Land Cover Change Mapping Research Articles

Mapping and monitoring of spatio-temporal land use and land cover changes and relationship with normalized satellite indices and driving factors

ABSTRACT Climate change has become a severe threat all around the world. Pakistan is also affected by climate change. It has become a severe problem in any part of the country. It is land degradation caused by climatic disturbance and anthropogenic activities. In this study, we used Landsat TM for the years 1980 and 1990, Landsat ETM+ for the years 2000 and 2010, and Landsat OLI/TIRS for the year 2020 were classified using the Maximum Likelihood Classification (MLC) into a built-up area, barren land, vegetation, and water area. This study has been conducted to assess the spatio-temporal analysis of Land Use/Land Cover (LULC) with driving factors from 1980–2020 in the Cholistan Desert, Punjab, Pakistan, and the relationship between different normalized satellite indices with LULC. Post-classification change detection methods were then used to assess the variation over the study period. The built-up area has increased by 6.25% from 1980–2020. The Kappa coefficient was estimated at 0.83, 0.82, 0.83, 0.85, and 0.88 in 1980, 1990, 2000, 2010, and 2020, respectively. At the annual scale (the years 2000 and 2020), a significant positive relationship was observed among LULC, NDVI, and MNDWI for the regions located in the western part of the Cholistan region, while negative trends in the year 1980 to the 2010 year.

Soil and vegetation water content identify the main terrestrial ecosystem changes.

Environmental change is a consequence of many interrelated factors. How vegetation responds to natural and human activity still needs to be well established, quantified and understood. Recent satellite missions providing hydrologic and ecological indicators enable better monitoring of Earth system changes, yet there is no automatic way to address this issue directly from observations. Here, we develop an observation-based methodology to capture evidence of changes in global terrestrial ecosystems and attribute these changes to natural or anthropogenic activity. We use the longest time record of global microwave L-band soil moisture and vegetation optical depth as satellite data and build spatially explicit maps of change in soil and vegetation water content and biomass reflecting large ecosystem changes during the last decade, 2010-20. Regions of prominent trends (from [Formula: see text] to 9% per year) are observed, especially in humid and semi-arid climates. We further combine such trends with land cover change maps, vegetation greenness and precipitation variability to assess their relationship with major documented ecosystem changes. Several regions emerge from our results. They cluster changes according to human activity drivers, including deforestation (Amazon, Central Africa) and wildfires (East Australia), artificial reforestation (South-East China), abandonment of farm fields (Central Russia) and climate shifts related to changes in precipitation variability (East Africa, North America and Central Argentina). Using the high sensitivity of soil and vegetation water content to ecosystem changes, microwave satellite observations enable us to quantify and attribute global vegetation responses to climate or anthropogenic activities as a direct measure of environmental changes and the mechanisms driving them.

SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis

Change detection has played an increasingly important role in multi-temporal remote sensing applications recently. Long time series analysis is providing new information of land cover changes and improving the quality and accuracy of the change information being derived from remote sensing. The purpose of this study is to dig for more change temporal information and change pattern information from synthetic aperture radar (SAR) image time series (ITS), which is of great significance for monitoring urban area changes, conducting land-use surveys, and renovating illegal constructions. In the study, a novel unified framework for long Time Series SAR image Change detection and Change pattern mining (SAR-TSCC) was proposed for land cover change mapping. To obtain the most notable change time rapidly, a fast SAR ITS change point search method based on pruned exact linear time (SAR-PELT) algorithm was adopted. Meanwhile, the deep time series classification network, named SAR-TST, was implemented to recognize the change patterns, which is based on time series transformer (TST) architecture. Considering the lack of real training data, a novel synthetic data generation method is developed. The combination of the synthetic and real data enhanced the generalization of the classifiers. The proposed framework was used for monitoring a large urbanization area in the northwest of Hong Kong, China. The Cosmo Skymed (CSK) time series data acquired from 2013 to 2020 were exploited for land cover change analysis. Experiment results showed that our approach achieved state-of-the-art performance, as the time accuracy reached 86% and the classification accuracy on the four main change patterns (impulse, step, cycle, and complex) is over 99%. In particular, the proposed SAR-TST model showed remarkable advantages in the presence of insufficient real data.

MANAGING, ASSESSING AND MONITORING URBAN SPRAWL USING REMOTE SENSING; CHANGE DETECTION, URBAN EXPANSION INTENSITY INDEX (UEII), AND SHANNON ENTROPY: A CASE STUDY OF AMMAN CITY, JORDAN (1990–2022)

Mapping and monitoring of land use land cover (LULC) changes in Amman city are important for sustainable regional development, urban planning, and environmental and land use management. To comprehend the temporal and the spatial variability of LULC change, six time-period satellite images of the study area have been examined based on remote sensing (RS) and geographic information system (GIS) techniques. The changes in LULC patterns of Amman city were monitored for the periods 1990-1995, 1995-1999, 1999-2004, 2004-2013, 2013-2017 and 2017-2022. Employing Spatio-temporal Landsat TM and ETM imagery acquired from 1990 to 2022 to comprehend, detect, and quantify the spatial pattern of urban sprawl. Supervised classification was used to extract built-up areas and measure changes in urban land-use class across the metropolis. The result of the urban expansion intensity index shows that urban expansion hits its peak between 2013 and 2017, with a 1.75 index resulting in high-speed development between the time frames. Shannon’s entropy approach was quantified to detect the urban sprawl phenomenon. The results showed that the Relative Shannon entropy values are above 0.5, indicating dispersedly spread-urban sprawl. Land Absorption Coefficient (LAC) and Land Consumption Ratio (LCR) were also used. A strong positive correlation between road networks and urban sprawl (0.7) indicates that urbanization plays a major role in the occurrence of urban sprawl. The findings obtained from this work would provide urban planners and decision-makers in Amman the tools and data they need, to implement planning interventions for sustainable future development and to mitigate the impacts of urban sprawl on different aspects on the environment.

The Application of Multi Temporal Satellite Data For Land Cover Mapping of Bawean Island, East Java

Land cover dynamics in a small island can be determined using Geographic Information System (GIS) approach based on multitemporal image analysis. This study aimed to classify major land cover types and to map land cover changes of Bawean Island. Two sets of 10 meter resolution satellite data ALOS AVNIR (2010) and Sentinel-2A (2020) were used in this study. Satellite image analysis was carried out through several stages namely image pre-processing including radiometric and geometric correction, supervised image classification and accuracy test. Image classification results from 2010 to 2020 showed a significant change in land cover on Bawean Island. The forest vegetation land cover declined significantly from 13,470.5 Ha in 2010 to 8,543.4 Ha in 2020. Most of the area have been converted into paddy fields and built-up areas. The accuracy test and validation were determined by comparing the 2020 Sentinel image classification results with field observation conducted in 2021. The analysis showed good results with 82.52% overall accuracy and 79.66 Kappa coefficient. Further investigation found that changes in land cover on Bawean Island occured due to the agriculture and infrastucture development.
 Keywords : Geographic Information System (GIS); land cover; satellite images; small island
 
 Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Modeling present and future ecosystem services and environmental justice within an urban-coastal watershed

Globally, urban-coastal areas are expected to experience substantial landscape shifts as a result of climate change induced sea level rise. Such changes will impact valuable ecosystem services. We employed sea level rise projections and land cover change mapping to develop a model which quantified present-day carbon sequestration, aboveground carbon storage, and belowground carbon storage ecosystem services and predicted the impact of sea level rise and accretion through 2100 on future ecosystem services in the urban-coastal Jamaica Bay, New York (USA) watershed. Our model predicted that future carbon sequestration, aboveground carbon storage, and belowground carbon storage potential in our watershed will be significantly impacted in wetlands and natural coastal-fringe habitat and have losses up to 0.16%, 15%, and 51%, respectively. We paired our present-day ecosystem services model results with data on socio-economic need (access to open space and poverty level of each census tract in the watershed) and used multivariate clustering analysis to identify clusters in which planning and restoration may help to address issues of ecological conservation and environmental justice. Our work addresses the need for better understanding of urban-coastal ecosystem service flows and the potential impact of future landscape change on these services. Our results provide support to increase coastal resilience through informed design, planning, and management of these ecologically and socially significant landscapes.

Assessing protected area networks in the conservation of elephants (Elephas Maximus) in Sri Lanka

• Time series remote sensing data allow for the identification of changes that have happened in protected regions • There is a close relationship between protected area performance and land use and land cover change • There is a correlation between fragmentation of protected areas and the incidence of human-elephant conflicts Assessing protected area networks in the conservation of elephants ( Elephas Maximus ) in Sri LankaProtected areas play a crucial role in the conservation and management of wildlife, but land use and land cover change (LULCC) threatens the status of protected areas. Sri Lanka has a history of severe human–elephant conflict (HEC). In the last 15 years, Sri Lanka has recorded the highest mortality of elephants and the second-highest human casualties among countries where the Asian elephant is native. In this study, we conducted a whole of country analysis of the effect of LULCC on protected areas using a land cover change map (1993–2018) recently developed by the authors using Landsat satellite data. Protected area performances were measured using five criteria including LULCC, the protected areas, and categorised into three performance levels. The protected area performances were then compared with number of HEC incidents. We found that 12% of Sri Lanka's protected area was affected by LULCC events, and every individual protected area experienced LULCC. We also found that 86% of elephant death incidents occurred within a 5 km radius of protected areas, with a strong negative correlation with distance from protected areas (r = –0.94, p < 0.05). Some 43% of HEC incidents and 23% of elephant deaths occurred inside protected areas, while 40% of elephant deaths in the last two years occurred inside protected areas. These areas were also found to fragment over time and elephant deaths increased, and showed a strong positive correlation, with fragmentation (r = 0.88, p < 0.05). Wildlife regions that experienced higher LULCC also experienced a greater number of elephant deaths, with a moderately positive correlation (r = 0.54, p < 0.05). Irrespective of the level of performance, all protected areas reported elephant deaths as well as HEC incidents, indicating that protected areas are failing to protect the endangered Elephas maximus population in Sri Lanka. These country-wide insights into protected areas can be used to re-evaluate the function and effectiveness of protected areas in managing and mitigating HEC while providing protection to elephants in Sri Lanka.

Land Use and Land Cover Change Mapping: A Spatio Temporal and Correlational Analysis of Ramganjmandi Tehsil, Kota, Rajasthan, India

Land use and Land cover signifies the human footprints on the surface of the earth. The current studydepicts the Land use and Land cover change analysis of Ramganjmandi tehsil of Kota district in Rajasthan,for a period of 30 years, 1990 to 2020. Supervised classification in ERDAS imagine software, opting maximumlikelihood classifier is used for the classification of the region into 7 classes. LANDSAT 1-5, 7, 8 Level 1collection were used for the year 1990, 2000, 2011, 2018 and 2020. The region shows a rapid change in theclasses of Mining, Agriculture, Vegetation, and waterbody. There is negative correlation between Miningarea and Vegetation (-0.68), Mining Area and Water Body (-0.86), Mining area and Cropland (-0.11), MiningArea and Open Land (-0.68), Mining area and Built-up area (0.84). The vegetation cover of the region hasalso declined from the year 1990 to 2020.

Machine learning algorithms for mapping Prosopis glandulosa and land cover change using multi-temporal Landsat products: a case study of Prieska in the Northern Cape Province, South Africa

Invasive alien plants (IAPs) are responsible for loss in biodiversity and the depletion of water resources in natural ecosystems. Prosopis species are IAPs previously introduced by farmers to provide shade and fodder for livestock. In the Northern Cape, Prosopis spp. invasions are associated with the loss of native species resulting in overgrazing and degrading rangelands. Mapping Prosopis glandulosa is essential for management initiatives to assist the government in minimising the spread and impact of IAPs. This study aims to evaluate the performance of two machine learning algorithms i.e., Support Vector Machine (SVM) and Random Forest (RF) to map the spatial dynamics of P. glandulosa in Prieska. The spatial invasion extent of P. glandulosa was mapped using multitemporal Landsat data spanning the period from 1990 to 2018. Validation of the results was done through an estimated error matrix with the use of the proportion of area and the estimates of overall accuracy, user’s accuracy and producer’s accuracy with a 95% confidence interval. The performance of the SVM and RF classifiers showed similar results in the overall accuracy and Kappa statistics throughout the years. These methods showed an overall increase of at least 3.3% of the area invaded by P. glandulosa from 1990 to 2018. The study indicates the importance of Landsat imagery for mapping historical and current land cover change of IAPs. The spread of P. glandulosa was confirmed by an increase in the total area of invasion, which enables decision-makers to improve monitoring and eradication initiatives.

Landsat8 vs. Sentinel-2: Land Use / Land Cover Change mapping for Karbala Governorate, Iraq, 2017 and 2021

Satellite images are the essential data source for analyzing and monitoring land cover on various time scales, particularly across large regions. Landsat satellite data with a medium resolution was used to estimate land cover change over a 40-year period.This data contains information on land use and land cover patterns, is now freely available in the international archives. The LULC Remote Sensing Study assists in the ongoing detection and mitigation of crucial habitat risks to protect the environment. Sentinel-2, a satellite mission launched by the European Space Agency between 2015 and 2017 that uses high-resolution 10-day time-lapse multispectral data, gives a new opportunity for ground-based mapping and monitoring in the tropics. We employed 2015 ERDAS, a supervised classification method employing the maximum likelihood technique, to achieve this goal in Karbala/Iraq. This study examines if There is a significant difference in quality of data supplied by Landsat 8 and Sentinel-2 photographs in terms of change-detection maps of land use and land coverfor 2017 and 2021, the results of two satellites were compared ,They showed that their overall accuracy increased by 2.07% for 2017 and 1.83% for 2021, which is more overall accuracy than Landsat-8.

Past and Future Land Use/Land Cover Changes in the Ethiopian Fincha Sub-Basin

The increasing human pressure on African regions is recognizable when looking at Land Use Land Cover (LULC) change maps, generally derived from satellite imagery. Using the Ethiopian Fincha watershed as a case study, the present work focuses on (i) identifying historical LULC change in the period 1989–2019; (ii) estimating LULC in the next thirty years, combining Geographical Information Systems (GIS) with Land Change Modelling (LCM). Landsat 5/8 images were combined with field evidence to map LULC in three reference years (1989, 2004, 2019), while the Multi-Layer Markov Chain (MPL-MC) model of LCM was applied to forecast LULC in 2030, 2040, and 2050. The watershed was classified into six classes: waterbody, grass/swamp, built-up, agriculture; forest; and shrub. The results have shown that, in the past 30 years, the Fincha watershed experienced a reduction in forest and shrubs of about −40% and −13%, respectively, mainly due to ever-increasing agricultural activities, and such a trend is also expected in the future. In fact, for the period 2019–2050, LCM simulated a significant decrease in both forest and shrubs (around −70% and −20%, respectively), in favor of more areas covered by grass (19%) and built-up (20%). It is worth noting that a decrease in natural forests can drive an increase in soil erosion, fostering siltation in the water reservoirs located in the sub-basin. The study pointed out the urgency of taking actions in the sub-basin to counteract such changes, which can eventually lead to a less sustainable environment.

Digital Mapping of Land Cover Changes Using the Fusion of SAR and MSI Satellite Data

The aim of this article is to choose the most appropriate method for identifying and managing land cover changes over time. These processes intensify due to human activities such as agriculture, urbanisation and deforestation. The study is based in the remote sensing field. The authors used four different methods of satellite image segmentation with different data: Synthetic Aperture Radar (SAR) Sentinel-1 data, Multispectral Imagery (MSI) Sentinel-2 images and a fusion of these data. The images were preprocessed under segmentation by special algorithms and the European Space Agency Sentinel Application Platform (ESA SNAP) toolbox. The analysis was performed in the western part of Lithuania, which is characterised by diverse land use. The techniques applied during the study were: the coherence of two SAR images; the method when SAR and MSI images are segmented separately and the results of segmentation are fused; the method when SAR and MSI data are fused before land cover segmentation; and an upgraded method of SAR and MSI data fusion by adding additional formulas and index images. The 2018 and 2019 results obtained for SAR image segmentation differ from the MSI segmentation results. Urban areas are poorly identified because of the similarity of spectre signatures, where urban areas overlap with classes such as nonvegetation and/or sandy territories. Therefore, it is necessary to include the field surveys in the calculations in order to improve the reliability and accuracy of the results. The authors are of the opinion that the calculation of the additional indexes may help to enhance the visibility of vegetation and urban area classes. These indexes, calculated based on two or more different bands of multispectral images, would help to improve the accuracy of the segmentation results.

An operational land cover and land cover change toolbox: processing open‐source data with open‐source software

Abstract Accurate and up‐to‐date land cover maps are vital for underpinning evidence‐based landscape management decision‐making. However, the technical skills required to extract tailored information about land cover dynamics from these open‐access geospatial data often limit their use by those making landscape management decisions. Using Dartmoor National Park as an example, we demonstrate an open‐source toolkit which uses open‐source software (QGIS and RStudio) to process freely available Sentinel‐2 and public LiDAR data sets to produce fine scale (10 m2 grain size) land cover maps. The toolbox has been designed for use by staff within the national park, for example, enabling land cover maps to be updated as required in the future. An area of 945 km2 was mapped using a trained random forest classifier following a classification scheme tailored to the needs of the national park. A 2019 land cover map had an overall user's accuracy of 79%, with 13 out of 17 land cover classes achieving greater than 70% accuracy. Spatially, accuracy was related via logistical regression to blue band surface reflectance in the spring and topographic slope derived from LiDAR (1 m resolution), with greater accuracy in steeper terrain and areas exhibiting higher blue reflectance. Between an earlier (2017–2019) and later (2019–2021) time frame, 8% of pixels changed, most of the change by area occurred in the most common classes. However, the largest proportional increase occurred in Upland Meadows, Lowland Meadows and Blanket Bog, all habitats subject to restoration efforts. Identifying areas of change enables future field work to be better targeted. We discuss the application of this mapping to land management within the Dartmoor national park and of the potential of tailored land cover and land cover change mapping, via this toolbox, to evidence‐based environmental decision‐making more widely.

Mapping pre and post earthquake land cover change in Melangkap, Kota Belud Sabah using multi-temporal satellite Landsat 8/OLI and Sentinel 2 Imagery

Earthquakes in mountainous belt can trigger major disturbance to the landscape impound a large amount of debris exported largely to the streams and forming landslide. During the 5th of June 2015, an earthquake of 5.9 Richter scale hit the heart of Mount Kinabalu, Sabah. The tremors caused the surrounding area to be affected largely to the population that is located near the river stream site. This study maps the land cover in Kota Belud following the earthquake that occurred in 2015 using Landsat 8 Operational Land Imagery (year 2014) and Sentinel-2 Multispectral Imagery data (2020). It was reported as one of the locations that severely impacted by the earthquake. Maximum likelihood algorithm was used to generate the map of land cover change of Kota Belud year 2014 and year 2020. Nine (9) classes of land cover were identified in the study area consisting of primary forest, secondary forest, shrubland, paddy, rubber plantation, bare rock, bare soil, built up, and river. The result obtained shows the most affected area is the river area, Sungai Panataran, that flow through Melangkap from Mount Kinabalu. Other land cover type also shows changes but mostly due to deforestation and agricultural activities. In conclusion, mapping the Kota Belud area using remote sensing and geographical information system (GIS) is useful for monitoring the landscape change of the site due to the vulnerability to natural disaster such as flood and earthquake.

Forest fragmentation in a forest Biosphere Reserve: Implications for the sustainability of natural habitats and forest management policy in Ethiopia

Forest fragmentation is a landscape-level process that comprises forest loss and changes in spatial patterns. Effective forest conservation programs thus require a comprehensive understanding of the relationship between forest cover and the spatial patterns of forest fragments. However, these matters remain less known for ecosystems in tropical regions. Hence, the study examines forest fragmentation of the Kaffa Biosphere Reserve in Ethiopia, which is a forested ecosystem of 744.9 thousand hectares. The study covers 33 years from 1986 to 2019 and forecasts for 2034 and 2049 using Landsat imagery. The land-use and land cover change maps were produced using a supervised classification following the maximum likelihood approach. The study quantified the spatial and temporal trends in forest loss and fragmentation using a multi-temporal satellite and FRAGSTAT raster dataset. The results display the forest areas declined from 46.9 to 30.5% from 1986 to 2019. The fragmentation caused an increase in the number of forest fragments from 4,067 to 8,832 from 1986 to 2019 but is expected to decline to nearly 2,000 in 2049 due to the complete conversion of small size forest fragments into other land use types. In addition, the size of the large core forest areas reduced from 43.7% in 1986 to 6.1% in 2019. The edge density of the forest declined from 30.5 to 27.1 m/ha from 1986 to 2019 and declined to 13 m/ha in 2049. The results confirmed natural and anthropogenic processes (e.g. agricultural and built-up area expansions and road network development) caused forest fragmentation and degradation in the Kaffa Biosphere Reserve. Therefore, the policy-makers should look at new options and revised the existed natural resource conservation strategies to safeguard the remaining natural forest covers.

Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020

Analyzing the dominant forms and extent of land cover changes in the Mount Elgon region is important for tracking conservation efforts and sustainable land management. Mount Elgon’s rugged terrain limits the monitoring of these changes over large areas. This study used multitemporal satellite imagery to analyze and quantify the land cover changes in the upper Manafwa watershed of Mount Elgon, for 42 years covering an area of 320 km2. The study employed remote sensing techniques, geographic information systems, and software to map land cover changes over four decades (1978, 1988, 2001, 2010, and 2020). The maximum likelihood classifier and post-classification comparison technique were used in land cover classification and change detection analysis. The results showed a positive percentage change (gain) in planted forest (3966%), built-up (890%), agriculture (186%), and tropical high forest low-stocked (119%) and a negative percentage change (loss) in shrubs (−81%), bushland (−68%), tropical high forest well-stocked (−50%), grassland (−44%), and bare and sparsely vegetated surfaces (−14%) in the period of 1978–2020. The observed changes were concentrated mainly at the peripheries of the Mount Elgon National Park. The increase in population and rising demand for agricultural land were major driving factors. However, regreening as a restoration effort has led to an increase in land area for planted forests, attributed to an improvement in conservation-related activities jointly implemented by the concerned stakeholders and native communities. These findings revealed the spatial and temporal land cover changes in the upper Manafwa watershed. The results could enhance restoration and conservation efforts when coupled with studies on associated drivers of these changes and the use of very-high-resolution remote sensing on areas where encroachment is visible in the park.

Mapping Land Cover Types for Highland Andean Ecosystems in Peru Using Google Earth Engine

Highland Andean ecosystems sustain high levels of floral and faunal biodiversity in areas with diverse topography and provide varied ecosystem services, including the supply of water to cities and downstream agricultural valleys. Google (™) has developed a product specifically designed for mapping purposes (Earth Engine), which enables users to harness the computing power of a cloud-based solution in near-real time for land cover change mapping and monitoring. We explore the feasibility of using this platform for mapping land cover types in topographically complex terrain with highly mixed vegetation types (Nor Yauyos Cochas Landscape Reserve located in the central Andes of Peru) using classification machine learning (ML) algorithms in combination with different sets of remote sensing data. The algorithms were trained using 3601 sampling pixels of (a) normalized spectral bands between the visible and near infrared spectrum of the Landsat 8 OLI sensor for the 2018 period, (b) spectral indices of vegetation, soil, water, snow, burned areas and bare ground and (c) topographic-derived indices (elevation, slope and aspect). Six ML algorithms were tested, including CART, random forest, gradient tree boosting, minimum distance, naïve Bayes and support vector machine. The results reveal that ML algorithms produce accurate classifications when spectral bands are used in conjunction with topographic indices, resulting in better discrimination among classes with similar spectral signatures such as pajonal (tussock grass-dominated cover) and short grasses or rocky groups, and moraines, agricultural and forested areas. The model with the highest explanatory power was obtained from the combination of spectral bands and topographic indices using the random forest algorithm (Kappa = 0.81). Our study presents a first approach of its kind in topographically complex Cordilleran terrain and we show that GEE is particularly useful in large-scale land cover mapping and monitoring in mountainous ecosystems subject to rapid changes and conversions, with replicability and scalability to other areas with similar characteristics.

Land use/Land Cover Dynamics in Pioneer Fronts and Implications on Livelihoods: Insights from Nkuh and Nkuv on the Bui Plateau

Land use dynamics in pioneer fronts in Sub-Saharan Africa is recurrent; yet, stakeholder interests and the various changes in land-use practices are still to be fully mainstreamed in resource use practices. The implications for livelihood improvement are indicative of the attracting force of these areas to different stakeholders. This study sets out to: 1. Map land use and land cover change in Nkuh and Nkuv pioneer fronts in the Bui Plateau; 2. Identify stakeholders and interests in pioneer fronts; 3. Understand land use change practices and implications for livelihoods. The study used diachronic analysis accompanied by 7 focus group discussions, 9 key informant interviews across native authorities and some government delegations on thematic analysis and narratives. The results revealed that small agroplantations are the major land-use changes from the existing rangelands and remnants of natural forests. Farmers from neighboring villages have migrated and settled in pioneer fronts, practicing agriculture through expansion, stabilization and consolidation. Diversification and adoption of new techniques like agro-forestry, agro-plantations (oil palms, fruit trees, cocoa) is growing amongst farmers, especially among return migrants, bureaucrats and land-use change/ land-use practices have important implication for livelihood improvement for the population implicated with an increase in output. Further studies can focus on how local endogenous processes and power asymmetries can improve resource use outcomes.

Evaluation of land cover changes and secondary ecological succession of typical agroforestry landscapes in Phu Yen Province

The study of land cover changes as well as landscape succession has theoretical and practical significance. Determining the characteristics, causes, and trends of the changes and succession of a landscape allows for assessing the natural and human characteristics of the territory. On this basis, appropriate solutions and policies in resource management, socioeconomic development, and environmental protection are available for each locality. Applying and integrating remote sensing data with field investigations and surveys improves the accuracy and reliability of research results. From the establishment of a land cover change map based on land cover map data collected in 2010, 2015, and 2020, variations in the land cover types of Phu Yen Province were observed. In the period 2010–2015, the area of natural forests and grasslands, shrubs, and bare lands decreased rapidly, whereas the covered areas of intentionally planted forests, industrial trees, and crops increased significantly. The secondary ecological succession of the agroforestry landscape of Phu Yen Province in the last 50–60 years occurred under the strong influence of human activities. Under the impact of chemical warfare and policies and farming practices of the local people, several evergreen broad-leaved tropical forest landscape units have undergone four to six stages of succession, with the interweaving of the economic development policy of Phu Yen Province and farming practices of the local people, making the structure and length of each period and succession very different.

Estimating land cover map accuracy and area uncertainty using a confusion matrix: A case study in Kalimantan, Indonesia

Remote sensing is widely used to generate land cover maps, but the maps derived from remote sensing often produce accuracy below expectations for map error. Therefore, quantifying map accuracy is essential for reporting the precision of an estimated area. This study describes a simple framework for assessing map accuracy and estimating land cover area uncertainty for a land cover changes map for Kalimantan in 2012-2018. This study compared simple random sampling and stratified random sampling to determine suitable procedures for estimating accuracy and area uncertainty. The validation relies on the visual assessment of high spatial resolution images such as SPOT 6/7 and high-resolution temporal images from Open Foris Collect Earth. Our results showed that the land cover change map assessed using random sampling had an overall accuracy of 74% while using stratified random sampling had an overall accuracy of 75%. Thus, for tropical regions with high cloud cover, we recommend using stratified random sampling. The major source of map error was in differentiating between native forest and plantation areas. Future map improvement requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.