Monitoring Land Cover Change Research Articles

Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.

Spatial Assessment of Urban Growth on Green Spaces in Rwanda: An insight from Rebero Mountain Landscape in Kicukiro District, City of Kigali

Green space is a crucial component of sustainable urban development that provides urban dwellers with several environmental and social benefits. This study investigates the impact of urban growth on green space management on hilly landscape using a case study of Rebero Mountain in Kicukiro District, Kigali City. The study used medium resolution satellite imagery (Landsat for 2000, 2010, and 2021) and high-resolution imagery (Quick Bird 2010 and Worldview 2 images of 2015) to assess Land Uses Land Covers (LULC) dynamics. To investigate the local community's perception on the effects of urban growth on green space, interviews were conducted. The LULC changes detection revealed a significant decrease in green space with an increase in the built-up areas. The urbanized area has been increasing from 7,422.10 Ha up to 14,730.94 Ha during the last two decades. The increasing built-up area was in parallel with reduction of green space, specifically 44 Ha (-6.7%) of urban forest and 255 Ha (- 26.2%) of agricultural land, for the period between 2000 and 2021. The reduction of green cover had a negative impact on the environment, such as soil erosion and biodiversity loss. In order to support decision-makers in fostering the development of sustainable and green smart cities, a spatially explicit assessment of the availability, management, and accessibility of urban green areas is also important. Based on this research findings, the restoration of green spaces, including the need for land-use and land-cover change monitoring, practical government physical planning interventions, and an inclusive strategy for managing the city's green spaces that involves NGOs and citizens, and advocates for green city development are strongly recommended.

Spatio-Temporal Change Detection Analysis of Land Use Land Cover of Bathinda District, Punjab, India

ABSTRACT: Due to rapid industrialization and urban sprawl in the last few decades, the land use pattern and its consumption takes place at a large scale that could lead to problems such as over-exploitation of land resources, food insecurity and pollution. It becomes imperative to carry out monitoring and subsequent modelling of land use land cover (LULC) changes. An attempt was made to study the changes in the LULC pattern of district of Bathinda, Punjab, India. Remote sensing (RS) and geographical information system (GIS) were used to perform the analysis of satellite data using image processing and classification procedures. For preparing LULC maps, supervised classification was carried out using maximum likelihood classification (MLC) algorithm, aided with Earth Resources Data Analysis System (ERDAS) Imagine 2014 and ArcGIS 10.3 software. Further, change detection study was done using multi-temporal Linear Imaging Self Scanning Sensor-III (LISS-III) data sets of the year 2006 and 2018 to analyze the temporal changes. It was observed that the region is occupied by various ground features such as water, built-up area, agricultural land, vegetation/trees and fallow land. The results revealed that the area under water bodies have increased by 0.413km2 in 2018. The built-up areas including human settlements, commercial infrastructures, roads and other pavements, have increased from 584.448km2 to 852.140km2 between 2006 and 2018, whereas the agricultural land has reduced from 2686.121km2 to 2398.384km2 during the period. The area under vegetation (trees) indicated that there was an increasing trend from 28.490km2 to 54.678km2 during 12years of time span whereas, the fallow land/barren land showed a decreasing trend from 26.361km2 to 18.367km2. It is suggested that the LULC change detection studies are very significant to conserve the land resources and to avoid further degradation.

Super resolution of historic Landsat imagery using a dual generative adversarial network (GAN) model with CubeSat constellation imagery for spatially enhanced long-term vegetation monitoring

Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earth’s surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30–60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In order to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) approach combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.90, for the dual RSS-GAN maps vs in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from − 5.8 % to 0.3 % and − 12.4 % to − 3.7 % for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which reflects fine-scale information from Planet Fusion. Finally, the dual RSS-GAN maps showed both spatial enhancement and reducing the underestimation of vegetation index in historic Landsat dataset from 1984. This study presents a new approach for resolving sub-pixel spatial information in Landsat images.

Monitoring land cover changes and droughts using statistical analysis and multi-sensor remote sensing data

Climate change is of paramount importance for ecosystems, especially in arid and semi-arid areas. The major aim of the current study is to monitor vegetation and land use changes, and run drought assessment using field and satellite data. The main precipitation proportions in the studied region are influenced by the Westerlies, meaning that any variations in these precipitation systems significantly impact the region. The utilized data entailed MODIS images for 16- and 8-day intervals between 2000 and 2013, TM and OLI sensor images recorded in 1985 and 2013, precipitation network data of TRMM satellite between 2000 and 2013, and synoptic data 32-year period. The Mann-Kendall (MK) test was used to monitor temporal changes in meteorological station data in annual and seasonal scales. The results indicated that there was a downward trend in 50% of the meteorological stations in the annual scale. This falling trend was statistically significant at the level of 95%. At the end, drought was assessed using PCI, APCI, VSWI, and NVSWI. The results showed that vegetation, forest, pasture, and agriculture areas recorded the strongest correlations with initial precipitation at the beginning of the study. Based on interactions among various factors influencing vegetation indices, reduction in green vegetation, especially the area of oak forests in the studied period, is around 95,744 hectares, which is attributed to lower precipitation rate. Increasing of agricultural land and water zones during the studied years is the result of human management and depends on how surface and underground water resources are exploited.

Monitoring Land Cover Changes in Tehran City over 5 years (2018 to 2022) using Remote Sensing based Spatial Information

Rapid economic growth has increased the speed of resource changes and many of these changes have rapid and harmful effects Natural environment such as agriculture, forest, water resources, value Cultural things such as historical landscape and health Humans have put Land use changes directly It changes the resources of the earth, which affects the temperature and humidity causes changes in the climate and weather of the region as well It reduces cultivated areas [1]. Considering that one of the main prerequisites for the optimal use of land, obtaining information One of the patterns of land use over time is map design. Related specializations are one of the most important goals in management it is considered natural resource [2]. In recent years, preparing land use maps by digital classification of remote sensing data have been adverted as appropriate alternative for using this type of maps. Remote sensing is a modern and useful technique in updating land use maps and detecting new changes. In this research ArcGIS pro used for classification that is one of the most accurate and updated software for remote sensing’s process for detecting 4 main type of classes in Tehran city in IRAN. standard accuracy in satellite image processing is important criteria in this study with standard kappa coefficient accuracy, and overall accuracy of data calculated for each maps, by considering 4 essential classes in a major city and converted to maps of changes in linear regression concluded that build-up class have a significant slope increase 3422/3 (hectares), plant class is improving during the study period as 2821/71 (hectares) but these increment are inhomogeneous, water class has sharp drop as 443.52 (hectares), Then the most of decrement is for the barren area which named soil class as 5800.48 (hectares). Part of accuracy in this research depends on severity of the numbers of test samples which given for classification that are more than5000 pixels to assessment reliable results. According to the standards of kappa coefficient that provided in USGS earth data site all off maps are acceptable.

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.

Assessments of coastal erosion in rivers state, Nigeria, using global cover 30

Coastal dynamics and erosion are phenomena that have a considerable impact on inhabitants of coastal areas, regardless of whether the ac-tivity driving them is natural or manmade. Because a high percentage of the world's population lives along the shoreline, a raise in sea level will have a substantial effect on many people. Additionally, it will have an effect on low-lying coastal areas and islands. Using satellites for coastal management and monitoring changes in land cover and land use, it is possible to gain a better understanding of global changes in Coastal Erosion, especially in Rivers State, Nigeria. This study investigated coastal erosion in Rivers state, Nigeria, utilizing Global cover 30 land cover data. GIS and post-processing data were utilized to track changes occurring over the twenty-year period from 2000 to 2020. Two distinct temporal frames were identified within the given time span. The result of the analysis indicated that there was more erosion as the gain by the major Rivers indicated erosion along the coastline, Furthermore, changes of 47151000m2 were observed within 2000 and 2020, that is a period of twenty years with over 47151km2 land lost within Rivers state coastline. It is suggested that regular utilization of remote sensing data and geographic information system together with ground surveys be employed to monitor erosion rates, thereby assist-ing in pinpointing the most advantageous sites for protective barriers along the Nigerian coast in an effort to curtail coastal erosion.

Multitemporal Incidence of Landscape Fragmentation in a Protected Area of Central Andean Ecuador

Monitoring land cover changes in protected areas is crucial to control the conservation efficiency of biodiversity and natural ecosystem conditions, especially in Ecuador, one of the most megadiverse countries in the world. Therefore, the purpose of the present study has been to estimate spatiotemporal changes in the landscape and the level of fragmentation using remote sensing in Llanganates National Park (PNL), a protected area in central Andean Ecuador. To obtain land cover, satellite images were processed using the Maximum Likelihood Classification (MLC) algorithm. After statistical analysis, it was encountered that there is no significant difference in land cover change between the years 1991 to 2016 nor among the three watersheds, while the level of fragmentation in the PNL is low. Land cover changes in the study area are not evident, as it is a protected area where ecosystems are usually expected to maintain their initial conditions over time. Therefore, with these results it has been concluded that the biodiversity and landscape conservation processes in the PNL are effective.

Monitoring Land Cover Change by Leveraging a Dynamic Service-Oriented Computing Model

Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users’ requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection.

Analysis of the relationship between urban heat island (UHI) phenomenon and land cover change in Medan city using Landsat satellite imagery

The increase in population accompanied by an increase in the amount of built-up land has led to an increase in the LST of many cities, including the city of Medan. The city of Medan is the capital of the province of North Sumatra, Indonesia. The utilization of Landsat satellite imagery can be used to monitor land cover changes and LST changes. This study aims to obtain information due to changes in land cover to LST in Medan City. The use of thermal Infrared band in Landsat satellite imagery can monitor LST changes that have occurred. The results of the study show that in the period 2000 to 2019 there has been an increase in the area of built-up land and a decrease in the area of vegetation in the city of Medan. In 2000 the LST ranged from 12-30°C while in 2019 it ranged from 18-33°C. This has triggered the occurrence of UHI, especially in urban centers in the city of Medan.

A Triple-Stream Network With Cross-Stage Feature Fusion for High-Resolution Image Change Detection

Change detection (CD) based on high-resolution remote sensing images can be used to monitor land cover changes, which is an important and challenging topic in the remote sensing field. In recent years, with the development of deep learning, CD methods based on deep learning have achieved good results in the field of CD. However, most current CD methods use single- or dual-stream networks to extract change features, which is insufficient to extract and learn bitemporal change information thoroughly. This article proposes a triple-stream network (TSNet) with cross-stage feature fusion for CD in high-resolution bitemporal remote sensing images. First, to obtain highly representative deep features in the original image, we perform feature extraction on bitemporal remote sensing images and their concatenated image with a dual-stream encoder and a single-stream encoder, respectively. Then, the bitemporal multiscale features extracted by the dual-stream encoder are input into a multistage bidirectional convolutional gated recurrent unit (MSBC_GRU) feature fusion module, allowing the network to learn the change information in a cross-stage manner. In addition, we use a dual-channel attention module to fuse the features extracted by dual- and single-stream encoders, improving the network’s ability to discriminate changed features. The effectiveness of TSNet is demonstrated with three publicly available CD datasets. The extensive experimental results demonstrate that the proposed method achieves the state-of-the-art CD performance on the above three datasets.

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.

Land cover change and utilization of village land forest reserves in Ludewa, Tanzania

Knowledge of land cover change and associated driving factors is important for developing improved management strategies and future monitoring of tropical forests. We present here for the first time the case of two forest reserves (Litwang'ata and Intake) in Ludewa district, Southern Highlands Tanzania. These reserves were recently (2019) declared Village Land Forest Reserves (VLFRs), with their management and ownership devolved to their communities. We used remote sensing and GIS methods to assess spatial-temporal land cover change of the two VLFRs for the period 1996–2016. We also used household interviews (HHIs), focus group discussions (FGDs) and key informants’ interviews (KIIs) to examine the perceived socio-economic drivers of land cover change and the household forest-based incomes derived from the VLFRs. Our findings overall showed that closed woodland decreased by 780.3 ha whereas open woodland, grass/shrub land and agricultural land increased by 700.9 ha, 71.7 ha and 50.9 ha, respectively. Wildfires are perceived as the major drivers of land cover change. To a large extent, local communities utilize VLFRs for their livelihoods, with major resources extracted being firewood, construction poles, fresh water and thatch grass. Of the total forest-based income, logging for timber accounted for about 33% for Intake VLFR users and 36% for villagers who utilize resources from Litwang'ata VLFR. Future monitoring of land cover and livelihood changes is recommended for sustainable forest management.

Review: Monitoring of land cover changes and plant phenology by remote‐sensing in East Asia

AbstractTo achieve a sustainable society and solve the problems caused by climate change and socioeconomic activities, it is necessary to monitor the spatial and temporal variability of ecosystem functions, services, and biodiversity at both regional and global scales. During the last decade, we have seen rapid and significant improvement in satellite and near‐surface remote‐sensing technologies. In this review, we describe how remote‐sensing observations are being used to effectively evaluate the spatial and temporal variability of land use and cover types and of plant phenology in East Asia. We discuss the current status, uncertainties, problems, and future perspectives for terrestrial ecosystem and biodiversity monitoring, and we address the multidisciplinary research by incorporating in situ and satellite observations for East Asia. Recent satellites with advanced optical sensors, such as MultiSpectral Instrument/Sentinel‐2A/2B, Advanced Himawari Imager/Himawari‐8, and PlanetScope, and satellites with synthetic aperture radar, such as Advanced Land Observing Satellite‐2, allow detailed monitoring of land use and cover type changes and plant phenology. These satellite sensors can provide images with high spatial (3–10 m) and high temporal (10 min to 5 days) resolution, at both large (≥1 km2) and small (<1 km2) scales. In particular, researchers are expected to develop discrimination techniques that will enable these advanced satellite technologies to detect evergreen broadleaf trees and ways to estimate the geographic distribution of leaf traits. We encourage more collaborations among ecologists, engineers, and remote‐sensing scientists to share their abundant knowledge and experience and thereby improve the monitoring capabilities.

Vegetational succession assessment in a fragment of the Brazilian Atlantic Forest

Vegetational succession assessment is an important step for better management practices, providing relevant quantitative and qualitative information. With the advancements of remote sensing algorithms and access to data, land use and land cover (LULC) monitoring has become increasingly feasible and important for the evaluation of changes in the landscape at different spatial and temporal scales. This study aims to analyze the vegetation succession achieved by a project funded by the Brazilian Environmental Ministry (Ministério do Meio Ambiente, in Portuguese) intended to recover degraded areas. A 2014 and a 2019 LULC map was generated using high-resolution (10cm) images. Given the great challenge of classifying high-resolution images, three classification algorithms were compared. The techniques to regenerate degraded areas were efficient to increase arboreal vegetation area by more than 30% between 2014 and 2019. Land cover and land use change monitoring is of paramount importance to strengthen sustainable practices, especially in the highly threatened Atlantic Forest biome. This study also shows that funding opportunities are essential for projects that make such actions possible, including the present research and the analysis of environmental regeneration.

An ensemble method for monitoring land cover changes in urban areas using dense Landsat time series data

This study proposes a new method for monitoring land cover change in urban areas using all available Landsat time series data, named the Ensemble of Bidirectional Time Series Analysis (EBTSA). In this method, the bidirectional Continuous Change Detection and Classification (CCDC) and the Chow Test are combined to improve the robustness against data scarcity in earlier times and reduce break detection errors and refine classification results. There are three key stages in this method: break detection using bidirectional CCDs, break refinement using the Chow Test, and bidirectional model integration and classification. The EBTSA method was evaluated over the Tianjin metropolitan area in China using Landsat data from 1989 to 2018. Results show that the proposed method improved spatial and temporal accuracies of both land cover classification and change detection, by reducing the influence of sparser Landsat data in the earlier years and the break detection errors. Using the land cover change results in the Tianjin area obtained using the EBTSA method, we analyzed the spatio-temporal distribution of land cover classification and change detection. It is found from the results that the Tianjin area experienced dramatic urban land changes, characterized by rapid urban expansion and noticeable transition from vegetation to urban land, with diverse changes from urban land to various nonurban land cover types. Results of this study showcase the effectiveness of the proposed method in land cover change monitoring in urban areas, which facilitates a more comprehensive understanding of urban land change dynamics and diversity.

Has global deforestation accelerated due to the COVID-19 pandemic?

As the COVID-19 pandemic unfolded, questions arose as to whether the pandemic would amplify or pacify tropical deforestation. Early reports warned of increased deforestation rates; however, these studies were limited to a few months in 2020 or to selected regions. To better understand how the pandemic influenced tropical deforestation globally, this study used historical deforestation data (2004–2019) from the Terra-i pantropical land cover change monitoring system to project expected deforestation trends for 2020, which were used to determine whether observed deforestation deviated from expected trajectories after the first COVID-19 cases were reported. Time series analyses were conducted at the regional level for the Americas, Africa and Asia and at the country level for Brazil, Colombia, Peru, the Democratic Republic of Congo and Indonesia. Our results suggest that the pandemic did not alter the course of deforestation trends in some countries (e.g., Brazil, Indonesia), while it did in others (e.g., Peru). We posit the importance of monitoring the long-term effects of the pandemic on deforestation trends as countries prioritize economic recovery in the aftermath of the pandemic.

Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images

Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p < 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R2 and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t/ha, while, for Landsat 9, they were 0.87 and 17.91 t/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics.

Monitoring Land Cover Changes in Past 32 Years Using Remote Sensing Technique (Case Study: Pishva Town, Tehran)

Monitoring Land Cover Changes in Past 32 Years Using Remote Sensing Technique (Case Study: Pishva Town, Tehran)