Land use and land cover dynamics with special emphasis on shifting cultivation in Eastern Ghats Highlands of India using remote sensing data and GIS

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (-3.27%), bare surface (-33.47%), and water bodies (-0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes.

Geospatial technology in agroforestry: status, prospects, and constraints

Agroforestry has an indispensable role in food and livelihood security in addition to its capacity to combat the detrimental effects of climate change. However, agroforestry has not been properly promoted and exploited due to lack of precise extent, geographical distribution, and carbon sequestration (CS) assessment. The recent advent of geospatial technologies, as well as free availability of spatial data and software, can provide new insights into agroforestry resources assessment, decision-making, and policy development despite agroforestry's small spatial extent, isolated nature, and higher structural and functional complexity of agroforestry. In this review, the existing application of geospatial technologies together with its constraints and limitations as well as the potential future application for agroforestry has been discussed. The review reveals that the application of optical remote sensing in agroforestry includes spatial extent mapping, production of tree species spectral signature, CS assessment, and suitability mapping. Simultaneously, the recent surge in the use of synthetic aperture radar in conjunction with algorithms based on vegetation photosynthesis and optical data enables a more accurate estimation of gross primary productivity at different scales. However, unmanned aerial vehicles equipped with sensors, such as multispectral, LiDAR, hyperspectral, and thermal, offer a considerably higher potential and accuracy than satellite-based datasets. In the future, the health monitoring of agroforestry systems can be a key concern that may be addressed by utilizing hyperspectral and thermal datasets to analyze plant biochemistry, chlorophyll fluorescence, and water stress. Additionally, current (GEDI, ECOSTRESS) and future space agency missions (BIOMASS, FLEX, NISAR, TRISHNA) have enormous potential to shed fresh light on agroforestry systems.

Monitoring of drainage system and waterlogging area in the human-induced Ganges-Brahmaputra tidal delta plain of Bangladesh using MNDWI index

Waterlogging is one of the major global problems which affects agro-economic activities around the world. In the coastal areas of Bangladesh, especially the southwestern coast, drainage congestion and waterlogging are very common which makes the area uninhabitable. Therefore, timely checking of drainage systems and surface water, and conveying data on the dynamics of drainages and surface water are important for plan and supervisory processes. The present study took an effort to illustrate the waterlogging and morphological change of the rivers in the southwestern coast of Bangladesh through the Modification Normalized Difference Water Index (MNDWI) values which are valuable indicators for monitoring the water area and land use pattern change. Landsat images (Landsat L8 Oli TIRS, Landsat ETM+, Landsat TM) were used in the research. The study reveals that from 1989 to 2020, the shallow water area (mostly covered with rivers) decreased by ^∼14.30 km² yr^-1, whereas the wet-land area (mostly covered with beels and water logging areas) increased by ^∼ 67.12 km² yr^-1. The bare land area also increased at a rate of ^∼ 36.90 km² yr^-1. On the other hand, the green vegetation decreased at a rate of ^∼166.1 km² yr^-1, whereas the moderate green vegetation area increased by ^∼ 69.77 km² yr^-1 for the same period. In the coastal zones of Bangladesh, the polders, embankments, upstream dams, etc., enhance more sedimentation within the channels rather than in the nearby tidal plains. As a result, the shallow water area which is mostly covered by rivers is gradually decreasing. Moreover, due to increase in wet-land areas with salinity intrusions which affect the vegetation. Therefore, the green vegetation area is regularly declining due to demolition or conversion to moderate green vegetation. The findings of the research will be supportive for coastal scientists worldwide, policy makers & planners, and finally supportive for sustainable management of the coastal areas including Bangladesh.

Monitoring land use and land cover change near a nuclear power plant construction site: Akkuyu case, Turkey

Land use and land cover (LULC) change analysis of the construction site and its surroundings of the Akkuyu Nuclear Power Plant project in southern Turkey was undertaken in this case study, which was supported by remotely sensed Landsat 8 image composites. The composite images compiled in 2017 and 2021 were prepared on the Google Earth Engine platform. The Random Forest algorithm was used as the classifier model. A high classification performance was obtained for both images (kappa > 0.88, overall accuracy > 90%). After the classification process, LULC maps for both years were generated, and statistical calculations for the LULC change were computed for both the entire study area (15 × 25 km) and a buffer zone with a radius of 1 km around the power plant. In the whole study area, artificial surfaces significantly increased (78.46%), whereas forests (- 8.31%) and barren lands experienced a considerable decrease (- 6.11%). In the 1 km buffer, artificial surfaces predominantly increased (113.94%), while forests and barren lands decreased dramatically (- 69.13% and - 74.28%, respectively). The agricultural areas in the study area were changed into other LULC classes: 9.1% to artificial surfaces, 27.6% to barren lands, and 21.7% to forest. The rise in the area of artificial surfaces was especially noticeable within the 1 km buffer zone: construction activities converted 36.1% of agricultural fields, 54.1% of forests, and 23.2% of barren lands into artificial surfaces. The filling activities on the seashore resulted in a loss of water bodies of up to 26.5%. The study provides an overview of how the LULC classes have evolved on the construction site and in the region. In the end, the study discusses how the current land use preferences in the region contradict the issues and concerns mentioned in the existing body of literature.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

Expansion of Rural Settlements on High-Quality Arable Land in Tongzhou District in Beijing, China

Settlement expansion caused by urbanization is an important factor leading to the loss of arable land across the world. Due to various factors in China, such as institutional problems, the total number of rural settlements is decreasing, while the total area continues to increase. Rural settlements expand mainly into arable land, resulting in a significant loss of high-quality farmland, thus threatening long-term food security. However, research on this subject is relatively scarce. In this study, using KeyHole and RESURS F1 satellite remote sensing images, we examined the spatial expansion of rural settlements in Tongzhou District, Beijing, in 1972 and 1991. Then, the consumption of high-quality arable land by rural settlements expansion was assessed. It was found that the overall accuracy of the produced maps for 1972 and 1991 were 93% and 90%, respectively. The accuracy of mapped changes from 1972 to 1991 was as high as 90%. From 1972 to 1991 and from 1991 to 2015, the rural settlements in Tongzhou District expanded by 51.54% and 79.91% respectively, with 53.72% and 60.64% of the expanded rural settlements being on arable land. Rural settlements expanded mainly into high-quality arable land at the beginning of the study period, whereas later on, medium- and low-quality farmland was also occupied, albeit to a lesser degree.