Sensing Imagery Research Articles

Integration of Landsat-8 OLI/TIRS and Sentinel-1A PolSAR for analyzing land surface temperature and its anomalies linked to ENSO in Surakarta, Indonesia

The increase in urban buildings leads to degraded vegetated areas, resulting in higher surface radiation and air temperatures. The rise of land surface temperature (LST) is also influenced by land cover changes and global climate related to the ENSO (El Nino-Southern Oscillation) phenomena. This study is the first to combine active and passive sensors to analyze LST anomalies linked to ENSO-related land cover change on a time series approach. Conducted from August 2018 to 2023 in an urban area dominated by buildings, we used Python programming to extract LST from the Landsat-8 OLI/TIRS passive sensor with a Mono-window algorithm. Meanwhile, the land cover classification was performed by Sentinel-1A active sensor imagery using polarimetric decomposition with unsupervised Wishart. The LST and land cover results were equalized to 30 m spatial resolution for regression and anomaly analysis based on reported ENSO phenomena. The results revealed that land cover type significantly affected LST variation during the study period, proven by the significance value of each land cover type being less than 0.05 and showing a positive correlation. However, the correlation is low, meaning that land cover change is not the dominant factor causing LST change. The low correlation caused by El Nino and La Nina, contributed more to the change in LST during the study period. The integrated method can overcome the weakness of passive sensors in penetrating clouds, contribute to a broader knowledge of the factors causing LST changes, and provide effective early mitigation strategies against the threat of future climate change crises.

Crack detection and dimensional assessment using smartphone sensors and deep learning

This paper addresses the crucial need for effective crack detection and dimensional assessment in civil infrastructure materials to ensure safety and functionality. It proposes a cost-effective crack detection and dimensional assessment solution by applying state-of-the-art deep learning on smartphone sensor imagery and positioning data. The proposed methodology integrates three-dimensional (3D) data from LiDAR sensors with Mask R-convolutional neural network (CNN) and YOLOv8 object detection networks, for automated crack detection in concrete structures, allowing for accurate measurement of crack dimensions, including length, width, and area. The study finds that YOLOv8 produces superior precision and recall results in crack detection compared to Mask R-CNN. Furthermore, the calculated crack-straight-length closely aligns with the ground-truth straight-length, with an average error of 1.5%. These research contributions include developing a multi-modal solution combining LiDAR observations with image masks for precise 3D crack measurements, establishing a dimensional assessment pipeline to convert segmented cracks into measurements, and comparing state-of-the-art CNN-based networks for crack detection in real-life images.

Biomass and bioethanol production of the shrub Ulex europaeus (Fabaceae) estimated with remote sensor imagery in the Andean paramos

Introduction: Gorse (Ulex europaeus, family Fabaceae) is an evergreen shrub native to Europe and invasive in the Andean high-mountain tropical paramos. Objective: To quantify the extent of a biological invasion within a paramo near Los Nevados National Natural Park in Tolima, Colombia, and evaluate bioeconomic solutions to encourage shrub eradication while promoting the local economy and industry. Methods: An object-based supervised classification approach was conducted using UAS (Uncrewed Aircraft Systems)-based RGB imagery and a Planet-derived NDVI (Normalized Difference Vegetation Index) layer, both from 2022, to quantify the area invaded. This value, the height obtained from a UAS-derived nDSM (Normalized Digital Surface Model), and a pair of allometric equations found in the literature were employed to estimate the gorse aboveground biomass (AGB) and aboveground available fuel, also known as fuel load. Then, documented bioethanol production estimations were applied to calculate its potential extraction based on the AGB values. Results: The invaded area was quantified to be 66 465 m2, with an overall accuracy rate of 85.3 %. Furthermore, the fuel load was found to be approximately half of the AGB, which poses a high risk of fire in the ecosystem. The findings also suggest that up to USD 88 933.7 could be generated if all gorse in the study area is exploited to produce bioethanol. Conclusions: This study underscores the urgency of managing the biological invasion of gorse in the Andean paramo and suggests the potential for bioeconomic solutions to mitigate the impact of these invasions.

Soil Adjusted Vegetation Index, Normalize Difference Buildup Index, and Land Surface Temperature between 1987 and 2023 in Abuja Municipal Area Council, Nigeria

Abuja Municipal Area Council (AMAC) is experiencing rapid urban expansion, which is expected to impact land surface temperatures (LST). This paper evaluates the trends in soil adjusted vegetation index (SAVI), normalize difference buildup index (NDBI), and land surface temperature (LST) between 1987 and 2023 in AMAC using Landsat 4 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery, respectively. Results show that in 1987, SAVI ranged from -0.126 to 0.477, NDBI from -0.186 to 0.678, and LST from 27.18 to 46.4 oC. In 2023, SAVI ranged from -0.253 to 0.71, NDBI from -0.308 to 0.619, and LST from 23.89 to 46.57 oC. Analysis showed an increase in vegetation in 2023 compared to 1987. Built-up and bareland areas became more concentrated in the northeast in 2023 compared to 1987, and temperature reductions were observed in areas with increased vegetation, notably in the south and southwest. Correlation analysis indicated a strong negative relationship (-0.772) between SAVI and LST in 1987, weakening in 2023 (-0.389). NDBI and LST remained moderately positively correlated (0.645 in 1987, 0.621 in 2023). Significant differences (P<0.01) were observed between 1987 and 2023 SAVI, NDBI, and LST values. These findings have important implications for environmental monitoring, and urban planning in rapidly urbanizing areas such as AMAC

Association mining of coastline change and land use patterns to enhance conservation

Coastal zones, as interfaces between marine and terrestrial ecosystems, possess great ecological and economic value but are environmentally fragile. This research provides a new perspective for studying development and change patterns in coastal zones, offering insights for land planning and contributing to the ecological preservation and sustainable development of coastal areas. We utilized Landsat TM and OLI sensor imagery (from the years 1990, 2000, 2010, and 2020) to extract coastline and land use information from the coastal zone of Shandong Province. Through the establishment of association mining models between coastline changes and land use patterns, the relationships between spatial layout changes and development processes in different geographical locations are revealed. In the initial stage, weak expansion and moderate changes in the coastline corresponded to units with lower land development intensity sequences. In the intermediate stage, with increasing coastline expansion and intense changes, the sequence of land development intensity was extremely strong > strong > moderate > weak from the sea to the land. This type of association was mostly distributed in the Laizhou Bay salt field and coastal port areas and was mostly focused on aquaculture and port trade, causing “strong” and “extremely strong” units of regional land development intensity. In the later stage, as coastline expansion slowed, the use of ports and docks caused the development intensity of the surrounding land to fluctuate. Farming, fishing, salt field and port construction in the coastal zone of Shandong Province were the major forms of land expansion and change. The association rules between coastline expansion or erosion and coastal development intensity revealed the impact of coastline changes on the spatial pattern from sea to land, which is important for planning layouts, optimizing industrial structures, and maximizing socioeconomic and environmental benefits in coastal areas.

Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI

Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance for a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which are often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is common practice. To alleviate the COT data scarcity problem, in this work, we propose a novel synthetic dataset for COT estimation, which we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the newly collected real dataset, code and models have been made publicly available.

Lyzenga Algorithm for Shallow Water Mapping Using Multispectral Sentinel-2 Imageries in Gili Noko Waters

Bathymetric survey is an essential approach for gathering marine geospatial information. The use of an echosounder can provide highly accurate bathymetric data, yet it is expensive and time-consuming. To achieve an accurate chart of shallow water areas is challenging as they are difficult to access by large survey vessels due to their draft limitation. Bathymetric survey can also be carried out by using an Unmanned Surface Vehicles; however, it is restricted to small and calm areas. Thus, a Satellite Derived Bathymetry (SDB) come as a solution to take the place of bathymetric survey using an echosounder, especially in a shallow water with many natural or man-made obstacles. This study utilises Sentinel-2 image data, an optical satellite imagery to determine depth values of shallow water area using SDB approach. Several algorithms have been developed to collect depth values using the SDB. One of the algorithms is Lyzenga algorithm which aims to simplify the process of extracting water depths by comparing water reflectance factors using three bands of panchromatic sensor imagery. This study applied the Lyzenga algorithm to determine water depths using multiple linear regression and it is validated using an in-situ data. The RMSE and MAE values is 1.711m and 1.254m, respectively whilst the correlation coefficient is 0.946.

Low-Tech and Low-Cost System for High-Resolution Underwater RTK Photogrammetry in Coastal Shallow Waters

Monitoring coastal seabed in very shallow waters (0–5 m) is a challenging methodological issue, even though such data is of major importance to many scientific and technical communities. Over the years, Structure-from-Motion (SfM) photogrammetry has emerged as a flexible and inexpensive method able to provide both a 3D model and high-resolution imagery of the seabed (~cm level). In this study, we propose a low-cost (about USD 1500), adaptable, lightweight and easily dismantled system called POSEIDON (for Platform Operating in Shallow-water Environment for Imaging and 3D reconstructiON). This prototype combines a floating support (typically a bodyboard), two imagery sensors (here, GoPro® cameras) and an accurate positioning system using Real Time Kinematic GNSS. Validation of this method was deployed in a macrotidal zone, comparing on the foreshore the point cloud provided by POSEIDON “SfM bathymetry” and by classical terrestrial SfM survey. Mean deviation was 5.2 cm and standard deviation was 4.6 cm. Such high-resolution SfM bathymetric surveys have a great potential for a wide range of applications: micro-bathymetry, hydrodynamics (bottom roughness), benthic habitats, ecological inventories, archaeology, etc.

Estimating of heavy metal concentration in agricultural soils from hyperspectral satellite sensor imagery: Considering the sources and migration pathways of pollutants

The evolution of hyperspectral remote sensing and artificial intelligence technologies has led to a surge in their application for predicting Soil Heavy Metal Concentrations (SHMC). Nevertheless, the preponderance of existing research within this sphere centers around data procured from ground-based and airborne hyperspectral sources. Studies that employ satellite-based methodologies typically rely on medium spatial resolution hyperspectral or multispectral satellite data. The application of high spatial and spectral resolution satellite data, such as that obtained from GaoFen-5 (GF-5), remains conspicuously underexplored. Furthermore, the impact of geographical environmental factors (GEFs) on the accuracy of predictions has been infrequently considered. In the context of this backdrop, the present study introduces stacking models designed to estimate SHMC. This approach integrates reflectance spectral features (SFs) derived from GF-5 hyperspectral imagery and GEFs, including topography and pollution sources. The results demonstrate a notable improvement in the predictive accuracy of SHMC using our Stacking model, as compared to single models. The incorporation of GEFs into the method results in a varying degree of reduction in the Root Mean Square Error (RMSE), along with an enhancement in the R2 on the training set. The predictive performance improvement is most prominent for Cd and As, with the RMSE decreasing by 52% and 48%, respectively. Notably, apart from Pb, there is an improvement in performance for all elements within the test set. This study confirms the effectiveness of integrating GEFs into SHMC prediction models to enhance accuracy. Applying this technique to predict soil pollution at a regional scale and to demarcate heavily polluted areas can yield satisfactory results. In the future, we plan to apply this technique to other research areas or datasets to expand its universality. Furthermore, we aim to delve more deeply into the potential of GEFs to enhance the predictive capacity of SHMC.

Methodology for performing bathymetric measurements of shallow waterbodies using an UAV, and their processing based on the SVR algorithm

State-of-art methods of bathymetric measurements for shallow waterbodies use Global Navigation Satellite System (GNSS) receiver, bathymetric Light Detection and Ranging (LiDAR) sensor or satellite imagery. Currently, photogrammetric methods with the application of Unmanned Aerial Vehicles (UAV) are gathering great importance. This publication aims to present step-by-step methodology for carrying out the bathymetric measurements of shallow waterbodies with the use of UAV-Structure from Motion (SfM) and Support Vector Regression (SVR) algorithms. Proposed model is explained and verified on the basis of measurement campaign on Raduńskie Górne Lake located in Poland. The campaign included GNSS Real Time Kinematic (RTK) bathymetric measurements for robust training dataset and photogrammetric flight pass for SfM point cloud. The presented methodology will allow to perform bathymetric measurements with the accuracy provided for the International Hydrographic Organization (IHO) Special Order (horizontal position error ≤ 2 m (p = 0.95), vertical position error ≤ 0.25 m (p = 0.95)).

Advances in Aerial Image Exploitation

The availability of low cost sensors, high bandwidth communication links and the growing popularity of Unmanned Aerial Reconnaissance Platforms like UAVs have resulted in dramatic new opportunities to conduct remote battlefield surveillance and reconnaissance missions from relatively safe Ground Control Stations. The sensors used in UAV explore the visible, infrared and microwave regions of the spectrum and the images are produced optically, electronically or by film. Aerial Image Exploitation is an innovative image utilization program that uses multi sensor, multi resolution and multi spectral imagery obtained from Aerial Reconnaissance Platform for the purpose of Extraction, Exploitation, Dissemination and Interpretation of Imagery Intelligence. Research in aerial image exploitation is focused to provide a total solution of the problem by combining both hardware and software at the system and subsystem level. Major operational goal of image exploitation technology is to extract the vital intelligence from a large storehouse of available imagery by filtering those which are most likely to produce valuable findings, to combine classical image/signal processing techniques with technologies available in other fields and to use specialized hardware wherever required. The first part of the paper describes the Ground Image Exploitation System and Imagery Intelligence Exploitation System being developed in ADE. The second part of the paper describes some of the basic and advanced research work being carried out in ADE, which is the backbone that lead to the development of these systems. Some of the areas discussed in this paper are adaptive preprocessing of imagery, image mosaicing, terrain classification and high-resolution reconstruction of target region.

Downscaling satellite night-time lights imagery to support within-city applications using a spatially non-stationary model

For mapping and monitoring socioeconomic activities in cities, night-time lights (NTL) satellite sensor images are used widely, measuring the light intensity during the night. However, the main challenge to mapping human activities in cities using such NTL satellite sensor images is their coarse spatial resolution. To address this drawback, spatial downscaling of satellite nocturnal images is a plausible solution. However, common approaches for spatial downscaling employ spatially stationary models that may not be optimal where the data are spatially heterogeneous. In this research, a geostatistical model termed Random Forest area-to-point regression Kriging (RFATPK) was employed to disaggregate coarse spatial scale VIIRS NTL images (450 m) to a fine spatial scale (100 m). The RF predicts at a coarse resolution from fine spatial resolution variables, such as a Population raster. ATPK then downscales the coarse residuals from the RF prediction. In numerical experiments, RFATPK was compared with three benchmark techniques, including the simple Allocation of pixel values from the coarse resolution NTL data, Machine Learning with Splines and Geographically Weighted Regression. The downscaled results were validated using fine resolution LuoJia 1-01 satellite sensor imagery. RFATPK produced more accurate disaggregated images than the three benchmark approaches, with mean root mean square errors (RMSE) for the year 2018 of 13.89 and 6.74 nWcm−2 sr−1, for Mumbai and New Delhi, respectively. Also, the property of perfect coherence, measured by the Correlation Coefficient, was preserved consistently when applying RFATPK and was almost 1 for all years. The applicability of the disaggregated NTL data to monitor socioeconomic activities at the within-city scale against the reference NTL was illustrated by utilizing them as a proxy for the Gross National Income (GNI) per capita and the Night Light Development Index. The GNI estimation from the downscaled NTL outperformed the coarse resolution NTL when examining their coefficients of determination, with R2 of 0.67 and 0.47 for the GNI estimation using the fine and coarse resolution NTL data, respectively. For the Night Light Development Index (NLDI), the results of the index were compared by measuring their correlation with the Human Development Index (HDI). The NLDI from the downscaled NTL outperformed the coarse resolution NTL when measuring the correlation with the HDI, with Pearson’s correlation coefficients of −0.48 and −0.35 for the NLDI using the fine and coarse resolution NTL data, respectively, for New Delhi. The outcomes indicate that RFATPK provides more accurate predictions than the three benchmark techniques and the downscaled NTL data are more suitable for fine scale socioeconomic applications, as demonstrated by the NLDI and GNI. This research, thus, shows that the RFATPK solution for NTL disaggregation can facilitate data enhancement for fine-scale sub-national applications in social sciences and can be generalized worldwide by including other cities as well as other applications.

A Gated Content-Oriented Residual Dense Network for Hyperspectral Image Super-Resolution

Limited by the existing imagery sensors, a hyperspectral image (HSI) is characterized by its high spectral resolution but low spatial resolution. HSI super-resolution (SR) aims to enhance the spatial resolution of the HSIs without modifying the equipment and has become a hot issue for HSI processing. In this paper, inspired by two important observations, a gated content-oriented residual dense network (GCoRDN) is designed for the HSI SR. To be specific, based on the observation that the structure and texture exhibit different sensitivities to the spatial degradation, a content-oriented network with two branches is designed. Meanwhile, a weight-sharing strategy is merged in the network to preserve the consistency in the structure and the texture. In addition, based on the observation of the super-resolved results, a gating mechanism is applied as a form of post-processing to further enhance the SR performance. Experimental results and data analysis on both ground-based HSIs and airborne HSIs have demonstrated the effectiveness of the proposed method.

Spatiotemporal dynamics of land use/land cover changes and its drivers in Bilate watershed, central rift valley, Ethiopia

Land use/land cover (LULC) changes influence the ecological function, consequence on ecosystem services, which are tightly linked to human wellbeing. However, quantification of the LULC changes and identifying the underlying factors remain patchy particularly in developing nations, despite this information is crucial to propose a feasible restoration action. Therefore, this study investigates the land use/land cover changes and its drivers in central Rift Valley, Ethiopia. GIS and Remote sensors i.e. Landsat 5 (TM), and Landsat 8 (OLI/TIRs) imagery sensors acquired from USGS, and field observation were used. Using the supervised classification method and the support of ArcGIS 10.5 and ERDAS IMAGINE 2014, all images were classified into various land cover types. Focus group discussions, key informant interviews, and structured questionnaire surveys were used to investigate the drivers of LULC change. NDVI was used to detect the vegetation cover change. Woodland, grassland, and barren lands were the major LULC types identified in this study. After 28 years, the woodland cover increased from 20.6% to 40.2% whereas the barren land decreased from 43.4% to 22.6%. Grassland showed very slight increment, i.e. from 35.9% to 36.9%. This implies that area enclosure plays a significant role in the restoration of degraded lands. The highest NDVI values (0.6) were determined in the year 2022 at the end of the classification. Focus group discussants and key informants confirmed that human-induced factors were the major drivers of LULC changes in the study area. Our findings indicated that human interventions are the key determinants of land use/land cover dynamics, and as a result, enforcement of the law and public education campaigns to change human behavior in support of the area enclosure approach are essential to restoring degraded land for the benefit and wellbeing of humans and nature while also advancing the achievement of the global goals.

The Effect of Temporal Resolution of Climatic Factors on Agriculture Degradation in Southern Baghdad by Applying Remote Sensing Data

The climate changes had been recognized as one of the major factors responsible for land degradation, which has a significant impact on diverse aspects. The present study aims to estimate how the climate change can influence land degradation in the south areas of Baghdad province (Al-Rasheed, Al-Mahmudiyah, Al-Yusufiyah, Al-Madaen, and Al-Latifiyah). The Satellite Landsat-8 OLI and satellite Landsat-5 TM sensor imagery were used to extent land degradation for the period (2010-2019). ArcGIS V.10.4 was applied to manage and analysis the satellite image dataset, including the use of climate factors data from the European Center for Climate Forecasts (ECMWF) by reanalyzes and extraction datasets. To achieve work objectives, many ground data were collected, including the temperature, rain precipitation, evaporation, and relative humidity from 30 meteorological monitoring stations. These data help us to utilize the interpolation methods for the extraction process of contour lines maps, to be scientific indicators of the relationship between climatic factors and satellite images classifications, involving the spectral indicators of the vegetation cover and water bodies. The results showed the agriculture degradation through the decreasing of vegetation cover rate from 56.57% in (2010) to 43.43% in )2019 (. This deterioration is thought to be related to climate changes with other factors such as water shortage that was 0.52 and 0.44, respectively, the greatest temperature reading was (24.57), the greatest precipitation was (0.21), the greatest relative humidity was (60.73), and vapor rate (-0.2) for the studied period.

Method for Frequent High Resolution of Optical Sensor Image Acquisition using Satellite-Based SAR Image for Disaster Mitigation

Method for frequent high resolution of optical sensor imagery data acquisition from satellite-based SAR (Synthetic Aperture Radar) image for disaster mitigation is proposed. The proposed method is based on Generative Adversarial Network: GAN-based super resolution and conversion method from a SAR imagery data to the corresponding optical sensor imagery data in order to increase observation frequency. Through experiments, it is found that it is possible to convert SAR imagery data to the corresponding optical sensor imagery data and also found that the spatial resolution of SAR imagery data is improved remarkably. Thus, initial stage of disaster (small scale of disaster) can be detected with resolution enhanced optical sensor imagery data derived from the corresponding SAR imagery data which results in prevention of secondary occurrence of relatively large scale of disaster. It is also found that 2.5 m of spatial resolution of optical sensor imagery data can be acquired every 2.5 days in the case that only Sentinel-1/SAR and Sentinel-2/MSI (Multi Spectral Imager) are used, for instance.

Tracking Liquefied Natural Gas Fuelled Ship’s Emissions via Formaldehyde Deposition in Marine Boundary Layer

One of the reasons that anthropogenic greenhouse gas emissions estimation is imprecise is the uncertainty of aerosol impacts on cloud properties. Maritime transportation is slowly changing fuel preferences. With the policy framework changing regulations, the shipping business is going in a direction that emits less sulfur dioxide and black carbon, which are the compounds that cause linear cloud formations known as ship tracks. Aside from their effects on the total radiative forcing of a transportation mean, this phenomenon enables the detection of ships via satellite imagery sensors. The rapidly increasing trend of shifting propulsion of maritime transportation from conventional heavy fuel oil and distillate marine fuels to liquefied natural gas causes enormous hikes in methane emissions. Therefore, oxidation of the volatile organic compound in the marine boundary layer by the hydroxyl radical in the troposphere makes significant deposition of formaldehyde which causes human effects, ecosystem damage, and climate impact. The primary triggering substance among the compounds in the ship plume is methane. This paper discusses methods to assess near real time tracking of anomalies and the deposition of the short lived substance in different seasons in one of the main occurring areas, shipping corridors. The study also employs anomaly map analysis for June and December 2010 and 2020. Several global tracking methods are available with satellites, monitoring experiments, and other satellite tracking tools. Apart from a few areas the results are not indicative since the formaldehyde formations caused by LNG fueled ships are not widespread enough alongside with overall LNG fueled fleet. On the other hand, the analysis and method are promising for the follow-up of the emissions in the future.

Aerial multi-spectral AI-based detection system for unexploded ordnance

Unexploded ordnance (UXO) poses a threat to soldiers operating in mission areas, but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard. Recent technological advancements in artificial intelligence (AI) and small unmanned aerial systems (sUAS) present an opportunity to explore a novel concept for UXO detection. The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral (MS) sensor on sUAS. This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single (visible) spectrum (SS) or MS digital electro-optical (EO) sensor. Specifically, it describes the design of the Deep Learning Convolutional Neural Network for UXO detection, the development of an AI-based algorithm for reliable UXO detection, and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.

Seasonal land use/land cover change and the drivers in Kafta Sheraro national park, Tigray, Ethiopia

Kafta Sheraro National Park (KSNP) has experienced rapid and consecutive destruction of dry woodland vegetation due to the influence of anthropogenic activities in the past three decades. However, to date, the change in woodland cover and its driving factors have not been addressed. This study aims to assess the spatial and temporal trends of land use/land cover change, seasonal vegetation cover change via the normalized difference vegetation index (NDVI), and human-induced drivers of change that occurred in the KSNP between 1988 and 2018 by using satellite imagery sensors (TM, ETM+, OLI), field observations, and local community interview data. The 2018 image results showed kappa coefficients of the dry season and wet season of 0.90 and 0.845, respectively. There was a continuous decline in woodland (29.38%) and riparian vegetation (47.11%) and an increasing trend in shrub bush land (35.28%), grassland (43.47%), bare land (27.52%), and cultivated land (118.36 km2) over the thirty-year period. Moreover, the results showed that bare land expanded from wet to drier months, while cultivated land and grazing land increased from dry to wet months. Based on the NDVI results, high to moderate vegetation was decreased by 21.47%, while sparse and non-vegetation expanded by 19.8% and 1.7%, respectively. Settlement and agricultural expansion, human-induced fire, firewood collection, gold mining, and charcoal production were the major proximate drivers that negatively affected park resources. Around KSNP, the local communities’ livelihood depends on farming (crop and livestock production). This expansion of farming is the main driver of woodland depletion, which leads to increased resource competition and a challenge for the survival of wildlife. Therefore, urgent sustainable conservation of park biodiversity by encouraging community participation in conservation practices and preparing awareness creation programs should be mandatory.

The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review

In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented.