Medium-resolution Data Research Articles

Country-Scale Crop-Specific Phenology from Disaggregated PROBA-V

Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail for smaller plots. This study generated crop-specific phenomaps for mainland France (2016–2020) using PROBA-V data. A spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels. Then, phenometrics were extracted from disaggregated PROBA-V and Sentinel-2 separately and compared to observed phenological stages. Results showed that PROBA-V-based phenomaps closely matched observations at regional level, with moderate accuracy at municipal level. PROBA-V demonstrated a higher detection rate than Sentinel-2, especially in cloudy periods, and successfully generated phenomaps before Sentinel-2B’s launch. The study highlights PROBA-V’s potential for operational crop monitoring, i.e., wheat heading and oilseed rape flowering, with performance comparable to Sentinel-2. PROBA-V outputs complement Sentinel-2: phenometrics cannot be generated at plot level but are efficiently produced at regional or national scales to study phenological gradients more easily than with Sentinel-2 and with similar accuracy. This approach could be extended to MODIS or SPOT-VGT, to generate historical phenological data, providing that a crop map is available.

Removing cloudiness on optical space images by a generative adversarial network model using SAR images

The object of this study is the process of removing cloudiness on optical space images. Solving the cloudiness removal task is an important stage in processing data from the Earth remote probing (ERP) aimed at reconstructing the information hidden by these atmospheric disturbances. The analyzed shortcomings in the fusion of purely optical data led to the conclusion that the best solution to the cloudiness removal problem is a combination of optical and radar data. Compared to conventional methods of image processing, neural networks could provide more efficient and better performance indicators due to the ability to adapt to different conditions and types of images. As a result, a generative adversarial network (GAN) model with cyclic-sequential 7-ResNeXt block architecture was constructed for cloud removal in optical space imagery using synthetic aperture radar (SAR) imagery. The model built generates fewer artifacts when transforming the image compared to other models that process multi-temporal images. The experimental results on the SEN12MS-CR data set demonstrate the ability of the constructed model to remove dense clouds from simultaneous Sentinel-2 space images. This is confirmed by the pixel reconstruction of all multispectral channels with an average RMSE value of 2.4 %. To increase the informativeness of the neural network during model training, a SAR image with a C-band signal is used, which has a longer wavelength and thereby provides medium-resolution data about the geometric structure of the Earth's surface. Applying this model could make it possible to improve the situational awareness at all levels of control over the Armed Forces (AF) of Ukraine through the use of current space observations of the Earth from various ERP systems

Spatial and Temporal Patterns of Maize Phenology in China From 2001 to 2020

AbstractClimate change has significantly altered crop phenology, which has further impacted crop growth and yield. Accurate monitoring of crop phenology is essential for managing agricultural production in response. However, regional monitoring requires high spatial resolution distribution data, as medium resolution data suffers from mixed pixel issues. This study based on a long‐term high spatiotemporal resolution fusion data set of Normalized Difference Vegetation Index and an annually updated maize distribution data set, used the relative threshold method to identify the maize phenology in 22 provinces of China from 2001 to 2020. We further analyzed the trend of maize phenology and assessed its responses to climate change. The results reveal large inter‐annual fluctuations and spatial variability in maize phenology from 2001 to 2020. The length of the growth season (LOS) of spring maize has prolonged by 4.28 days in the northern maize zone and has shortened by 4.90 days in the southern maize zone. Additionally, the LOS of summer maize in the Huang‐Huai‐Hai region has shortened by 2.24 days. We also found a positive correlation between the length of the vegetative growth stage and the mean temperature and a negative correlation between the length of the reproductive growth stage and accumulated precipitation. This study utilized large‐scale, high‐resolution maize phenology data to analyze the trend of maize phenology and its response to climate change. These findings are expected to provide valuable support for assessing maize growth status and developing agricultural adaptive practices.

A bibliometric analysis for remote sensing applications in bush encroachment mapping of grassland and savanna ecosystems

Grasslands and savannas are experiencing transformation and degradation due to bush encroachment (BE). BE has been monitored using restrictive traditional techniques that include field surveys and manual long-term observations. Owing to the limitations of traditional techniques, remote sensing (RS) is an attractive alternative to assess BE because of its generally high precision and return interval, cost-effectiveness, and availability of historical data archives. Furthermore, RS has an added advantage in its ability of acquiring global coherent data in near-real time compared to the snapshot acquisition mode with traditional surveying techniques. Despite its extensive application and vast possibilities, a critical synthesis for RS successes, shortcomings, and best practices in mapping BE in savannas and grasslands is lacking. Thus, broadly, the direction, which this type of investigation has taken over the years is largely unknown. This study sought to connect and measure the progress RS has made in mapping BE in grassland and savanna ecosystems through bibliometric analysis. One hundred and twenty-three peer-reviewed English written documents from the Web of Science and Scopus databases were evaluated. The study revealed 13.05% average annual publication growth, indicating that RS and BE mapping research in grasslands and savannas has been increasing over the survey period. Most published studies came from the USA, while the rest came from South Africa, China, and Australia. The results indicate that BE has been extensively mapped in grasslands and savannas using coarse to medium resolution data. As a result, there is a weak relationship (r² = 0.324) between the dependent variable (aerial images) and the independent variable (percentage of woody cover). This connotes the need to improve BE assessments in grasslands and savannas by integrating recent high-resolution data, machine learning algorithms and artificial intelligence.

Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring

Monitoring displacement at transport infrastructure using Sentinel‑1 Interferometric Synthetic Aperture Radar (InSAR) faces challenges due to the sensor’s medium spatial resolution, which limits the pixel coverage over the infrastructure. Therefore, carefully selecting coherent pixels is crucial to achieve a high density of reliable measurement points and to minimize noisy observations. This study evaluates the effectiveness of various pixel selection methods for displacement monitoring within transport infrastructures. We employ a two-step InSAR time series processing approach. First, high-quality first-order pixels are selected using temporal phase coherence (TPC) to estimate and correct atmospheric contributions. Then, a combination of different pixel selection methods is applied to identify coherent second-order pixels for displacement analysis. These methods include amplitude dispersion index (ADI), TPC, phase linking coherence (PLC), and top eigenvalue percentage (TEP), targeting both point-like scatterer (PS) and distributed scatterer (DS) pixels. Experiments are conducted in two case studies: one in Germany, characterized by dense vegetation, and one in Spain, with sparse vegetation. In Germany, the density of measurement points was approximately 30 points/km², with the longest segment of the infrastructure without any coherent pixels being 2.8 km. In Spain, the density of measurement points exceeded 500 points/km², with the longest section without coherent pixels being 700 meters. The results indicate that despite the challenges posed by medium-resolution data, the sensor is capable of providing adequate measurement points when suitable pixel selection methods are employed. However, careful consideration is necessary to exclude noisy pixels from the analysis. The findings highlight the importance of choosing a proper method tailored to infrastructure characteristics. Specifically, combining TPC and PLC methods offers a complementary set of pixels suitable for displacement measurements, whereas ADI and TEP are less effective in this context. This study demonstrates the potential of Sentinel‑1 InSAR for capturing both regional-scale and localized displacements at transport infrastructure.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

Assessment of the effectiveness of coarse resolution fire products in monitoring long-term changes in fire regime within protected areas in South Africa

Although high-resolution satellite and in situ airborne observations are becoming a common data source for fire monitoring, reconstruction of the historical fire record is based on less suitable data. In many regions, managers and policy-makers rely solely on coarse resolution global fire datasets. This study assessed the suitability of readily available products to accurately depict several fire metrics by comparing them to the manually derived fire scars within three natural reserves in South Africa (2003–2020). Contrary to previous findings, we showed that MCD64A1 and FireCCI51 products detected the majority of the burned area (70–97 %) within protected areas and showed high temporal consistency. Additionally, active and burned area products accurately detected the length and the peak of the fire season. Nonetheless, we found neither medium nor coarse resolution data suitable for evaluating long-term changes in fire size distribution. Coarse resolution burned area products demonstrated a very limited capacity to detect fires smaller than 100 ha, while a lack of sufficient non-cloudy Landsat images led to substantial gaps in fire reconstruction, bringing an unknown level of uncertainties in fire metrics estimates. Similarly, the only available medium-resolution burned area product (GABAM) was unsuitable for fire monitoring since unmapped areas were classified as unburned, making no distinction between regions with an absence of data and an absence of fires. Meanwhile, the performance of FireCCILT11 was ranked the lowest among all the available fire products. Given the lack of resources in many protected areas in South Africa and the pressing need to evaluate current and historical fire management practices and policies, we conclude that, in the absence of a more accurate higher-resolution alternative, MCD64A1 was the most suitable product for capturing year-to-year fluctuations in fire activity and a combination of MODIS and VIIRS active fire products for monitoring changes in fire seasonality.

Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification

ABSTRACTUrban landscape pattern analysis is conducive to understanding the land cover distribution and changes and promoting urban spatial layout rationality and sustainable development. Previous research has often used low and medium spatial resolution data to classify urban land cover at the pixel level and then analyse landscape pattern indices. However, the underlying semantic information provided by pixel classification needs to consider the spatial correlation of surface features, and it is challenging to obtain high-level semantic information on urban functional areas and analyse the relationship between human activities and land use. Leveraging the abundant spatial characteristics in high-resolution remote sensing images, high spatial urban scene classification can infer the feature categories and spatial relationships between features to realize higher-level image interpretation and obtain regional urban scene details. And it contributes to urban planning and decision-making, better meeting the different demands. Therefore, this study aims at a novel methodological framework for landscape pattern analysis of cross-temporal multi-scale urban scene classification. The results imply that: (1) The high-resolution images can make up for the lack of information on urban functional zones in low and medium resolution images; (2) Landscape pattern analysis based on scene classification can analyse urban spatial relationships more rationally than traditional research; and (3) With the continuous advancement of urbanization, the urban functional zoning becomes more apparent, and the dominant industries have changed significantly from industry to commerce and services. This study expands the analytical application range of landscape ecology and remote sensing technology and provides new inspirations for urban planning, environmental monitoring, ecological protection.

Multi-Temporal and Multiscale Satellite Remote Sensing Imagery Analysis for Detecting Pasture Area Changes after Grazing Cessation Due to the Fukushima Daiichi Nuclear Disaster

Despite advancements in remote sensing applications for grassland management, studies following the 2011 Fukushima Daiichi nuclear disaster have often been constrained by limited satellite imagery with insufficient focus on pasture changes. Utilizing different resolutions of optical satellite data is essential for monitoring spatiotemporal changes in grasslands. High resolutions provide detailed spatial information, whereas medium-resolution satellites offer an increased frequency and wider availability over time. This study had two objectives. First, we investigated the temporal changes in a mountainous pasture in Japan from 2007 to 2022 using high-resolution data from QuickBird, WorldView-2, and SPOT-6/7, along with readily available medium-resolution data from Sentinel-2 and Landsat-5/7/8. Second, we assessed the efficacy of different satellite image resolutions in capturing these changes. Grazing ceased in the target area after the 2011 Fukushima Daiichi nuclear accident owing to radiation. We categorized the images as grasses, broadleaf trees, and conifers. The results showed a 36% decline using high-resolution satellite image analysis and 35% using Landsat image analysis in the unused pasture area since grazing suspension in 2011, transitioning primarily to broadleaf trees, and relative stabilization by 2018. Tree encroachment was prominent at the eastern site, which has a lower elevation and steeper slope facing north, east, and south. WorldView-2 consistently outperformed Landsat-8 in accuracy. Landsat-8’s classification variation impedes its ability to capture subtle distinctions, particularly in zones with overlapping or neighboring land covers. However, Landsat effectively detected area reductions, similar to high-resolution satellites. Combining high- and medium-resolution satellite data leverages their respective strengths, compensates for their individual limitations, and provides a holistic perspective for analysis and decision-making.

Shoreline change rate detection applying the DSAS technique on low and medium resolution data: Case study along Ash Shu’aybah-Al Mujayrimah coastal Area of the Eastern Red Sea, Saudi Arabia

The Ash Shu’aybah and Al Mujayrimah coast area in Saudi Arabia is threatened by constantly changing shoreline dynamics. This work explains how to appropriately quantify shoreline change rates using the Landsat and Sentinel-2 datasets, predict the coastline after twenty years (2022–2042) and compare the results from both satellite data. The technique used depends on the integration of remote sensing data and Geographic Information Systems (GIS) with a Digital Shoreline Analysis System tool (DSAS). For calculating shoreline change rates and predicting future shoreline positions in 2042, eight Landsat satellite images over 36 years from 1986 to 2022 were analysed along with four Sentinel-2 satellite images from 2015 to 2022. Based on the LRR models’ data, the shoreline is exposed to erosion with average values of −3.25 and −2.12 m/yr for the Sentinel-2 and the Landsat datasets, respectively. Segment 2 in the central part of the coastline is the area most affected by the dynamics of the shoreline. To assess the accuracy of the obtained results, the root mean square error (RMSE), the mean annual error (MAE) and coefficient of determination (R2) were used. The results indicated that both the Sentinel-2 and the Landsat datasets can reliably calculate the shoreline change rate of the study area based on the similar patterns of their data, although the Sentinel-2 had a somewhat better ability in calculating the change of the shoreline.

Evaluation of surface reflectance retrieval over diverse surface types using SREM algorithm in varied aerosol conditions for coarse to medium resolution data from multiple spaceborne sensors

ABSTRACT Surface reflectance (SR) is one of the most important components for deriving different biophysical parameters from remotely sensed data. For accurate retrieval of SR, it is essential to accurately estimate the degree of atmospheric alteration to the target signals reaching at-sensor. Simplified and robust SR estimation method (SREM) is one such approximate estimation method that could demonstrate the retrieval of SR from top-of-atmosphere (ToA) signals, without using any precalculated LUTs or aerosol information. The present study attempts to assess the accuracy of the SREM algorithm over varied Indian land surfaces using data from Landsat 8, Sentinel 2, AWiFS and LISS III. The results indicate that while SREM performs quite well for Sentinel 2 over most land surface types, the performance is good for Landsat 8 as well except over water and in coastal aerosol band. SREM retrievals also show high correlation for AWiFS and LISS III but the estimated values are much lower than level 2 SR due to smaller ToA reflectance values. Furthermore, while aerosol load does impact the performance adversely, it is least impactful for Sentinel 2 and LISS III datasets and most over Landsat 8, particularly over water. Additionally, while AWiFS and LISS III SR retrievals are underestimated at all spectral bands, retrieval is slightly underestimated from green to SWIR band in case of Sentinel 2 and SWIR band in case of Landsat 8. Overall, the findings demonstrate that while SREM will need to be modified to adapt to LISS III and AWiFS retrievals, it can, nevertheless, be readily used as a simplified method for quick and approximate estimation of SR over the Indian region using data from Landsat 8 and Sentinel 2.

Baseline-Based Soil Salinity Index (BSSI): A Novel Remote Sensing Monitoring Method of Soil Salinization

Soil salinization leads to dehydration of plants, which seriously threatens ecologically sustainable development and food security guarantee. In the complex and diverse coastal wetland environment, the impervious surface and bare soil have similar spectral features with salinized soil, which make it difficult for traditional satellite data and algorithms to accurately and timely monitor the small surface features of salinization. This article presents a baseline-based soil salinity index (BSSI) for soil salinization monitoring using medium-resolution data. In BSSI, we construct a virtual salinization baseline by connecting the near-infrared (NIR) band and the short-wave infrared-2 (SWIR2) band to enhance the spectral feature of salinized soils which border on the impervious surface. In addition, we calculate the distance between the short-wave infrared-1 (SWIR1) band and the virtual salinization baseline as the BSSI, which can effectively improve the stability of salinity inversion for different soils. Through data comparison and model simulations, BSSI has shown advantages over a series of the traditional salinization spectral indices (SSIs). The results show that the saline soil extraction accuracy of BSSI exceeds 85% and the correlation coefficient of the BSSI and the degree of soil salinization exceeds 0.90. Since the related spectral bands, such as NIR, SWIR1, and SWIR2, are available on many existing satellite sensors such as Landsat TM/ETM+, OLI, and sentinel 2, the BSSI concept can be extended to establish long-term records for soil salinization monitoring.

Convolutional Fuzzy Machine Learning based Algorithms for Mapping Chickpea Agricultural Crop Fields using Temporal Satellite Data

In this research, we employed the convolutional classifiers, namely Fuzzy Local Information c-Means (FLICM) and Adaptive Fuzzy Local Information c-Means (ADFLICM), to classify and map chickpea agricultural fields in the Nagaur region of Rajasthan. This classification was performed using Planet Scope and Sentinel-2 data. To streamline the processing of temporal data and reduce spectral information, we experimented with the Normalized Difference Vegetation Index (NDVI) indices. To optimize the performance of the FLICM and ADFLICM algorithms, we utilized the Mean Membership Difference (MMD) technique to determine the most suitable fuzziness value. We also estimated the area of the ground-truthed chickpea fields to compare the performance of both methods. For the FLICM and ADFLICM algorithms with Planet Scope data, the estimated areas were 8914 m2 and 11278 m2, respectively. The ADFLICM algorithm was also applied to classify Sentinel-2 data, demonstrating its effectiveness with medium-resolution data, and the estimated area for Sentinel-2 data was 10633 m2. Finally, we calculated the Root Mean Square Error (RMSE) for the ADFLICM algorithm, which was found to be 0.075194, using Planet Scope satellite data as the reference dataset and Sentinel-2 satellite data as the classified dataset.

Landscape Ecological Evaluation of Cultural Patterns for the Istanbul Urban Landscape

With the widespread population growth in cities, anthropogenic influences inevitably lead to natural disturbances. The metropolitan area of Istanbul, with its rapid urbanization rate, has faced intense pressure regarding the sustainability of urban habitats. In this context, landscapes comprising patches affected by various disturbances and undergoing temporal changes must be analyzed, in order to assess city-related disturbances. In this study, the main objective was to understand how urbanization changed the function of the spatial distribution of the urban mosaic and, more specifically, its relationship with the size, shape, and connection among land-use classes. For this purpose, we took Besiktas, a district of Istanbul, as the study area. We evaluated the landscape pattern of the urban environment in two stages. First, we used medium-resolution satellite imagery to reveal the general interactions in the urbanization process. Landscape- and class-level landscape metrics were selected to quantify the landscape connectivity, and the distances between classes (green areas and artificial surfaces), patterns, and processes, using five satellite images representing a time span of 51 years (1963, 1984, 1997, 2005, and 2014). The general landscape structure was examined by looking at the temporal–spatial processes of artificial surface and green areas obtained from these medium-resolution satellite images. The trends in selected landscape-level metrics were specified and discussed through the use of a moving window analysis. We then used Pleiades high-resolution satellite imagery (2015) to analyze the landscape structure in more detail. This high-resolution base image allows us to recognize the possibility of classifying basic cultural landscape classes. The findings regarding the spatial arrangement of each class in the areas allocated to 14 cultural landscape classes were interpreted by associating them with the landscape functions. Finally, particulate matter (PM10) concentration data were collected and evaluated as an ecological indicator, in order to reveal the relationships between landscape structure and landscape function. In short, we first evaluated the whole landscape structure using medium-resolution data, followed by the classification of cultural landscapes using high-resolution satellite imagery, providing a time-effective—and, therefore, essential—auxiliary method for landscape evaluation. This two-stage evaluation method enables inferences to be made that can shed light on the landscape functions in an urban environment based on the landscape structure.

A first look at the SMACS0723 JWST ERO: spectroscopic redshifts, stellar masses, and star-formation histories

ABSTRACT We present a first-look analysis of the JWST ERO data in the SMACS J0723.3-7327 cluster field. We begin by reporting 10 new spectroscopic redshifts from λobs = 1.8–5.2 μm NIRSpec medium-resolution (R = λ/Δλ = 1000) data. These are determined via multiple high-SNR emission line detections with five objects at 1 < z < 3 displaying multiple rest-frame near-infrared Hydrogen Paschen lines, and five objects at 5 < z < 9 displaying rest-frame optical Oxygen and Hydrogen Balmer lines. For the five higher-redshift galaxies we extract fluxes in six NIRCam bands spanning λobs = 0.8–5 μm and perform spectral energy distribution fitting in combination with existing HST photometry. The 7 < z < 9 objects exhibit a U-shaped pattern across the F277W, F356W, and F444W bands, indicating a Balmer break seen in emission (Balmer jump) and high-equivalent-width [O iii] emission. This indicates an extremely young stellar population with the bulk of the current mass having formed within the past 10 Myr. We report robust stellar masses and mean stellar ages from our spectral fitting with the four z > 6 galaxies exhibiting low-stellar masses from log10 (M*/M⊙) = 7.1–8.2, and correspondingly young mean stellar ages of only a few Myr. This work highlights the critical importance of combining large upcoming NIRCam surveys with NIRSpec follow-up to measure the spectroscopic redshifts necessary to robustly constrain physical parameters.

Technological Solutions Used of the Development Segment of the Flood Monitoring Information System in the Volga River Basin

A satellite segment of the flood monitoring and forecasting information system in the Volga River basin (GIS Volga) has been developed at the Federal State Budget Institution «Scientific-research center for space hydrometeorology «Planeta». The satellite segment contains web services of high and medium spatial resolution data of Russian and foreign spacecraft of the Canopus-B series, Meteor M series, Sentinel-1 series, Sentinel-2 series, EOS, SUOMI NPP and NOAA-20 series, as well as satellite information products (relative soil moisture, daily precipitation, snow cover, etc.). User gets quick access in near real-time mode to all types of satellite, ground-based, predictive information in the GIS Volga information system to make timely decisions to reduce the consequences of floods.

Predicting the location of larval habitats of Anopheles mosquitoes using remote sensing and soil type data

Malaria is a mosquito-borne infectious disease transmitted by the bite of Anopheles mosquitoes. Accurate and timely identification of Anopheles larval habitats and analysis of environmental factors affecting the formation and stability of these locations are very effective in the prevention and spread of malaria. In the absence of sufficient field observations and environmental parameters required in a suitable spatial coverage, remote sensing data can be effective in predicting the habitats of Anopheles mosquitoes. In this article, high-risk depressions that have the potential for Anopheles larval habitats had been identified by fusing a Digital Surface Model (DSM) extracted from very high spatial resolution aerial stereo images with land-use and soil type maps. land-useFinally, the high-risk map of malaria based on the prone larval habitats of Anopheles was created. To evaluate the results, 22 important depressions were identified using field observations in the 1.5 km buffer around Qaleh-Ganj city, Kerman Province, Iran. Of these 22 samples, 14 of them were stable for more than one month at temperatures above 30 °C and the rest were able to store water for three to four weeks. 12 out of 14 samples were consistent with the identified 130 high-risk depressions in the buffer range. Also, 19 of these 22 samples were compatible with the optimal depressions in the buffer range. By comparing the proposed method with previous methods based on data with medium and low spatial resolution or meteorological data, it was concluded that the correct and accurate seasonal and temporary position of Anopheles larval habitats depend on higher spatial resolution of remote sensing data. In addition, this study demonstrated that the use of vegetation and water indices cannot predict the exact location of all habitats of Anopheles. Because many of these habitats were temporary and these indices could not estimate and predict their exact location. The proposed method can be applied to search for suitable larval habitats of Anopheles around local residential areas, where neither medium and low spatial resolution data nor limited field observations can be used.

Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods

Remote sensing technology has penetrated all the natural resource segments as it provides precise information in an image mode. Remote sensing satellites are currently the fastest-growing source of geographic area information. With the continuous change in the earth’s surface and the wide application of remote sensing, change detection is very useful for monitoring environmental and human needs. So, it is necessary to develop automatic change detection techniques to improve the quality and reduce the time required by manual image analysis. This work focuses on the improvement of the classification accuracy of the machine learning techniques by reviewing the training samples and comparing the post-classification comparison with the image differencing in the algebraic technique. Landsat data are medium spatial resolution data; that is why pixel-wise computation has been applied. Two change detection techniques have been studied by applying a decision tree algorithm using a separability matrix and image differencing. The first change detection, e.g., the separability matrix, is a post-classification comparison in which individual images are classified by a decision tree algorithm. The second change detection is, e.g., the image differencing change detection technique in which changed and unchanged pixels are determined by applying the corner method to calculate the threshold on the changing image. The performance of the machine learning algorithm has been validated by 10-fold cross-validation. The experimental results show that the change detection using the post-classification method produced better results when compared to the image differencing of the algebraic change detection technique.

An Introduction to High Data Rate Current-Steering Nyquist DACs: Fasten your seat belts

The current-steering DAC is the most common architecture for medium resolution and ultrahigh data rates, extending well to tens of giga samples per seconds in nanometer MOS process technologies. It is commonly found in all types of communication systems, instrumentation, and consumer applications, among others, due to its capability of efficiently synthesizing the analog output on a low impedance load. This tutorial builds upon previous ones, pointing to a variety of techniques that extend the current-steering DAC’s dynamic performance to an ever-higher input data rate and analog output frequency.

Using topographic attributes to predict the density of vegetation layers in a wet eucalypt forest

ABSTRACT Mapping the structure of forest vegetation with field surveys or high-resolution light detection and ranging (LiDAR) data is costly. We tested whether landscape topography and underlying geology could predict the vegetation density of a 19 km2 area of wet eucalypt forest at the Warra Long-Term Ecological Research Supersite, Tasmania, Australia. Using spatial layers for 12 topographic attributes derived from digital terrain models (DTMs) and a geology layer, we predicted the vegetation density of three strata with a high degree of accuracy (validation root mean square error ranged from 9.0% to 13.7%). The DTMs with 30 m resolution provided greater predictive accuracy than DTMs with higher resolution. The importance of different variables depended on spatial resolution and strata. Among the predictor variables, geology generally had the highest predictive importance, followed by solar radiation. Topographic Position Index, aspect, and System for Automated Geoscientific Analyses (SAGA) Wetness Index had moderate importance. This study demonstrates that geological and topographic attributes can provide useful predictions for the density of vegetation layers in a tall wet sclerophyll primary forest. Given the good performance of the model based on 30 m DTM resolution, the predictive power of the models could be tested on a larger geographical area using lower-density LiDAR point clouds combined with medium-resolution satellite data.