Remote Sensing Information Research Articles

Assessment of Suitability of Gridded Precipitation Data for Hydrological Simulation in Eastern Himalaya: A Case Study

Gridded precipitation datasets have been effectively employed in hydrological modeling in absence of gauge data. The study assessed the applicability of five spatially distributed precipitation datasets, Indian Meteorological Department [IMD] (gauge-interpolated), Climate Forecast System Reanalysis [CFSR] (reanalysis), Tropical Rainfall Measuring Mission [TRMM] (satellite-based), Precipitation Estimation From Remotely Sensed Information using Artificial Neural Networks [PERSIANN-CDR] (satellite-based), and Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources [APHRODITE] (gauge-interpolated), for hydrological modeling in an Eastern Himalayan basin. These gridded datasets were input to the Soil and Water Assessment Tool (SWAT), which was calibrated using the SWAT-CUP SUFI2 algorithm. Based on monthly simulated results, the CFSR gridded dataset outperformed others. Streamflow underprediction was also acceptable for the entire study period. IMD and TRMM performed satisfactorily in calibration but failed to perform in validation. APHRODITE and PERSIANN showed good correlation, but due to the overall low rainfall estimation, the data failed to produce satisfactory results and hence is considered unsuitable for hydrological simulation. The TRMM model simulation had the best overall trend against the observed data but failed to match the peaks. The study concluded that CFSR can be alternatively used for modeling in the absence of gauge data for the mountainous river basins of Eastern Himalaya.

The Relationship between the Development of Population Growth and Density and Land Use in Irbid Governorate using Remote Sensing and Geographic Information System

The Relationship between the Development of Population Growth and Density and Land Use in Irbid Governorate using Remote Sensing and Geographic Information System

Urban heat island relation with land use land cover change in Hetauda sub-metropolitan city of Nepal

Urban areas are expanding globally at the expense of natural productive land which affects the quality of life of urban residents. Hetauda sub-metropolitan city of Nepal has been undergoing rapid urban growth for the last few decades causing local climatic effects such as land surface temperature (LST) variation. Thus, exploring spatio-temporal changes in land use, land cover (LULC), and urban heat island (UHI) analysis could be an effective means of exposing local environmental issues caused by anthropogenic activities. Development in thermal Remote Sensing and Geographic Information System (GIS) has enabled the monitoring of spatial LST, UHI, and its correlation to LULC. We used Landsat 8 OLI/TIRS satellite data and a supervised classification algorithm for land use land classification for the years 1995, 2008, and 2018 in Arc map software. The spatial pattern of LST was obtained through mathematical calculation of the thermal band of Landsat images. Correlation analysis was applied to explore the relationship between LST, LULC types, and LUCL indices. The LST was higher for urban/built-up and cultivated land use types. There was approximately 4°C mean LST variation for all three years of study. The regression analysis showed a positive correlation of urban/built-up with the Normal Difference Built-Up Index (NDBI) however a negative correlation with the Normal Difference Vegetation Index (NDVI) which implies that green structure weakens the UHI effects while urban/built-up areas strengthen the UHI. Overall, the study can be useful for urban planners in sustainable urban planning and management as well as to raise public awareness of climate change and the warming effect.

Decadal Change in Land Use and Land Cover Dynamics: A Comprehensive Assessment of the Kharun Catchment

Land Use and Land Cover (LULC) changes exert profound influences on environmental and resource dynamics, particularly in developing regions experiencing rapid urbanization and agricultural expansion. This study investigates LULC changes over a 30-year period (1992–2022) in the Kharun catchment, part of the upper Mahanadi basin in Chhattisgarh, India. Encompassing 4,143.17 km² across six districts, the catchment plays a critical role in supporting agricultural and urban activities. Landsat imagery from 1992, 2002, 2012, and 2022 was analyzed using supervised classification with Remote Sensing (RS) and Geographic Information System (GIS) tools to categorize six LULC types: water bodies, forests, urban areas, upland paddy fields, lowland paddy fields, and barren land. The results reveal dynamic and significant LULC changes. In 1992, water bodies (2.00%), forests (4.86%), and urban areas (1.92%) occupied smaller portions of the landscape, while barren land accounted for 28.64%. By 2002, water bodies slightly increased to 2.12%, forests decreased to 3.98%, urban areas expanded to 3.21%, and barren land reduced to 27.80%. Urban expansion became pronounced by 2012, with urban areas rising to 6.42%, while forests further declined to 3.11%, and barren land reduced to 26.30%. By 2022, water bodies expanded to 2.63%, forests declined significantly to 2.77%, and urban areas surged to 9.35%, indicative of extensive deforestation and urbanization. Barren land continued to decrease, covering 24.15%. These findings highlight the urgent need for integrated land-use management strategies that balance developmental needs with environmental conservation. Policy frameworks should prioritize reforestation initiatives, sustainable urban planning, and soil and water conservation practices to mitigate the adverse effects of deforestation and land degradation. Regular and systematic monitoring of LULC dynamics at the catchment scale is essential to support informed decision-making, ensuring ecological integrity and socio-economic sustainability in rapidly developing regions.

Geomorphological Characteristics of a Hydrographic Basin using RS and GIS on the Example of the Venna River (Maharashtra, India)

Assessing the impact of geomorphological processes on a catchment's hydrology necessitates a quantitative analysis of its geometry. This can be effectively achieved using remote sensing (RS) and geographic information systems (GIS), which have become increasingly popular for supporting strategists and decision-makers in making precise and efficient plans and decisions. A morphometric analysis of the Venna River basin was conducted using RS and geographic information systems GIS. This study aims to assist local communities in effectively utilizing land and water resources for the sustainable development of the basin area. The drainage network was developed by digitizing Survey of India (SOI) toposheets. The Venna River basin, covering an area of 318.96 km², exhibits a dendritic drainage pattern with a 5thorder river system dominated by lower-order streams. A mean bifurcation ratio of 6.02 indicates undulating topography with structural influences on drainage. The basin's elongated shape is reflected in its circulatory ratio (0.3) and elongation ratio (0.38), while a form factor of 0.12 suggests low peak flows over extended durations. Low drainage density value of 0.6, indicates that basin has high permeability, slow surface runoff and value of constant of channel maintenance of 1.66 indicates basin has moderate slopes and effective infiltration. The relief ranges from 619 m to 1380 m, with a relief ratio of 0.015, indicating less resistant rock types. A ruggedness number of 0.46 suggests moderately rough terrain, allowing for some erosion potential while highlighting the basin's capacity for water retention, infiltration and sustainable resource management. The areal parameters, such as the form factor, circulatory ratio, elongation ratio and drainage density, suggest elongated basin geometry with high drainage intensity, implying slower runoff and moderate potential for groundwater recharge. The results of this study are valuable for effective watershed management, erosion control, and informed decision-making in land use planning and engineering projects.

Groundwater potential zones delineation in Oued Zdin basin using GIS, RS and hierarchical analysis process

Water scarcity poses a significant challenge, particularly in regions with limited rainfall such as Algeria, where groundwater plays a crucial role in supporting both daily life and economic activities. This research aims to evaluate the groundwater potential in the Oued Zdin basin in northern Algeria by utilizing advanced geomatics methods, particularly the Analytic Hierarchy Process (AHP). Through the integration of Remote Sensing (RS) and Geographic Information Systems (GIS), the study incorporates multiple datasets, including rainfall patterns, topography, geology, drainage networks, land use, and hydrological data to assess areas with high groundwater potential. By applying AHP, the study assigns relative importance to these factors, creating a groundwater potential map that classifies the region into very high, high, low, and poor potential zones. The results indicate that 7% of the basin has very high potential for groundwater recharge, 33% has high potential, while 56% is categorized as low potential, and 4% falls under poor potential. The accuracy of the results is validated through comparison with existing well data, which aligns with the identified high-potential zones. The research demonstrates that combining GIS, RS, and AHP is an effective approach for mapping groundwater potential, offering valuable insights for sustainable water resource management in areas experiencing water scarcity. This methodology presents a scalable model that can be applied to similar regions facing groundwater challenges.

Use of HAND Model for Estimating Flood-Prone in Serawai Basins Base on Remote Sensing and Sistem Information Geography

A river basin's flood-prone mapping is essential for managing flood risks, developing mitigation plans, and developing flood forecasting and warning systems, among other things. This research uses the HAND model to estimate the level of flood-prone and its distribution in watersheds. The method used is survey and image interpretation. The data used is DEM imagery with a resolution of 10 meters. Data analysis uses spatial analysis, which includes elevation, hydrological analysis, fill, flow accumulation, flow direction, flow distance, and minus statistical analysis. The results showed that the Serawai watershed has five classes: very prone, prone, moderate, not prone, and very not prone. The very prone class has an area of 112,213.82 ha (65.41%), including Tontang, Sedaha, Nanga Serawai, Begori, Nanga Lekawai, Surga, Buntut Ponte, and Nanga Segulang village. The prone class has an area of 29,356.65 ha (17.14%), spread across the village of part of Beurgea, part of Nanga Segulang, Nanga Jelundung, and part of Tontang village. The moderate level has an area of 18,971.52 ha (11.08%), spread across Tontang, part of Nanga Jelundung, and part of Baras Nabun village. The area with a not-prone is 7,996.20 ha (4.67%), spread across Baras Nabun and parts of Nanga Jelundung village. For areas that are very not prone, they have an area of 3,004.20 (1.75%), spread over parts of the villages of Sedaha, parts of Baras Nabun, and Nanga Jelundung. Based on the research results, it can be concluded that the HAND Model is an effective and easy-to-use model for estimating flood-prone areas.

THE RELATIONSHIP BETWEEN GEODIVERSITY AND BIODIVERSITY – A THEORETICAL APPROACH

The relationship between geodiversity and biodiversity is often considered within geoecological frameworks. Their interaction within real space forms the natural structure of landscapes. Although they are defined as two separate and equivalent systems with different structures, their relationship makes them interdependent, with geospatial distribution being the common point of contact. The term “geodiversity” is relatively recent, defined as the desire to express the opponent of the diversity of the living world – biodiversity. Thus, through a holistic concept, it is possible to observe multiple levels of interaction between geodiversity and biotic resources, revealing their interrelationship. The challenges of researching both geodiversity and biodiversity are characterized by similar shortcomings. Based on the previous researches of different authors, the work aims to define in the best possible way a biotic-abiotic relationship as an essential component in the concept of nature, the sphere of their connection and joint action. However, due to the numerous fields of their interaction, the work only partially defines them, without analyzing each field of interaction, in detail. In addition, the connection of these two concepts also requires interdisciplinary cooperation, the goal of which must be to improve the understanding of biodiversity and geodiversity, and their integration in evaluation, with the common goal of protecting and preserving nature as a whole and its individual parts. To a significant extent, these can be addressed through the application of geoinformatics tools, methods, and techniques, especially Remote Sensing (RS) and Geographic Information Systems (GIS), which offer the possibility of more adequate evaluation and interpretation of results.

Meteorological and satellite-based data for drought prediction using data-driven model

This work presents a data-driven model, the Artificial Neural Network-Multilayer Perceptron Neural Network (ANN-MLP), for use in meteorological drought deciles index (DDI) predictions over various climatic sub-zone. Two types of rainfall data from meteorological weather stations (WSs) and satellite-based estimates of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network) were adopted. This work considered the calculated DDI (DDI original) from WSs to train and develop the proposed algorithm at three sub-zones (ANN-MLP-DDI models). The newly developed model was tested for DDI prediction using PERSIANN, and compared with the calculated DDI original from WSs. The results positively revealed that the ANN-MLP-DDI models showed high performance (Correlation coefficient r= 0.981) for DDI prediction against the DDI original from WSs. It can be concluded that data-driven models are feasible for drought prediction, and this work could help water managers in mitigating drought impacts and in providing information for policy makers

Assessment of Hydrological Resource Management Using Remote Sensing (RS) and Geographic Information Systems (GIS); Wadi Sannur Valley, Beni-Suef, Egypt.

Assessment of Hydrological Resource Management Using Remote Sensing (RS) and Geographic Information Systems (GIS); Wadi Sannur Valley, Beni-Suef, Egypt.

Assessment of groundwater potential zones for hard rock area of sabi river basin using an integrated approach of remote sensing, GIS and AHP techniques

The study was conducted to assess groundwater potential zones (GWPZs) for hard rock area of Sabi river basin for the sustainable development. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as crucial methods in retrieval, monitoring and conserving groundwater resources were used along with Analytical Hierarchy Process (AHP) to demarcate the GWPZs. In order to assess GWP in the study area, eight thematic maps: drainage density, geomorphology, geology, rainfall, land use/land cover, lineament density, slope and soil were prepared using RS and conventional data. The AHP was applied to assign the weightages for each thematic map and its sub classes. Then, the thematic layers were overlaid using the weighted overlay analysis means of ArcGIS 10.4 to assess the GWPZ map of Sabi basin. The Sabi basin was categorized into five classes of GWPZ as very good (0.01%), good (24.72%), moderate (56.54%), poor (10.45%) and very poor (8.28%). The accuracy of produced GWPZ map was assessed with well yield data by agreement scheme using ground water prospects map and by using ROC curve analysis through R-studio software which indicates the satisfactory GWPZ map prediction accuracy. This study provides valuable database in planning and development of groundwater recharging and to delineate locations of potential artificial recharge sites in the hard rock regions and semi -arid areas so that appropriate structures could be planned and constructed to manage aquifer recharge for sustainable groundwater management in the future.

Shoreline Change Analysis in New Washington, Aklan using Digital Shoreline Analysis System (DSAS)

Abstract. Shoreline changes are a critical indicator of coastal vulnerability, impacted by natural processes and human-induced factors such as climate change. This study investigates shoreline changes in New Washington, Aklan, Philippines, focusing on erosion and accretion patterns since 1985. With the use of satellite imagery, coupled with Remote Sensing and Geographical Information System (GIS) techniques, this study examines the spatio-temporal variability in erosion and accretion patterns. Through the Digital Shoreline Analysis System (DSAS), a quantitative analysis of shoreline change rates was conducted. Using four (4) DSAS change metrics such as Endpoint rate (EPR), Linear Regression Rate (LRR), Shoreline Change Envelope (SCE), and Net Movement Shoreline (NMS), it was identified that the shoreline change in Bakhawan Park area is due to its location being at the mouth of Aklan River. Most of New Washington’s shoreline experiences erosion with 0-1.46 m/yr from LRR, eroding at most 40m of sediments in thirty years; evident along areas of Brgy. Tambac and Poblacion. The largest shoreline deviation can be observed in the areas located near the mouth of Batan Bay with 153 meters since 1985.

Transformer-embedded 1D VGG convolutional neural network for regional landslides detection boosted by multichannel data inputs

Up-to-date studies have proved the effectiveness of Convolutional Neural Networks (CNN) in landslide detection. With the rapid development of Remote Sensing and Geographic Information System technologies, an increasing amount of spectral and non-spectral information is available for CNN modeling. It offering a comprehensive perspective for landslide detection, but also presents challenges to CNNs, especially in efficiently learning long-range feature associations. Therefore, we proposed a novel Transformer-improved VGG network (Trans-VGG). It takes spectral (RGB images) and non-spectral information (elevation, slope, and PCA components) as data inputs and integrating both local and global feature in modeling. The method is tested in two landslide cluster areas in Litang County, China. The results in site a show that the Trans-VGG model demonstrates an improvement in F1-score, ranging from 4% to 21%, compared with the conventional machine learning and CNN models. The validation result in site b further proved the validity of our proposed method.

Recent innovations in land capability classification for sustainable development: a brief overview.

Land is a finite resource that must be managed wisely to ensure its sustainability. Consequently, land evaluation has become essential. Identifying and utilizing productive capacity of land efficiently and profitably is crucial; otherwise, resource degradation can severely impact natural ecosystems and food production. Over the years, various methodologies have been employed to assess land resources, and one such method is the Land Capability Classification (LCC). LCC is a widely used and fundamental approach to land-use planning, traditionally assessing land based on its intrinsic qualities and climate. This study aims to highlight new approaches and developments in land evaluation techniques. It provides a brief overview of recent technological and scientific advancements integrated into land evaluation. The findings suggest that LCC alone is inadequate for precise land assessment, emphasizing the need to integrate new technologies. Remote Sensing (RS) and Geographic Information System (GIS) technologies are becoming increasingly significant. Integrating various software, decision-making systems, and mathematical models can also enhance the accuracy of land assessment results. The continuous advancement of GIS and remote sensing technologies is paving the way for new tools to facilitate natural resource mapping, appraisal, surveillance, and management. Utilizing these technologies for future projections will be highly beneficial in accurately assessing the long-term impacts of current land management practices.

Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye

ABSTRACTPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Remote Sensing - Based Analysis of LST and NDVI Correlation for the Coolest and Hottest Month of 2023 in Khanaqen City

Urban land cover characteristics often lead to variations in surface temperature. Remote Sensing (RS) and Geographic Information Systems (GIS) are valuable tools for obtaining detailed information about surface indices. This study evaluates the temporal variation of Land Surface Temperature (LST) in Khanaqen city, located in South eastern Iraqi Kurdistan and Eastern Iraq. The research investigates the relationship between LST and the Normalized Difference Vegetation Index (NDVI) for the coolest and hottest month of 2023 using Landsat images from the USGS Earth Explorer. The objective is to explore how green spaces influence LST during the cool, humid winter and hot, dry summer. Two satellite images from Landsat 9 were used to retrieve LST and NDVI for the study periods. The results indicate that green spaces significantly reduce LST in July, so that the result of the correlation analysis of variables, significant inverse correlation between LST and NDVI (R = -0.53, R2 = 0.28 and P value = 0.00) highlights the effect of vegetation cooling. Conversely, January shows a modest correlation (R = -0.01, R2 = 0.00, and P value = 0.90), indicating a minimal effect of vegetation cover on LST.

APPLICATION OF GEO-INFORMATICS IN THE MAPPING OF AREAS VULNERABLE TO SURFACE WATER STRESS IN KADUNA STATE, NIGERIA

The main purpose of this paper is to identify water-stressed areas as a response to land use/land cover (LULC), climate variability, and increasing population in Kaduna State using Remote Sensing and Geographic Information systems. To achieve this aim, the paper set out to identify/map out areas vulnerable to water stress in the study area. The Meteorological data for the study were sourced from twelve remotely sensed stations around Kaduna State for thirty-seven years (1984 to 2021), land use data from remotely sensed satellite imageries that covered thirty-seven years, and projected population for thirty-seven years respectively. There are several indices used to compute water stress and the paper opted for indices that could monitor climate variability and surface water resources. Thereafter, the six indicators were standardized, weighted, and aggregated in Microsoft Excel, and imported into the ArcGIS 10.2.1 environment, and a weightier vulnerability model analysis was used to determine the weights of each variable’s contribution to water stress. The result achieved indicated that: 54.41% of the entire study area is vulnerable to water stress, 15.80% is moderately vulnerable, 26.23% is less vulnerable and 3.56% is not vulnerable to water stress within the period under study. Furthermore, Birnin Gwari, Chikun, Igabi, Ikara, and Kajuru Local Government Areas (LGAs) were the most vulnerable areas in the State while not vulnerable areas are; Kauru, Kagarko, and Jaba LGAs. The paper recommends the implementation of water conservation measures which include the creation of wetlands and shelter belts in areas vulnerable to water stress.

Potential Sites for Rainwater Harvesting Focusing on the Sustainable Development Goals Using Remote Sensing and Geographical Information System

An innovative way to combat water scarcity brought on by population increase and climate change is rainwater harvesting (RWH), particularly in arid and semiarid areas. Currently, Pakistan is facing major water issues due to underprivileged water resource management, climate change, land use changes, and the sustainability of local water resources. This research aims to find out the suitable sites and options for RWH structures in the Quetta district of Pakistan by integrating the depression depth technique, Boolean analysis, and weighted linear combination (WLC) with hydrological modeling (HM), multicriteria analysis (MCA), a geographic information system (GIS), and remote sensing (RS). To find suitable sites for RWH, a collection of twelve (12) thematic layers were used, including the slope (SL), land use land cover (LULC), subarea (SA), runoff depth (RD), drainage density (DD), lineament density (LD), infiltration number (IFN), distance from built-up area (DB), distance from roads (DR), distance from lakes (DL), maximum flow distance (MFD), and topographic wetness index (TWI). The Boolean analysis and WLC approach were integrated in the GIS environment. The consistency ratio (CR) was calculated to make sure the assigned weights to thematic layers were consistent. Overall, results show that 6.36% (167.418 km2), 14.34% (377.284 km2), 16.36% (430.444 km2), 18.92% (497.663 km2), and 18.64% (490.224 km2) of the area are in the categories of very high, high, moderate, low, and very low suitability, respectively, for RWH. RWH potential is restricted to 25.35% (666.86 km2) of the area. This research also identifies the five (5) best locations for checking dams and the ten (10) best locations for percolation tanks on the streams. The conducted suitability analysis will assist stakeholders in selecting the optimal locations for RWH structures, facilitating the storage of water, and addressing the severe water scarcity prevalent in the area. This study proposes a novel approach to handle the problems of water shortage in conjunction with environmental and socioeconomic pressures in order to achieve the sustainable development goals (SDGs).

Estimation of Forest Above-Ground Biomass in the Study Area of Greater Khingan Ecological Station with Integration of Airborne LiDAR, Landsat 8 OLI, and Hyperspectral Remote Sensing Data

Accurate estimation of forest above-ground biomass (AGB) is significant for understanding changes in global carbon storage and addressing climate change. This study focuses on 53 samples of natural forests at the Greater Khingan Ecological Station, exploring the potential of integrating Canopy Height Model (CHM) with multi-source remote sensing (RS) data—airborne LiDAR, Landsat 8 OLI, and hyperspectral data to estimate forest AGB. Firstly, RS features with strong horizontal and vertical correlation with the forests AGB are optimized by a partial least squares algorithm (PLSR). Then, multivariate linear stepwise regression (MLSR) and K-nearest neighbor with fast iterative features selection (KNN-FIFS) are applied to estimate forest AGB using seven different data combinations. Finally, the leave-one-out cross-validation method is selected for the validation of the estimation results. The results are as follows: (1) When forest AGB is estimated using a single data source, the inversion results of using LiDAR are better, with R2 = 0.76 and RMSE = 21.78 Mg/ha. (2) The estimation accuracy of two models showed obvious improvement after using fused CHM into RS information. The MLSR model showed the best performance, with R2 increased by 0.41 and RMSE decreased to 14.15 Mg/ha. (3) The estimation results based on the KNN-FIFS model using the combined data of LiDAR, CHM + Landsat 8 OLI, and CHM + Hyperspectral imaging were the best in this study, with R2 = 0.85 and RMSE = 18.17 Mg/ha. The results of the study show that fusing CHM into multi-spectral data and hyperspectral data can improve the estimation accuracy a lot; the forest AGB estimation accuracies of the multi-source RS data are better than the single data source. This study provides an effective method for estimating forest AGB using multi-source data integrated with CHM to improve estimation accuracy.