Land Use Cover Classification Research Articles

ASSESSMENT OF AGRO-ECOLOGICAL CHANGES IN THE KASSENA-NANKANA DISTRICT(S) AS A RESULT OF THE CONSTRUCTION OF THE TONO RESERVOIR USING MULTI-TEMPORAL IMAGERY

Land Use / Land Cover (LULC) changes has been progressive across the globe, causing the alteration and shifting of various land use/land cover features. These changes are caused by natural factors such as extreme environmental conditions, earthquakes, volcanic eruption, flooding and also human factors including agricultural expansion, urban and industrial development of infrastructure. The study combined spatial time series satellite data and field observations to observe the changes which have occurred in the land cover mapping between 1999 and 2007. Land cover features were categorized into Vegetation, Crop/farmland, Settlement, Bare land and Water body. Satellite imagery for the intervening period was used to assess the changes in LULC. LULC classes of Settlements and Bare land recorded an increase of 3.21%, and 33.1%, whereas Vegetation, Crop/farmland and Water decreased by 1.8%, 33% and 2.1% respectively. It was observed that the mean conversional change between 1999 and 2007 was 2148.55 hectares. A total land size of 9297.79 hectares did not undergo any change; conversion into Water body was 417.62 hectares, 7688.56 hectares was changed into Crop/farmland, 1137.76 hectares into Settlement, 337.17 hectares into Bare land, and 1161.65 hectares into Vegetation. It was also observed that the sharp increase in Settlement is as a result of rapid population growth and expansion of infrastructure.

Application of Parametric and Non Parametric Classifiers for Assessing Land Use/Land Cover Categories in Cocoa Landscape of Juaboso and Bia West Districts of Ghana

Satellite image classification has been used for long time in the field of remote sensing since classification results are used in environmental research, agriculture, climate change and natural resource management. The cocoa landscape of Ghana is complex and diverse in nature, composing of mixture of closed forest, open forest, settlements, croplands and cocoa farms which make mapping the landscape difficult. The purpose of this research is to assess and compare the classification performances of three machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and a statistical classification algorithm: Maximum Likelihood (ML) to know which classifier is best suited for mapping the cocoa landscape of Ghana using Juaboso and Bia West districts of Ghana as study area. A representative sampling approach was adopted to collect 1246 sample points for the various Land Use/Land Cover (LULC) types. These sample points were divided at random into 869 which form 70% for classification and 377 which constitute 30% of the total sample points for validation. The Stacked sentinel-2 image, classification data and validation data storing the identities of the LULC classes were imported in R to run supervised classification for each classifier. The classification results show that the highest overall accuracy and kappa statistics were produced by the support vector machine (86.47%, 0.7902); next is the artificial neural network (85.15%, 0.7700), followed by the random forest (84.08%, 0.7559) and finally the maximum likelihood (78.51%, 0.6668). The final LULC map produced under this study can be used to monitor cocoa driven deforestation especially in the gazetted forest and game reserves. This map will also be very useful in the national forest monitoring framework for the REDD + cocoa landscape project.

Assessing the Effects of Land Use/Land Cover Change on Groundwater Recharge in a Sudano-Sahelian Zone: Case of Koda Catchment, Mali, West Africa

Groundwater is the main source of water in the studied area; therefore, it is significantly requested in all the activities of the inhabitants. These natural resources are affected by some drivers especially Land Use/Land Cover (LULC) and Climate Change. A Land Use/Land Cover (LULC) dynamics study is crucial for any global environmental change evaluation. For instance, for a given place, its change could affect considerably water cycle components. Therefore, the knowledge of the effects of LULC on groundwater recharge is then the key in water resources management system, in particular for the decision makers of the Koda Catchment where the scarcity of the water availability for agriculture is real. The spatiotemporal variation of the different units of LULC present in the catchment has been examined in this study. The Envi 4.5 Software coupled with ArcGIS using the Supervised Classification method, was applied to subset Landsat images from 1990 to 2016. Five (5) major LULC categories, cultivated land, bare land, herbaceous savannah, shrubby savannah and degraded savannah, were identified in the catchment. In a parallel direction, the groundwater recharge has been estimated through the conceptual Gardenia model for the same period 1990-2016. The results showed that the portion of cultivated land and bare land increased (14.9% and 23.5% respectively) while, the portion of savannah decreased: herbaceous savannah by 24.4%, degraded savannah by 10.32% and Shrubby Savannah by 3.6%. Savannah areas in Koda catchment is converted to agricultural land and urban area due to human activities. The decline of 8.4% in groundwater recharge might become so far obvious in the future if the current rate of deforestation continues in the Koda catchment. There is a need to closely monitor the changes in LULC for sustainable development. The results of this study could help to well understand the recharge pattern across Koda catchment under a changing LULC.

Prioritization of Sub-Watersheds in Ruparel Watershed, Rajasthan Based on Morphometric and Land Use/Land Cover Analysis Using Remote Sensing and GIS

Watershed prioritization is considered as the most significant aspect in watershed resource management and development program. The present work attempts to prioritize seventeen sub-watersheds in Ruparel watershed of Alwar district of Rajasthan, India. For prioritization of sub-watersheds, morphometric and land use/land cover (LULC) analysis were performed using remote sensing and GIS. Base map of the study area has been derived from SOI toposheet on 1:50,000 scale whereas LULC mapping was done using IRS P6 LISS III data. Standard methods for drainage morphometry have been followed for computing morphometric parameters such as linear and shape for seventeen sub-watersheds and allotted ranks based on their relationship with erodibility and a compound value has been calculated for final ranking. Five main LULC categories were computed and were assigned priority ranks and subsequently a compound parameter was determined for final ranking. Integration of both morphometric and LULC results reveal that SBW5, SBW7, SBW12 and SBW16 are the common sub-watersheds that fall under high priority, SBW3 falls under Medium category and SBW11 comes under low priority. The results of the analysis can be used to identify the sub-watersheds which need immediate restoration and will eventually help in watershed resource management for sustainable development.

Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia

Mapping and quantifying the status of Land use/Land cover (LULC) changes and drivers of change are important for identifying vulnerable areas for change and designing sustainable ecosystem services. This study analyzed the status of LULC changes and key drivers of change for the last 30 years through a combination of remote sensing and GIS with the surveying of the local community understanding of LULC patterns and drivers in the Gubalafto district, Northeastern Ethiopia. Five major LULC types (cultivated and settlement, forest cover, grazing land, bush land and bare land) from Landsat images of 1986, 2000, and 2016 were mapped. The results demonstrated that cultivated and settlement constituted the most extensive type of LULC in the study area and increased by 9% extent. It also revealed that a substantial expansion of bush land and bare land areas during the past 30 years. On the other hand, LULC classes that has high environmental importance such as grazing land and forest cover have reduced drastically through time with expanding cultivated and settlement during the same period. The grazing land in 1986 was about 11.1% of the total study area, and it had decreased to 5.7% in 2016. In contrast, cultivated and settlement increased from 45.6% in 1986 to 49.5% in 2016. Bush land increased from 14.8 to 21% in the same period, while forest cover declined from 8.9 to 2% in the same period. The root causes for LULC changes in this particular area include population growth, land tenure insecurity, and common property rights, persistent poverty, climate change, and lack of public awareness. Therefore, the causes for LULC changes have to be controlled, and sustainable resources use is essential; else, these scarce natural resource bases will soon be lost and will no longer be able to play their contribution in sustainable ecosystem services.Article HighlightsForest cover and grazing lands declined rapidly.Fluctuating trends in cultivated and settlement, bush land and bare land.Population pressure and associated demand are the main causes behind LULC changes in the study area.

Evaluation and comparison of the earth observing sensors in land cover/land use studies using machine learning algorithms

The rapid transformation of land cover/land use (LCLU) is a strong indication of global environmental change. In order to monitor LCLU through maps, a significant dataset and robust technique are necessary. Thus, the primary objective of the current research is to evaluate and compare the efficiency of several notable satellite sensors including Landsat-8 (L-8), Sentinel-2 (S-2), Sentinel-1 (S-1), combined Sentinel-1 and Sentinel-2 (S-1-2), LISS III (L-3), and LISS IV (L-4) for LCLU mapping applying random forest (RF), logit boost (LB), stochastic gradient boosting (SGB), artificial neural network (ANN), and K-nearest neighbor (KNN) models. For this purpose, 300 samples for each of the six LCLU classes have been selected based on field survey and high resolution Cartosat-3 images. The classification accuracy namely producer accuracy (PA), user accuracy (UA), overall accuracy (OA) and kappa coefficient have been calculated from the confusion matrix of the applied models. This results show the highest accuracy has been derived from the integration of S-1-2 datasets followed by S-2, L-8, L-3, L-4, and S-1. On the other hand, LB model is the most consistent and efficient in comparison with other models for all the datasets. Regarding importance of variable, SWIR band is repeatedly the most crucial factor while blue band is the least significant variable. From this comparative assessment of sensors, it has been found that high spatial and spectral resolutions along with combination of satellite datasets are required to get better accuracy rather than only high spatial resolution in regional scale mapping. The present study strongly advocates the use of combined S-1-2 data together with the application of LB model for LCLU classification.

The effects of habitat loss and fragmentation on updated estimates of the population of the Agulhas Long-billed Lark Certhilauda brevirostris, a South African endemic

The Agulhas Long-billed Lark Certhilauda brevirostris is a South African endemic and is restricted to the fynbos habitat of the Agulhas Plain in the southwestern corner of South Africa. The 1990, 2014, and 2018 land use land cover (LULC) databases prepared for South Africa were used to determine the LULC categories that best describe suitable habitat for this bird using beta regressions. Previously published densities of Agulhas Long-billed Lark and the surface area of the LULC categories considered suitable habitat for the birds were used to estimate their historical and current population sizes, and the rates of change in the bird’s population. Beta regression models were compiled from the bird’s adjusted reporting rate and the total surface area, and three other landscape metrics, of the per pentad LULC categories considered suitable habitat for them. These models revealed that although the Agulhas Long-billed Lark is adversely affected by the loss of its natural fynbos habitat these adverse effects have been offset by an increase in the availability of fallow fields in summer. This confirms previous observations that agriculture may have allowed the Agulhas Long-billed Lark to increase in numbers. The Agulhas Long-billed Lark’s extent of occurrence (EOO) was estimated at 1 322 509 ha (n = 197 pentads) with an area of occupancy (AOO) estimated to be 1 076 854 ha in 2020. The decline in habitat available to the birds was estimated to be 3.5% over three generations (10.5 years). The present study provides improved estimates of the population of the Agulhas Long-billed Lark of between 146 344 and 264 691 individuals, with between 60 753 and 109 883 individuals in natural habitat, and between 85 591 and 154 808 individuals in transformed habitat. The results of the present study indicate that the Agulhas Long-billed Lark’s Near Threatened status should be reviewed.

Future land use change simulations for the Lepelle River Basin using Cellular Automata Markov model with Land Change Modeller-generated transition areas

Background: Land use/land cover (LULC), change is one of the major contributors to global environmental and climate variations. The ability to predict future LULC is crucial for environmental engineers, civil engineers, urban designers, and natural resource managers for planning activities. Methods: TerrSet Geospatial Monitoring and Modelling System in conjunction with ArcGIS Pro 2.8 were used to process LULC data for the region of the Lepelle River Basin (LRB) of South Africa. Driver variables such as population density, slope, elevation as well as the Euclidean distances of cities, roads, highways, railroads, parks and restricted areas, towns to the LRB in combination with LULC data were analysed using the Land Change Modeller (LCM) and Cellular-Automata Markov (CAM) model. Results: The results reveal an array of losses (-) and gains (+) for certain LULC classes in the LRB by the year 2040: natural vegetation (+8.5%), plantations (+3.5%), water bodies (-31.6%), bare ground (-8.8%), cultivated land (-29.3%), built-up areas (+10.6%) and mines (+14.4%). Conclusions: The results point to the conversion of land uses from natural to anthropogenic by 2040. These changes also highlight how the potential losses associated with resources such as water will negatively impact society and ecosystem functioning in the LRB by exacerbating water scarcity driven by climate change. This modelling study seeks to provides a decision support system for predicting future land resource utilization in the LRB and perhaps assist for planning purposes.

Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

Detecting and mapping the spatial distribution of Chromoleana odorata invasions in communal areas of South Africa using Sentinel-2 multispectral remotely sensed data

Alien Invasive Plants (AIPs) have been proclaimed to be a significant non-climatic driver of global change. The Siam weed (Chromoleana odorata) is highly invasive in South African rangelands, with serious environmental, agricultural and socio-economic consequences. Therefore, a comprehensive analysis of C.odorata's spatial distribution is necessary for implementing relevant mitigation and management approaches to vulnerable and invaded landscapes. Remotely sensed data offer a viable opportunity for detecting and mapping AIPs spatial extents. Hence, this study sought to compare the value of algorithms (i.e. maximum likelihood and random forest) in detecting and mapping C.odorata's spatial distribution in relation to other land use land cover classes (LULC) using the freely available Sentinel-2 multispectral image data. The findings of the study revealed that the vegetation red edge and near-infrared spectral bands of Sentinel-2 multispectral imagery (MSI) sensor were the most important spectral variables for discriminating C.odorata from other land cover classes. The random forest algorithm yielded the highest overall classification accuracy of 83%, outperforming the traditional maximum likelihood classifier (75%) in classifying C.odorata's spatial distribution. Overall, results demonstrate that the combination of freely available Sentinel-2 MSI satellite data and machine learning random forest algorithm produces the highest accuracies for detecting the spatial distribution of C.odorata, particularly in heterogeneous environments. These results could be beneficial to sustainable rangeland management and the adoption of site-specific mitigation approaches in areas invaded by C.odorata.

Surface runoff response to long-term land use changes: Spatial rearrangement of runoff-generating areas reveals a shift in flash flood drivers

Nowadays, rapid growths of urban areas and associated land use/land cover (LULC) changes notably affect the surface runoff response to rainfall in urbanised catchments. This effect of urbanisation on surface runoff is frequently addressed since it relates to the increasing incidence of pluvial floods worldwide. Studies on this issue commonly assess runoff changes over several most recent decades. However, human-induced factors likely resulted in increased surface runoff and associated flash floods in rural catchments even in the past centuries. Such long-term changes have been rarely studied and thus are not well documented. This paper aims to fill this gap and assess the surface runoff response to LULC changes in a small catchment in southwestern Slovakia over more than two centuries. To meet this goal, we applied the Soil Conservation Service Curve Number (SCS-CN) method. We mapped past (1784) and present (2018) LULC classes based on historical maps and modern remote sensing data. Then, we estimated surface runoff depths for specific design rainstorms for both periods by executing CN method equations in the ArcGIS environment. Results revealed significant changes in surface runoff conditions between evaluated periods. In total, surface runoff magnitudes increased by ~230 and ~ 130% for 40- and 65-mm design rainstorms, respectively, compared to 1784. Moreover, surface runoff values show different spatial patterns on historical and present-day output raster images revealing spatial redistribution of runoff-enhancing and runoff-preventing areas during the evaluated period. Although both infiltration-enhancing and runoff-enhancing effects of LULC changes were identified, the latter effect associated with urbanised lowland areas was recognised as prevailing at present. In contrast, runoff-promoting conditions were associated with hilly vineyard areas in the past. These findings likely have important implications for understanding the past and present LULC-based controls of surface runoff generation and flash floods initiation.

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA

The analysis of land use and land cover data provides invaluable support to a variety of land management and conservation activities. However, historically its application has been limited by high costs associated with data acquisition, analysis, and classification. In recent years, freely available satellite imagery and geospatial data sets and rapid advancement in data analysis capabilities provide immense opportunities to understand and solve the real-world environmental problems. Open-source platforms such as Google Earth Engine (GEE) provide a planetary-scale environmental science data and analyses capability at much greater efficiency and accuracy than the traditional workflows. We evaluated the GEE Python API utility for classifying the freely available NAIP aerial imagery of 2017 to derive the land use land cover (LULC) information of a Panhandle area of Florida, USA. We identified eight major LULC classes with an overall accuracy of 86% and Kappa value of 79%. We completed all remote sensing data analyses procedures including data retrieval, classification, and report preparation in the Jupyter notebook, an open-source web application. Computation time for the procedure was less than 15 min. Our results demonstrate the usefulness of this approach for conducting land use and land cover analysis using much less time, money, and human resources. The open-source nature of GEE Python API and its library of remote sensing data could benefit remote sensing projects throughout the world, especially where access to commercial image processing software packages and remote sensing data are limited.

Technical note: AQMEII4 Activity 1: evaluation of wet and dry deposition schemes as an integral part of regional-scale air quality models

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes.

Assessment of Land Use Land Cover with Special Reference to Trees Outside Forests (TOF) in The Central Region of Kashmir Himalayas Using IRS LISS-IV data

Spatial assessment of trees outside forests (TOFs) is widely being accepted as a critical matter in sustainable natural resource management owing to their role in providing variety of goods and valuable services like carbon sequestration. Such information is highly important for decision making in projects under CDM and REDD+. The present study was taken to assess landuse land cover (LULC) with special reference to TOF in central region of Kashmir Himalayas using high spatial resolution IRS-LISS IV data of 2016. LULC classes delineated were supplemented with validation through ground truthing. The results revealed that forest class occupied maximum area (34.43%) of the region. Overall classification accuracy of LULC map estimated as 85.00% (kappa coefficient of 0.833). TOFs were classified into three strata i.e. block, linear and scattered as per FSI scheme of classification of TOF (rural) followed in India. Based on the preliminary survey results, TOF strata were further classified into six TOF practices. Among the TOF practices roadside plantations, riverside plantations and boundary plantations) formed linear stratum while as woodlots & horticulture formed block stratum and the last scattered patches with clumpy plantation (SPCP) was included in scattered TOF stratum. Area under TOF was found to be 6.77% of total geographical area. Among TOF strata, block TOF covered the maximum area (6.395%) whereas linear TOF occupied 0.370% and scattered TOF occupying 0.005%. Among the TOF practices, horticulture covered the maximum area of 5.73% whereas SPCP occupied only 0.01% of the region. The overall classification accuracy of TOF map was assessed to be 86.11% with kappa coefficient of 0.833. There is a need to assess TOFs on periodical basis in the region for temporal changes as TOFs assume importance in provisional and regulating ecosystem services like prevention of soil erosion and climate amelioration.

Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

Mapping Spatiotemporal Dynamics of Akure Industrial Layout for Sustainable Development

This research aimed at mapping the spatiotemporal dynamics of the Industrial Layout located in Akure Ondo State Nigeria. The dataset used are the administrative map of Ondo State, Akure Industrial Layout Boundary,various Landsat imageries of 32m resolution which are Thematic Mapper (TM) of 1986 & 1991, Enhanced Thematic Mapper Plus (ETM)+ of 2002, Operational Land Imager / Thermal Infrared Sensor (OLI/TIRS) of 2014, 2017, 2020; and Worldview 3 image 2020 of 1.24m resolution. The Landsat data were used to extract the different Land use/Land cover (LULC) within the study area. GPS receiver and Worldview 3 image were used to obtain the coordinates of the different LULC classes, which aided in the classification of image, and also for accuracy assessment of the classified image. All the Landsat standard data products were processed, to ascertain that they are free of radiometric and geometric errors using the Level 1 Product Generation System (LPGS) and extracted to obtain the landsat image bands. The extracted Landsat images (bands) were used in the processing and calculating the Normalized Difference Vegetation Index (NDVI) and calculation of LULC changes. Evaluation the accuracy of the results produced from the land cover classification was carried out by comparing the results of ground coordinates with the coordinates obtained from a higher resolution image (Worldview 3 image) in order to determine the accuracy of the land cover classification in the study area. The trend of changes of land cover in these areas was assessed and also, the prediction for the future condition both in terms of development was determined based on the results obtained from the initial results. Results from various maps produced and numerical data generated showed that Akure Industrial Layout was mainly dominated by shrub and grass land in 1986 and has in 34 years experienced transformation of 604% in the built environment (18% /year), 119% of Bareland (3.5%/year), and -29% of Grassland (0.9%/year), -66% of Shrub (2%/year). The forecast of the probable spatial extent for the years 2025 and 2030 were estimated to be 175.3Ha and 214.8Ha respectively, which shows there will be a continuous increase in the future development in Akure Industrial layout. The research recommended a proactive action from the government and end-users that will ensure a sustained manageability of the layout.

Land use/land cover change and ecosystem services in the Bagmati River Basin, Nepal.

Delivery of ecosystem services is strongly affected by changes in the land use/land cover (LULC) of an area. In this study, we analyze spatiotemporal changes in LULC of the rapidly changing Bagmati River Basin (BRB) of Nepal during 1988-2018 using Landsat satellite images. We also quantify carbon storage in different physiographic regions and LULC classes using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and assess economic valuation of carbon using the benefit transfer method. According to our analysis, there were increases in urban/built-up (247.5%), barren land (109.5%), shrub land (32.4%), and declines in forest cover (- 6.2%), cultivated land (- 4.1%), waterbodies (- 30.3%), sand (- 29.2%), and grass cover (- 10.6%) during the study period. As a result of these changes in LULC, carbon storage declined from 31.4 million tons year-1 in 1988 (worth 157.0 million USD) to 30.8 million tons year-1 (154.1 million USD) in 2018 with the total loss of 2.9 million USD. The largest decline in stored carbon was observed in Tarai and Dun valleys, from 6.8 to 6.5 million tons (- 1.4 million USD) followed by Churia, from 7.8 to 7.6 million tons (- 1.1 million USD). Increases in carbon storage were observed in urban/built-up and shrub land areas and declines in cultivated land, forest, barren land, waterbodies and grass land. The results of LULC change and estimated carbon stock in BRB provides a baseline for planners and policy makers to formulate appropriate plans to sustainably manage the region's land cover and to mitigate carbon loss.

Hydrological Responses to Land Use Land Cover Changes in the Fincha’a Watershed, Ethiopia

Land use land cover (LULC) changes are highly pronounced in African countries, as they are characterized by an agriculture-based economy and a rapidly growing population. Understanding how land use/cover changes (LULCC) influence watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies to minimize the undesirable effects of future land use/cover change or modification and sustain the local socio-economic situation. The hydrological response of the Ethiopia Fincha’a watershed to LULCC that happened during 25 years was investigated, comparing the situation in three reference years: 1994, 2004, and 2018. The information was derived from Landsat sensors, respectively Landsat 5 TM, Landsat 7 ETM, and Landsat 8 OLI/TIRS. The various LULC classes were derived via ArcGIS using a supervised classification system, and the accuracy assessment was done using confusion matrixes. For all the years investigated, the overall accuracies and the kappa coefficients were higher than 80%, with 2018 as the more accurate year. The analysis of LULCC revealed that forest decreased by 20.0% between the years 1994–2004, and it decreased by 11.8% in the following period 2004–2018. Such decline in areas covered by forest is correlated to an expansion of cultivated land by 16.4% and 10.81%, respectively. After having evaluated the LULCC at the basin scale, the watershed was divided into 18 sub-watersheds, which contained 176 hydrologic response units (HRUs), having a specific LULC. Accounting for such a detailed subdivision of the Fincha’a watershed, the SWAT model was firstly calibrated and validated on past data, and then applied to infer information on the hydrological response of each HRU on LULCC. The modelling results pointed out a general increase of average water flow, both during dry and wet periods, as a consequence of a shift of land coverage from forest and grass towards settlements and build-up areas. The present analysis pointed out the need of accounting for past and future LULCC in modelling the hydrological responses of rivers at the watershed scale.