Thematic Mapper Research Articles

NDVI forest cover changes at high altitudes of the Sulaymaniyah Governorate of Kurdistan Region, northern Iraq, 1984−2021

ABSTRACT This study aims to demonstrate the extent of forest cover at high altitudes in Sulaymaniyah Governorate (Province) in the Kurdistan Region in northern Iraq, from 1984 to 2021 and discuss and explain the changes. The forest data were collected from the satellite digital images of the Landsat Thematic Mapper (TM) from 1984 to 2008 and Landsat Operational Land Imager (OLI) from 2014 to 2021. Data preprocessing preparation included employing a radiometric calibration to convert the images from radiance to Top Atmospheric Reflectance (TOA) in order to remove the cosine effect of different solar zenith angles. Next, a quick atmospheric correction (QUAC) was applied to remove atmospheric noise, particles, molecules and vapor from the data; then the basic equation of Normalized Difference Vegetation Index (NDVI) was applied to extract the forest data for 98 samples taken from across the study area. The study shows that forest cover more than doubled during the survey period: in 1984, 9.1% of the study area was covered by forest, but by 2021 forest cover had increased to 18.4%. We discuss how this increase in forest cover is related to better forest management and preservation by the government and by improving the population’s environmental awareness.

Multitemporal analysis of land use and land cover in the Uraim river microbasin, Paragominas - Pará

The objective of the work was to identify and characterize the changes in land use and land cover that occurred between 1984 and 2018, in the watershed of the Uraim River, Paragominas-PA. The method used was deductive, with a quantitative and qualitative scope, and an applied and explanatory nature. Supervised classification was using ENVI Classic, v.5.1 software. The matrix data (Thematic Mapper and Operational Land Imager images from the LANDSAT 4-5 and 8 satellites) used acquired from the USGS platform. Eight classes defined: water bodies, primary forest, altered forest, degraded forest, agricultural activities, exposed soil, urban area, and clouds and shadows. The data obtained indicated that between 1984 and 1999 there was a loss of areas (n= 127,109.12 ha; 44%) in the primary forest class and an increase in the agricultural areas (n= 47,985.03 ha; 9%) in the watershed region, which contributed to the deforestation aspect; this basin suffers environmental pressures, which can seriously impact the water resources that supply a large part of the municipality. Thus, the development of management plans for land use and occupation and maintenance of Environmental Protection Areas is one of the tools indicated for environmental conservation and recovery of degraded areas.

Assessing the Impacts of Land Use/Land Cover Changes on Water Resources of the Nile River Basin, Ethiopia

Land use/land cover change and climate change have diverse impacts on the water resources of river basins. This study investigated the trends of climate change and land use/land cover change in the Nile River Basin. The climate trends were analyzed using the Mann–Kendall test, Sen’s slope estimator test and an innovative trend analysis method. Land use/land cover (LULC) change was examined using Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) with a resolution of 30 m during 2012–2022. The findings revealed that forestland and shrub land area decreased by 5.18 and 2.39%, respectively. On the other hand, area of grassland, cropland, settlements and water bodies increased by 1.56, 6.18, 0.05 and 0.11%, respectively. A significant increasing trend in precipitation was observed at the Gondar (Z = 1.69) and Motta (Z = 0.93) stations. However, the trend was decreasing at the Adet (Z = −0.32), Dangla (Z = −0.37) and Bahir Dar stations. The trend in temperature increased at all stations. The significant changes in land use/land cover may be caused by human-induced activities in the basin.

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

Studies on how cities are affected by urban heat islands (UHI) are critical nowadays for a better understanding of the connected effects and for providing helpful insights for sustainable city development planning. In this study, Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper+ (ETM+), and Landsat-8 Operational Land Imager (OLI) images were used to assess the dynamics of the spatiotemporal pattern of land use/land cover (LU/LC) and land surface temperature (LST) in the metropolitan city of Mashhad, Iran in the period between 1990 and 2019. The Markov chain model (MCM) was used to predict LU/LC and LST for 2030. In the analyzed LU/LC maps, three LU/LC classes were distinguished, including built-up land (BUL), vegetated land (VL), and bare land (BL) using the maximum likelihood (ML) classification method. The collected data showed different variations in the geographical pattern of Mashhad LST during the research period that impacted the LST in this metropolis. The study evaluated the variations in LU/LC classes and evaluated their impact on the LST. The value of the LST was positively correlated with the occurrence of the built-up land (BUL), and with the bare land areas, while it was negatively correlated with the occurrence of the VL areas. The analysis of changes observed over three decades with 10-year intervals and the prediction of the LU/LC and LST for 2030 constitute an important contribution to the delineation of the dynamics of long LU/LC and LST records. These innovative results may have an important impact on policymaking fostering environmental sustainability, such as the control and management of urban expansion of Mashhad in connection with UHI.

Thin cloud correction method for visible remote sensing images using a spectral transformation scheme

ABSTRACT Thin cloud contamination adversely influences the interpretation of ground surface information in optical remote sensing images, especially for visible spectral bands with short wavelengths. To eliminate the thin cloud effect in visible remote sensing images, this study proposes a thin cloud correction method based on a spectral transformation scheme. Given the strong linear correlation among the three visible bands, a spectral transformation scheme is presented to derive intermediate images and further estimate spectrally varied transmission maps for the three visible bands. An improved strategy that locally estimates atmospheric light was also developed to produce spatially varied atmospheric light maps. Spectrally varied transmission maps and spatially varied atmospheric light maps contribute to the complete removal of thin clouds. Several remotely sensed images featuring various land covers were collected from Landsat 8 Operational Land Imager, Landsat 7 Enhanced Thematic Mapper Plus, and GaoFen-2 to perform simulated and real tests and verify the effectiveness and universality of the presented approach. Four existing thin cloud correction algorithms were used as baseline for comprehensive evaluation, including three single-image-based approaches and a deep-learning-based approach. Results demonstrate that the proposed method outperformed the other four baseline methods, yielding more natural and clearer cloud-free images. The ground surface information in the cloud-contaminated images can be appropriately recovered by the proposed method, with high determination coefficients (>0.8906) and low root-mean-square errors (<2.4711) and spectral angle maps (<0.8870). In summary, the proposed method can completely remove thin clouds, faithfully recover ground surface information, and effectively work for visible remote sensing images from diverse sensors. Furthermore, the factors potentially affecting correction performance and the applicability to open-source datasets were investigated.

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

GIS based landslide susceptibility mapping and assessment using weighted overlay method in Wayanad: A part of Western Ghats, Kerala

The objectives of the study are to generate a landslide susceptibility zonation (LSZ) map of Wayanad for effective and efficient disaster management in the future and to determine the various parameters and usefulness of remote sensing and GIS in landslide susceptibility mapping. The topographical factors like slope, elevation, and aspects influencing landslides are derived from the digital elevation model (DEM). The rainfall data were collected from 2011 to 2019 from the Indian Meteorological Department (IMD) and four sampling sites were identified through a field visit. We used Landsat8 Enhanced Thematic Mapper Plus (ETM+) satellite imagery with a resolution of 30 m to create land use and land cover (LULC) using the unsupervised classification method. It was divided into seven categories based on the annual precipitation of the study area using inverse distance weighting (IDW). The LSZ map was derived using the weighted overlay method by analysing the geographical and climatic factors. These factors helped produce the final LSZ map, which was divided into three zones from high to low. LSZ areas with steep slopes, high elevation, prolonged rainfall, and predominant land use changes. Study reveals that the north-east, south, and south-east regions of the study area are more vulnerable to landslides.

Assessment of Taungya agroforestry system in dryland forests rehabilitation in Sudan

The decline of the forest cover due to deforestation and agricultural expansion in Sudan has reached a critical situation. Taungya agroforestry systems have been used as a strategy to safeguard against this process, allowing farmers to cultivate their subsistence crops at the early stage of tree plantations. The current study research tackled Nabag Forest Reserve (NFR) as a case study to assess and explore &lt;em&gt;Taungya&lt;/em&gt; agroforestry system as a practice used for forest rehabilitation in the study area. Satellite images of Landsat 5 Thematic Mapper, and Landsat 8 Operational Land Imager/Thermal Infrared Sensor of 1991, 2001, 2011, and 2021 were used to generate forest cover maps. Both unsupervised and supervised classification, as well as ground truth points, were applied to classify the vegetation cover in NFR as dense forest (DF), light forest (LF), agricultural field (AF), and bare land (BL). The results showed that two trends of forest cover changes occurred in NFR between 1991-2001 and 2011-2021. It was well explored that through &lt;em&gt;Taungya &lt;/em&gt;agroforestry, there was a considerable increase in forest cover over this period. This could be clearly detected by increasing dense forest and light forest cover by 1041.73 ha (24.95%) and 2.95 ha (0.07%), respectively and decreasing of bare land and agricultural field by 409.79 ha (9.81%) and 634.52 ha (15.15%) during the addressed period. The findings of the study indicated that &lt;em&gt;Taungya&lt;/em&gt; agroforestry system could be a feasible land-use alternative for forest recovery in the dry land of Sudan.

Land Use/Land Cover Change and Environmental Impact Analysis of Ramgarh-Naudiha Region in Uttar Pradesh, India through Geospatial Technology

Rapidly changing LULC scenario with growing population is of great concern in the modern world. The present study was undertaken to evaluate the changes in LULC pattern in Ramgarh-Naudiha region of Sonbhadra district, UP, over 20 years during 1998-2018 using datasets from the Landsat Thematic Mapper (TM) 5 and Landsat 8 (OLI/TIRS) satellites. LULC map for the chosen period has been generated by unsupervised classification with maximum likelihood algorithm. Results indicate that the study area is vulnerable to such LULC changes due to its sensitive geographic location. It is found that the major changes did happen in agriculture, forest, wasteland and water bodies. Agriculture and Forest areas have decreased by ~2 and 6.56% respectively in the study period. The wastelands had increased fast from 5.08% in 1998 to 18.87% in 2018 at the cost of the forest cover and agricultural land respectively. In 1998, water bodies were 7.49%, whereas, it has decreased to 2.04% in 2018. On the contrary, urban fringe area has grown from 0.33% in 1998 to 0.49% in 2018 especially due to population growth. The present study concludes that this LULC analysis will increase awareness and help in taking necessary action in appropriate land use planning and management.

NVIRONMENTAL IMPACTS OF URBAN SPRAWL USING REMOTE SENSING INDICES: A CASE STUDY OF AMMAN CITY – THE CAPITAL OF JORDAN

Urban sprawl is known to have a negative impact on the environment and is considered one of the main challenges in urban development. The current work aims to understand the spatio-temporal characteristics of urban growth and its environmental impact in the city of Amman. The paper's empirical method relies on NDVI, NDBI, and LST calculated using remote sensing and GIS techniques. For this study, Landsat Thematic Mapper Landsat 5-TM images from 1990, 1995, 1999, and 2004 were acquired, as well as Landsat 8-OLI images from 2013, 2017, and 2022. The surface temperature of Amman has increased as the area of paved roads, residential, commercial and industrial land use types have increased, while green spaces and vacant lots have decreased. The NDVI analysis revealed that there was variation in the vegetation index during the study year due to human activities as well as climatic change. Correlations between biophysical variables and LST revealed that NDVI had a significant negative correlation, while NDBI had a positive relationship. Further, a significant positive correlation was observed between road networks and built-up areas which enhance the occurrence of urban sprawl due to urbanization. This study highlighted the significance of considering the environmental consequences of urban sprawl when developing and implementing GAM policies and strategies.

Effects of Climate Change, Land Use and Land Cover Variability on Green and Blue Water in Wami/Ruvu Basin, Tanzania

Water basins are the primary food sources, giving green water and blue water worldwide. Despite the basins’ potential, information on the periodical variations in blue water and green water is sparse, particularly in developing countries. The study specifically evaluated the changes in land use and land cover variability (LULCV), effects of land use and land cover variability on green water and blue water variations, and effects of climatic changes on green water and blue water. The evaluation involved the Enhanced Thematic Mapper and Operational Land Imager satellite images of 1990, 2000, 2010 and 2020. Image processing utilized the Soil and Water Assessment Tool (SWAT) in ArcGIS software. The land use and land cover variabilities indicated that land use supporting social-economic activities increased, while natural land cover decreased. Proportionally, blue water per annum was decreasing due to declining natural vegetation, enhanced by the increased socio-economic activities. Whereas, the increase in green water per annum was due to the increased temperatures, boosted by climate changes. Since the temperature rise is mainly uncontrolled, greenhouse farming should be encouraged for making green water more productive in agriculture and communities should be encouraged to practice environmentally friendly anthropogenic activities for sustainable green water and blue water management.&#x0D; Keywords: Basin, Green water, Blue water, SWAT model, Climate change

Image Processing Approaches as a Diagnostic Parameter to Determine Pollution by Using Satellite Imagery, Northern Iraq

This work highlights the estimation of the Al-Khoser River water case that disposes of its waste directly into the Tigris River within Mosul city. Furthermore, the work studies the effects of environmental and climate change and the impact of pollution resulting from waste thrown into the Al-Khoser River over the years. Al-Khoser River is located in the Northern Mesopotamia of Mosul city. This study aims to detect the polluted water area and the polluted surrounding area. Temporal remote sensing data of different Landsat generations were considered in this work, specifically Enhanced Thematic Mapper Plus of 2000 and Operational Land Imager of 2015. The study aims to measure the amount of pollution in the study area over 15 years using a supervised classification approach and other tools in ERDAS Imagine Software version 2014. Supervised classification is favored for remote sensing data processing because it contains different digital image processing methods. It is noticed by applying to preprocess and post-processing techniques adopted in the polluted section of Al-Khoser River and monitoring the changes in the objects around it. Hence, the river’s water has been classified into clear water and contaminated water, which shows the impact of pollution over the years. The analysis detected a polluted area in the river that enlarged over the years 2000 to 2015 from 4.139 km² to 21.45 km², respectively. The study showed the differences in the size of objects around the river. The study concludes that daily wastes produced by the residential areas through which Al-Khoser and Tigris rivers pass would cause the polluted sections of the river to increase.

Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data

Performing research on the variation in lake water on the Qinghai–Tibet Plateau (QTP) can give the area’s ecological environmental preservation a scientific foundation. In this paper, we first created a high-precision dataset of lake water level variation every 10 days, from July 2002 to December 2022, using multi-source altimetry satellite SGDR data (Envisat RA-2, SARAL, Jason-1/2, and Sentinel-3A/3B SRAL), which integrated the methods of atmospheric path delay correction, waveform re-tracking, outlier detection, position reduction using a height difference model, and inter-satellite deviation adjustment. Then, using Landsat-5 Thematic Mapper, Landsat-7 Enhanced Thematic Mapper, and Landsat 8 Operational Land Imager data, an averaged area series of Lake Qinghai (LQ) from September to November, each year from 2002 to 2019, was produced. The functional connection between the water level and the area was determined by fitting the water level–area series data, and the lake area time series, of LQ. Using the high-precision lake water level series, the fitted lake surface area time series, and the water storage variation equation, the water storage variation time series of LQ was thus calculated every 10 days, from July 2002 to December 2022. When the hydrological gauge data from the Xiashe station and data from the worldwide inland lake water level database are used as references, the standard deviations of the LQ water level time series are 0.0676 m and 0.1201 m, respectively. The results show that the water storage of LQ increases by 11.022 × 109 m3 from July 2002 to December 2022, with a growth rate of 5.3766 × 108 m3/a. The growth rate from January 2005 to January 2015 is 4.4850 × 108 m3/a, and from January 2015 to December 2022, the growth rate is 8.9206 × 108 m3/a. Therefore, the increased rate of water storage in LQ over the last 8 years has been substantially higher than in the previous 10 years.

Lake surface temperature retrieved from Landsat satellite series (1984 to 2021) for the North Slave Region

Abstract. Lake surface temperature (LST) is an important attribute that highlights regional weather and climate variability and trends. The spatial resolution and thermal sensors on Landsat platforms provide the capability of monitoring the temporal and spatial distribution of lake surface temperature on small- to medium-sized lakes. In this study, a retrieval algorithm was applied to the thermal bands of Landsat archives to generate a LST dataset (North Slave LST dataset) for 535 lakes in the North Slave Region (NSR) of the Northwest Territories (NWT), Canada, for the period of 1984 to 2021. North Slave LST was retrieved from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS); however, most of the dataset was created from the thermal bands of Landsat 5 (43 %) due to its longevity (1984–2013). Cloud masks were applied to Landsat images to eliminate cloud cover. In addition, a 100 m inward buffer was applied to lakes to prevent pixel mixing with shorelines. To evaluate the algorithm applied, retrieved LST was compared with in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) LST observations. A good agreement was observed between in situ observations and North Slave LST, with a mean bias of 0.12 ∘C and a root mean squared deviation (RMSD) of 1.7 ∘C. The North Slave LST dataset contains more available data for warmer months (May to September; 57.3 %) compared to colder months (October to April). The average number of images per year for each lake across the NSR ranged from 20 to 45. The North Slave LST dataset, available at https://doi.org/10.5683/SP3/J4GMC2 (Attiah et al., 2022), will provide communities, scientists, and stakeholders with spatial and temporal changing temperature trends on lakes for the past 38 years.

Shoreline change detection along North Sebou–Moulay Bousselham, based on remote sensing analysis

Sustainable management of the shoreline is crucial. Shoreline mapping is very essential to investigate the impact of the dynamic nature of the coastal area over a certain period. Coastal zone at the north Sebou estuary experience some issues as shoreline retreats as well as shoreline advancement in some locations. Shoreline mapping along the northern area of Sebou estuary was implemented by applying remote sensing technology to detect and analyze shoreline changes.Multi-temporal satellite images with different periods during the last 37 years (1984–2021) were used to detect changes as well as analyze shoreline change rates. Landsat images (Landsat 5–TM, Thematic Mapper TM, Landsat7 ETM+, Landsat 8 OLI_TIRS) were corrected from geometric and radiometric perspectives, then classified by assigning six different classes for analyzing the evolution of the coastal zone from 1984 to 2021. To detect changes in both land use and land cover, the Support Vector Machine algorithm supervised classification was executed to classify the images. As a powerful statistical analysis tool, DSAS was used to assess the rate of shoreline change by EPR technique and to anticipate the required coastal protection measures along the coastline.The classification results show that agricultural land, bare land, and sand-dune beaches have decreased by −8.26%, −5.92%, and −4.53% respectively. On the other hand, statistical analysis of DSAS shows that the northern area of Sebou estuary experienced an average accretion rate of (+0.96 m/yr) while erosion rates achieved an average of (−1.04 m/yr). A drastic erosion was noticeable at the right side of the estuary with (−3.1 m/yr). The decision matrix by EPR method has been developed. It illustrates the evolution, risk level, and proposed decision at each sector along the shorelines during the preceding three decades. The final goal of this study is to support decision-makers in implementing preventive management techniques in the most vulnerable locations.

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.

Land-use and land cover change dynamics in urban Ghana: implications for peri-urban livelihoods

ABSTRACTThis paper examined the dynamics of urban land use and land cover change, and their implications for livelihoods in peri-urban Tamale, Ghana. The study employed household data and Landsat Thematic Mapper (TM) for 1986, Enhanced Thematic Mapper Plus (ETM +) for 2004 and Landsat 8 Operationalised Landsat Imager/Thermal Infrared Sensor (OLIS/TIRS) for 2019. The findings show that the urban expansion process witnessed a transition from agricultural livelihood to a more complex and monetised urban economy, which presented mixed impacts on the livelihoods of the peri-urban households. We argued that the horizontal expansion of urban areas into prime agricultural lands could be halted by promoting vertical development in the form of multi-storey buildings rather than the horizontal multiplication of individual housing units. Supporting the peri-urban households to diversify their livelihood portfolios by venturing into beekeeping, poultry farming, and livestock keeping, which do not require large tracts of land is recommended.

The Forest Fire Dynamic Change Influencing Factors and the Impacts on Gross Primary Productivity in China

Forest fire as a common disturbance has an important role in the terrestrial ecosystem carbon cycling. However, the causes and impacts of longtime burned areas on carbon cycling need further exploration. In this study, we exploit Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to develop a quick and efficient method for large-scale forest fire dynamic monitoring in China. Band 2, band 4, band 6, and band 7 of MOD09A1 were selected as the most sensitive bands for calculating the Normalized Difference Fire Index (NDFI) to effectively estimate fire burned area. The Convergent Cross Mapping (CCM) algorithm was used to analyze the causes of the forest fire. A trend analysis was used to explore the impacts of forest fire on Gross Primary Productivity (GPP). The results show that the burned area has an increased tendency from 2009 to 2018. Forest fire is greatly influenced by natural factors compared with human factors in China. But only 30% of the forest fire causes GPP loss. The loss is mainly concentrated in the northeast forest region. The results of this study have important theoretical significance for vegetation restoration of the burned area.

Exploratory Mapping of Blue Ice Regions in Antarctica Using Very High-Resolution Satellite Remote Sensing Data

Mapping spatiotemporal changes in the distribution of blue ice regions (BIRs) in Antarctica requires repeated, precise, and high-resolution baseline maps of the blue ice extent. This study demonstrated the design and application of a newly-developed semi-automatic method to map BIRs in the Antarctic environment using very high-resolution (VHR) WorldView-2 (WV-2) satellite images. We discussed the potential of VHR satellite data for the mapping of BIRs in the Antarctic environment using a customized normalized-difference blue-ice index (NDBI) method devised using yellow, green, and near-infrared spectral bands of WV-2 data. We compared the viability of the newly developed customized NDBI approach against state-of-the-art target detection (TD), spectral processing (SP) and pixel-wise supervised (PSC) feature extraction (FE) approaches. Four semi-automatic FE approaches (three existing plus one newly developed) consisting of 16 standalone FE methods (12 existing + four customized) were evaluated using an extensive quantitative and comparative assessment for mapping BIRs in the vicinity of Schirmacher Oasis, on the continental Antarctic coastline. The results suggested that the customized NDBI approach gave a superior performance and the highest statistical stability when compared with existing FE techniques. The customized NDBI generally rendered the lowest level of misclassification (average RMSE = 654.48 ± 58.26 m2), followed by TD (average RMSE = 987.81 ± 55.05 m2), SP (average RMSE = 1327.09 ± 127.83 m2) and PSC (average RMSE = 2259.43 ± 115.36 m2) for mapping BIRs. Our results indicated that the use of the customized NDBI approach can greatly improve the semi-automatic mapping of BIRs in the Antarctic environment. This study presents the first refined map of distribution of BIRs around the Schirmacher Oasis. The total area of blue ice in the study area was estimated to be 106.875 km2, approximately 61% of the study area. The WV-2 derived BIR map area presented in this study locally refined the existing BIR map derived using Landsat Enhanced Thematic Mapper Plus (ETM+) and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based mosaic of Antarctica (MOA) dataset by ~31% (~33.40 km2). Finally, we discussed the practical challenges and future directions in mapping BIRs across Antarctica.

Interdecadal glacier inventories in the Karakoram since the 1990s

Abstract. Multi-temporal glacier inventories provide key information about the glaciers, their characteristics, and changes and are inevitable for glacier modelling and investigating geodetic mass changes. However, to date, no consistent multi-temporal glacier inventory for the whole of the Karakoram exists, negatively affecting the monitoring of spatio-temporal variations in glaciers' geometric parameters and their related applications. We used a semi-automatic method combining automatic segmentation and manual correction and produced a multi-temporal Karakoram glacier inventory (KGI) compiled from Landsat TM/ETM+/OLI (Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager) images for the 1990s, 2000s, 2010s, and 2020s. Our assessments using independent multiple digitisation of 37 glaciers show that the KGI is sufficiently accurate, with an overall uncertainty of ±3.68 %. We also performed uncertainty evaluation for the contiguous glacier polygons using a buffer of half a pixel, which resulted in an average mapping uncertainty of ±5.21 %. We calculated more than 20 attributes for each glacier, including coordinates, area, supraglacial debris area, date information, and topographic parameters derived from the ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer global digital elevation model). According to KGI-2020s, approximately 10 500 alpine glaciers (&gt;0.01 km2 each) cover an area of 22 510±828 km2 of which 10.18±0.38 % (2290±84 km2) is covered by supraglacial debris. Over the past 3 decades, the glaciers experienced a loss of clean ice and/or snow area but a gain in supraglacial debris. Supraglacial debris cover has increased by 17.63±1.44 % (343.30±27.95 km2), while non-debris-covered glaciers decreased by 1.56±0.24 % (319.85±49.92 km2). The total glacier area was relatively stable and showed only a slight insignificant increase of 23.45±28.85 km2 (0.10±0.13 %). The glacier area has declined by 3.27±0.24 % in the eastern Karakoram, while the glacier area slightly increased in central (0.65±0.10 %) and western Karakoram (1.26±0.11 %). Supraglacial debris has increased over the whole of Karakoram, especially in areas above 4200 m a.s.l. (above sea level), showing an upward shift. The glacier area changes were characterised by strong spatial heterogeneity, influenced by surging and advancing glaciers. However, due to global warming, the glaciers are on average retreating. This is in particular true for small and debris-free glaciers. The multi-temporal KGI data are available at the National Cryosphere Desert Data Center of China: https://doi.org/10.12072/ncdc.glacier.db2386.2022 (F. Xie et al., 2022).