limited information available about these changes in Oba Hills Forest Reserve, Nigeria. The existing spatial analysis of the forest excluded important land use classes like settlements. Therefore, this study aimed at assessing the dynamics of LULC in Oba Hills Forest Reserve between 1987 and 2019. Images from Landsat 5, Landsat 7, and Landsat 8 for the years 1987, 2001, 2013, and 2019 were obtained and subjected to preprocessing and classification using the maximum likelihood algorithm, change detection, and Normalized Differential Vegetation Index (NDVI). The coordinates of specific benchmark locations and other points were acquired for ground-truthing and developing Digital Elevation Model (DEM). Three distinct LULC classes were identified: forest, bare land (including open spaces, agriculture, rocks, and grasslands), and built-up areas. The forest cover in the reserve gradually decreased from 56% in 1987 to 47% in 2019, resulting in a total area loss of 455.4 hectares. Correspondingly, the other LULC classes experienced exponential expansion. Bare land increased from 44% in 1987 to 52% in 2019, while the built-up area expanded by 57.28 hectares. These changes are attributed to prevalent anthropogenic activities such as agriculture, grazing, logging, firewood collection, and population growth within the catchment area. The declining NDVI values in the forest reserve, from 0.52 to 0.44 within the years of assessment, further substantiated the substantial loss of forest cover. The DEM and topographical map highlighted notable steep slopes and elevations of up to over 550 m above sea level (asl) within the reserve, which have implications for forest growth and dynamics. In conclusion, this study reveals extensive rates of forest cover changes into bare land, primarily for agriculture, and settlements, and offers further recommendations to reverse the trend.

Modelling joint distribution of tree diameter and height using Frank and Plackett copulas

Bivariate distribution models are veritable tools for improving forest stand volume estimations.Their accuracy depends on the method of construction.To-date,most bivariate distributions in forestry have been constructed either with normal or Plackett copulas.In this study,the accuracy of the Frank copula for constructing bivariate distributions was assessed.The effectiveness of Frank and Plackett copulas were evaluated on seven distribution models using data from temperate and tropical forests.The bivariate distributions include:Burr III,Burr XII,Logit-Logistic,Log-Logistic,generalized Weibull,Weibull and Kumaraswamy.Maximum likelihood was used to fit the models to the joint distribution of diameter and height data of Pinus pinaster(184 plots),Pinus radiata(96 plots),Eucalyptus camaldulensis(85 plots)and Gmelina arborea(60 plots).Models were evaluated based on negative log-likelihood(-ΛΛ).The result show that Frank-based models were more suitable in describing the joint distribution of diameter and height than most of their Plackett-based counterparts.The bivariate Burr III distributions had the overall best performance.The Frank copula is therefore recommended for the construction of more useful bivariate distributions in forestry.

Analysis of Factors Instigating Land Use Conflicts in Selected Forest Reserves of Ondo State, Nigeria

Practice of agriculture and other none forestry uses in forest reserves often generates conflict owing to the former’s incompatibility with the latter. The need to identify the factors that triggers this conflict is germane to sustainable forest resources management. Thus, this paper report findings on various factors instigating land use conflicts in the high forest zone of Ondo state, Nigeria. Idanre and Oluwa forest reserves in the state were purposively selected for the study. Household counting was carried out in order to obtain a population in each settlement using participatory rural appraisal (PRA) technique. Fifty percent sampling intensity of individuals in settlements within and around the sites was used to select a total of 302 respondents for the study. Primary data were collected using both interview schedule guide and focus group discussion. Means, frequency counts and percentages were employed for descriptive analysis while factor analysis was used to identify the various factors instigating land use conflicts. Majority of the respondents were male (80.8%), married (86.1%), Yorubas (69.9%), farmers (69.6%) with a mean age of 43 ± 7.9 years. Boundary dispute (= 2.60), Resource control conflict (= 2.31), Inheritance conflict (= 2.11) as well as conflict between human/cultural and natural use (= 1.66) were the major types of conflicts identified in the study area. Four major factors that amplified the causes of land use conflicts in the forest reserves were: Cultural (settling land dispute cultural values between different ethnic group and access to land ownership);Economic (desperate for short term monetary gain , unpaid rent to landlords and unauthorised sale of common or collectively owned land);Social factors (increase in number of people and several people claiming the same land), and Political factors (breach of contract with government and changes in government policies on the use of forest).

Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

The rate of forest degradation and deforestation in Nigeria has been increasing over the years and is prominent in the southwestern parts. Despite the significant change and degradation observed in a lowland rainforest in the region—Ogbese Forest Reserve, there is a great dearth of information about the level of forest cover change. Therefore, this study determined the cover dynamics of the rainforest reserve over the epoch of 20 years using Geographic Information System and remote sensing techniques. Coordinates of the boundary and some other benchmark places within the forest reserve were obtained. Secondary data collection included: Landsat imageries of 1998, 2002 and 2018. An interview guide was used to obtain information from forest officials and locals of the surrounding communities to complement the spatial data obtained. Image classification was done using the maximum likelihood algorithm. The rate of change across the epochs was determined using the area of the land cover classes. The level of vegetation disturbance in the reserve was determined through Normalized Difference Vegetation Index. Five different forest cover classes were identified in the study area: forest, plantation, farmland, grassland, and bare land. The natural forest reduced significantly from 34.43 km2 (48%) in 1998 to 8.73 km2 (12%) in 2002 and was depleted further by 2018, while other cover classes increased. NDVI value also reduced from 0.25 to 0.13. Agriculture, among others, was observed as the main driver of forest degradation and deforestation in Ogbese Forest Reserve. The study concluded that the remaining forest (i.e. plantation) could also be depleted by 2025, as it decreases by −0.94 km2 per year if proper reforestation and management practices are not introduced.

Spatial Distribution of Soil Moisture Content and Tree Volume Estimation in International Institute of Tropical Agriculture Forest, Ibadan, Nigeria

The role of soil moisture in the survival and growth of trees cannot be over-emphasized and it contributes to the net productivity of the forest. However, information on the spatial distribution of the soil moisture content regarding the tree volume in forest ecosystems especially in Nigeria is limited. Therefore, this study combined spatial and ground data to determine soil moisture distribution and tree volume in the International Institute of Tropical Agriculture (IITA) forest, Ibadan. Satellite images of 1989, 1999, 2009 and 2019 were obtained and processed using topographic and vegetation-based models to examine the soil moisture status of the forest. Satellite-based soil moisture obtained was validated with ground soil moisture data collected in 2019. Tree growth variables were obtained for tree volume computation using Newton’s formular. Forest soil moisture models employed in this study include Topographic Wetness Index (TWI), Temperature Dryness Vegetation Index (TDVI) and Modified Normalized Difference Wetness Index (MNDWI). Relationships between index-based and ground base Soil Moisture Content (SMC), as well as the correlation between soil moisture and tree volume, were examined. The study revealed strong relationships between tree volume and TDVI, SMC, TWI with R2 values of 0.91, 0.85, and 0.75, respectively. The regression values of 0.89 between in-situ soil data and TWI and 0.83 with TDVI ascertain the reliability of satellite data in soil moisture mapping. The decision of which index to apply between TWI and TDVI, therefore, depends on available data since both proved to be reliable. The TWI surface is considered to be a more suitable soil moisture prediction index, while MNDWI exhibited a weak relationship (R2 = 0.03) with ground data. The strong relationships between soil moisture and tree volume suggest tree volume can be predicted based on available soil moisture content. Any slight undesirable change in soil moisture could lead to severe forest conditions.

Farmers’perceptions on cultivation and the impacts of climate change on goods and services provided by Garcinia kola in Nigeria

Background:Garcinia kola is an indigenous multipurpose tree species commonly found in the tropical rain forest zone of West and Central Africa.Providing economic,ecological,and socio-cultural benefits for people,they have potentials to improve the regional and local income generation to the farmers but the cultivation of the species is very limited in Nigeria.Methods:The study investigated cultivation and farmers’perceptions on the impacts of climate change on goods and services provided by G.kola in Nigeria.Structured questioners and interviews were used.The data obtained was analyzed using descriptive and inferential statistic such as frequency,percentage,chi-square,and multinomial logit regressions with SPSS Version 20 and R software Version 3.1.0.Results:The results show that farmers are presently not cultivating G.kola,and most of the available stands were inherited from grandparents.The farmers still believe it is only God that can make G.kola to germinate;however,information about the new improved methods of raising G.kola was not spread across farmers’communities.Over 93%of these farmers were not aware of these new methods,and the only means they raise the species is by picking the wildlings that regenerate naturally close to mother tree and are rarely found.The finding also shows that farmers are well aware of climate change and its impact on crop productivity is not clear to them.The result shows that five explanatory variables(age,gender,marital status,education level,household size and primary occupation)are the main factors significantly influencing farmers’perception of climate change and the cultivation of the G.kola.During interview section,the farmers reported variability of Harmattan season influences fruit production of the species;according to them,increase in Harmattan season usually leads to increase in fruiting of G.kola.Conclusions:Based on our findings,all the 215 respondent interviewed agree that climatic variability influences the availability of G.kola which will in turn have significant effects on the goods and services provided to the people.Efforts should be made at educating the rural farmers on propagation possibilities,potential ecosystem services,and the impact of climate change on multiple-purpose agroforestry species.

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.