Acquisition Of Imagery Research Articles

Overcoming the UAS limitations in the coastal environment for accurate habitat mapping

Unmanned Aerial Systems (UAS) have been recently used as a close-range remote sensing tool, providing high spatial and temporal resolutions compared to other remote sensing methods. Their abilities have constantly increased their use in many environmental studies with very satisfactory results. However, the acquisition of reliable information for accurate marine habitat mapping is still challenging due to the plethora of parameters (mostly environmental conditions) that affect the accurate depiction and delineation of habitats in aerial imagery.In this study we are proposing an efficient methodology for marine habitat mapping that includes three main steps: i) data acquisition and processing of UAS imagery, ii) Object-Based Image Analysis (OBIA) and classification of marine habitats, iii) image quality and accuracy assessment of high-resolution thematic maps. This methodology overcomes the UAS limitations in the coastal environment by identifying the optimal acquisition times through the UASea toolbox and fully exploits the potentials of UAS in environmental applications. Moreover, the OBIA workflow and classification combined with the image quality and accuracy assessment demonstrates the importance of such methods in producing very high-resolution and reliable maps of marine habitats.The proposed methodology was implemented in four coastal areas, in Greece, with different characteristics and prevalent environmental conditions, using different UAS with RGB sensors. In particular, the efficiency of the methodology was tested by acquiring data in several conditions in each site and by comparing the results as to the image quality and the classification accuracies. The results showed that the imagery acquired in the proposed acquisition times was of higher quality along with high classification accuracies. This methodology offers an efficient solution for the acquisition of high-resolution and reliable marine information and accurate habitat delineation that can be adaptable in a range of ecological and environmental studies.

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

Autonomous Detection of Spodoptera frugiperda by Feeding Symptoms Directly from UAV RGB Imagery

The use of digital technologies to detect, position, and quantify pests quickly and accurately is very important in precision agriculture. Imagery acquisition using air-borne drones in combination with the deep learning technique is a new and viable solution to replace human labor such as visual interpretation, which consumes a lot of time and effort. In this study, we developed a method for automatic detecting an important maize pest—Spodoptera frugiperda—by its gnawing holes on maize leaves based on convolution neural network. We validated the split-attention mechanism in the classical network structure ResNet50, which improves the accuracy and robustness, and verified the feasibility of two kinds of gnawing holes as the identification features of Spodoptera frugiperda invasion and the degree. In order to verify the robustness of this detection method against plant morphological changes, images at the jointing stage and heading stage were used for training and testing, respectively. The performance of the models trained with the jointing stage images has been achieved the validation accuracy of ResNeSt50, ResNet50, EfficientNet, and RegNet at 98.77%, 97.59%, 97.89%, and 98.07%, with a heading stage test accuracy of 89.39%, 81.88%, 86.21%, and 84.21%.

Palmer Amaranth (Amaranthus palmeri S. Watson) and Soybean (Glycine max L.) Classification in Greenhouse Using Hyperspectral Imaging and Chemometrics Methods

HighlightsHyperspectral image processing was used to classify Palmer amaranth and soybean species.Chemometrics methods (PCA, PLS-DA, and SIMCA) were used to extract features and establish classification models.Abstract. Herbicide-resistant weed species are one of the largest threats to modern agriculture, as ineffective weed control results in significant yield losses or increased costs through alternatives such as mechanical methods. Palmer amaranth (Amaranthus palmeri S. Watson) has been one of the most troublesome weeds. Its identification through the adoption of site-specific weed management systems will help farmers select more appropriate control options to reduce costs and improve efficacy, resulting in increased farm revenue. In this study, a pixel-wised method was evaluated for the classification of Palmer amaranth and soybean (Glycine max L.). A pushbroom hyperspectral imagery acquisition system was used to collect imagery from 224 spectral bands ranging from 400 to 1000 nm. Greenhouse experiments were conducted in three different runs. Greenhouse-grown plants were evaluated to generate predictive models from paired samples generated with 56 replications in each run. Data collection occurred weekly when Palmer amaranth plants were between approximately 2.5 and 12.7 cm tall. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were tested to classify Palmer amaranth and soybean. Half of the dataset (Palmer amaranth = 42, soybean = 42) was used to train the models, and the other half (Palmer amaranth = 42, soybean = 42) was used to test model performance. Preliminary results showed that the PLS-DA model with PLS factors as input had a cumulative variation (R2Y(cum)) of 60% and predictive ability (Q2Y(cum)) of 60%. The SIMCA model showed a cumulative variation of 85% and a predictive ability of 82%. Overall, this study illustrated the capability of hyperspectral imagery to classify Palmer amaranth and soybean, which will increase the efficiency of weed control in modern agriculture. Keywords: Chemometrics methods, Hyperspectral imaging, Palmer amaranth classification.

Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico

Integrating information about the spatial distribution of carbon stocks and species diversity in tropical forests over large areas is fundamental for climate change mitigation and biodiversity conservation. In this study, spatial models showing the distribution of carbon stocks and the number of species were produced in order to identify areas that maximize carbon storage and biodiversity in the tropical forests of the Yucatan Peninsula, Mexico. We mapped carbon density and species richness of trees using L-band radar backscatter data as well as radar texture metrics, climatic and field data with the random forest regression algorithm. We reduced sources of errors in plot data of the national forest inventory by using correction factors to account for carbon stocks of small trees (<7.5 cm DBH) and for the temporal difference between field data collection and imagery acquisition. We created bivariate maps to assess the spatial relationship between carbon stocks and diversity. Model validation of the regional maps obtained herein using an independent data set of plots resulted in a coefficient of determination (R2) of 0.28 and 0.31 and a relative mean square error of 38.5% and 33.0% for aboveground biomass and species richness, respectively, at pixel level. Estimates of carbon density were influenced mostly by radar backscatter and climatic data, while those of species richness were influenced mostly by radar texture and climatic variables. Correlation between carbon density and species richness was positive in 79.3% of the peninsula, while bivariate maps showed that 39.6% of the area in the peninsula had high carbon stocks and species richness. Our results highlight the importance of combining carbon and diversity maps to identify areas that are critical—both for maintaining carbon stocks and for conserving biodiversity.

UASea: A Data Acquisition Toolbox for Improving Marine Habitat Mapping

Unmanned aerial systems (UAS) are widely used in the acquisition of high-resolution information in the marine environment. Although the potential applications of UAS in marine habitat mapping are constantly increasing, many limitations need to be overcome—most of which are related to the prevalent environmental conditions—to reach efficient UAS surveys. The knowledge of the UAS limitations in marine data acquisition and the examination of the optimal flight conditions led to the development of the UASea toolbox. This study presents the UASea, a data acquisition toolbox that is developed for efficient UAS surveys in the marine environment. The UASea uses weather forecast data (i.e., wind speed, cloud cover, precipitation probability, etc.) and adaptive thresholds in a ruleset that calculates the optimal flight times in a day for the acquisition of reliable marine imagery using UAS in a given day. The toolbox provides hourly positive and negative suggestions, based on optimal or non-optimal survey conditions in a day, calculated according to the ruleset calculations. We acquired UAS images in optimal and non-optimal conditions and estimated their quality using an image quality equation. The image quality estimates are based on the criteria of sunglint presence, sea surface texture, water turbidity, and image naturalness. The overall image quality estimates were highly correlated with the suggestions of the toolbox, with a correlation coefficient of −0.84. The validation showed that 40% of the toolbox suggestions were a positive match to the images with higher quality. Therefore, we propose the optimal flight times to acquire reliable and accurate UAS imagery in the coastal environment through the UASea. The UASea contributes to proper flight planning and efficient UAS surveys by providing valuable information for mapping, monitoring, and management of the marine environment, which can be used globally in research and marine applications.

Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors

Over the last several years, semantic image segmentation based on deep neural networks has been greatly advanced. On the other hand, single-instance segmentation still remains a challenging problem. In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional incorporates a data fit term, an explicit shape model, and accounts for object overlap. Efficient solution neighborhood operators are proposed, enabling optimization through metaheuristics such as simulated annealing. We instantiate the proposed framework in the context of segmenting individual fallen stems from high-resolution aerial multispectral imagery, providing problem-specific energy potentials. We validated our approach on 3 real-world scenes of varying complexity, using 730 manually labeled polygon outlines as ground truth. The test plots were situated in regions of the Bavarian Forest National Park, Germany, which sustained a heavy bark beetle infestation. Evaluations were performed on both the polygon and line segment level, showing that the multi-contour segmentation can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7 percentage points (pp) in recall and 6 in precision compared to an iterative sample consensus line segment detection baseline was achieved. Despite the simplicity of the applied shape parametrization, an explicit shape model incorporated into the energy function improved the results by up to 4 pp of recall. Finally, we show the importance of using a high-quality semantic segmentation method (e.g. U-net) as the basis for individual stem detection, as the quality of the results degraded dramatically in our baseline experiment utilizing a simpler method. Our method is a step towards increased accessibility of automatic fallen tree mapping in forests, due to higher cost efficiency of aerial imagery acquisition compared to laser scanning. The precise fallen tree maps could be further used as a basis for plant and animal habitat modeling, studies on carbon sequestration as well as soil quality in forest ecosystems.

AUTOMATED BUILDING SEGMENTATION AND DAMAGE ASSESSMENT FROM SATELLITE IMAGES FOR DISASTER RELIEF

Abstract. After a natural disaster or humanitarian crisis, rescue forces and relief organisations are dependent on fast, area-wide and accurate information on the damage caused to infrastructure and the situation on the ground. This study focuses on the assessment of building damage levels on optical satellite imagery with a two-step ensemble model performing building segmentation and damage classification trained on a public dataset. We provide an extensive generalization study on pre- and post-disaster data from the passage of the cyclone Idai over Beira, Mozambique, in 2019 and the explosion in Beirut, Lebanon, in 2020. Critical challenges are addressed, including the detection of clustered buildings with uncommon visual appearances, the classification of damage levels by both humans and deep learning models, and the impact of varying imagery acquisition conditions. We show promising building damage assessment results and highlight the strong performance impact of data pre-processing on the generalization capability of deep convolutional models.

Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt.

In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources.

3D Integrated Survey for the Study of Archaeological Sites: the Case Study of Euryalus Castle in Siracusa

The present article deals with the results of a recent study conducted by the Laboratory of Representation at the University of Catania regarding the 3D modelling process of the archaeological site of Euryalus Castle in Siracusa. The purpose of the research was to discuss the survey methods implemented in the archaeological field, considering each methodology as a singular or as a multi-sensor approach. Digital integrated survey (range-based and image-based methods) and, in particular, the UAS system (Unmanned Aerial System) reduce the problems connected with the survey of complex or large objects and increase capability exposure thanks to the high quality of 3D photorealistic technology. The traditional survey systems applied to the area around Siracusa and to the archaeological site have been integrated, in the present case study, with UAV (Unmanned Aerial Vehicle) photogrammetric survey for the acquisition of nadir and oblique imagery. The research shows that modern survey technology contributes to the digital 3D monitoring and documentation of cultural heritage.

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China.

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China.

Application of GIS in spatial analysis of industry concentration: the case study of Tesanj municipality (Bosnia and Herzegovina)

The focus of this study is a spatial analysis of the industry distribution and concentration, on the example of municipality of Tesanj (Bosnia and Herzegovina), based on the application of GIS methods. Municipality of Tesanj is one of the most developed municipalities in Bosnia and Herzegovina, while Tesanj?s industry significantly contributes to the economic development of this country. GIS analysis of industry was based on the field work, locating and georeferencing industrial companies, acquisition, adaptation, adjustment and validation of digital surface models and google satellite imagery, mapping of relief, hydrography and transport network. With the aim of geovisualization of spatial distribution of industry, spatial descriptive statistics and non-parametric density estimation analysis (Kernel Density Estimation) were performed. In order to determine the distribution of industry in relation to relief (elevation, slope, and aspect), hydrography and transport network, GIS techniques of reclassification, vectorization, count point in polygons, buffer and clip geoprocessing tools were used. Conducted research provided a new insight into the data mining and visualization based on principles of QGIS, the effects of geographic factors on industry distribution, and confirmed the importance of relief, hydrographic and transport network on the concentration of industry in municipality Tesanj.

An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors

Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. Nevertheless, the use of hyperspectral data still poses challenges mainly in postprocessing to correct from high geometric deformation of images. In general, the acquisition of high-quality hyperspectral imagery is achieved through a time-consuming and complex processing workflow. However, this effort is mandatory when using hyperspectral imagery in a multisensor data fusion perspective, such as with thermal infrared imagery or photogrammetric point clouds. Push-broom hyperspectral sensors provide high spectral resolution data, but its scanning acquisition architecture imposes more challenges to create geometrically accurate mosaics from multiple hyperspectral swaths. In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic. Using as input preprocessed hyperspectral swaths, apart from the need of introducing some control points, the workflow is fully automatic and consists of: adaptive swath subdivision into multiple fragments; detection of significant image features; estimation of valid matches between individual swaths and the RGB orthophoto mosaic; and calculation of the best geometric transformation model to the retrieved matches. As a result, geometrical distortions of hyperspectral swaths are corrected and an orthomosaic is generated. This methodology provides an expedite solution able to produce a hyperspectral mosaic with an accuracy ranging from two to five times the ground sampling distance of the high-resolution RGB orthophoto mosaic, enabling the hyperspectral data integration with data from other sensors for multiple applications.

Mapping and estimating harvest potential of seaweed culture using Worldview-2 Satellite images: a case study in Nusa Lembongan, Bali − Indonesia

Unreliable information on harvest potential is a persistent challenge for the Indonesian government and industry alike to manage an efficient supply chain of seaweed raw material. The use of remote sensing technology to assess seaweed harvest potential has been scarcely available in the literature. This current research aimed at estimating the harvest potential of seaweed Kappaphycus alvarezii through remote sensing using supervised classification with maximum likelihood (MLC) and contextual editing (CE) methods. This research evaluated the capabilities of different band combinations along with depth invariant index (DII) to enhance the remote sensing accuracy in estimating seaweed harvest potential. The seaweed classification using Worldview-2 imagery was compared with the in-situ references (ground-truthing). The potential data bias resulted from different imagery acquisition timestamps with the in-situ measurement was kept minimal as both data time stamps were ten days apart and within the same seaweed culture cycle. The average dry weight of all seaweed samples collected during the research was 924 ± 278.91 g/m2 with culture ages between 1 and 40 days. The classification results based on MLC+CE with a 5-band combination method without DII showed a better correlation and closer fit with the in-situ references compared to the other methods, with an overall accuracy of 79.05% and Tau coefficient value of 0.75. The estimated total harvest potential based on the combined seaweed classes was 531.26 ± 250.29 tons dry weight.

Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2

The frequent acquisitions of fine spatial resolution imagery (10 m) offered by recent multispectral satellite missions, including Sentinel-2, can resolve single agricultural fields and thus provide crop-specific phenology metrics, a crucial information for crop monitoring. However, effective phenology retrieval may still be hampered by significant cloud cover. Synthetic aperture radar (SAR) observations are not restricted by weather conditions, and Sentinel-1 thus ensures more frequent observations of the land surface. However, these data have not been systematically exploited for phenology retrieval so far. In this study, we extracted crop-specific land surface phenology (LSP) from Sentinel-1 and Sentinel-2 of major European crops (common and durum wheat, barley, maize, oats, rape and turnip rape, sugar beet, sunflower, and dry pulses) using ground-truth information from the “Copernicus module” of the Land Use/Cover Area frame statistical Survey (LUCAS) of 2018. We consistently used a single model-fit approach to retrieve LSP metrics on temporal profiles of CR (Cross Ratio, the ratio of the backscattering coefficient VH/VV from Sentinel-1) and NDVI (Normalized Difference Vegetation Index from Sentinel-2). Our analysis revealed that LSP retrievals from Sentinel-1 are comparable to those of Sentinel-2, particularly for winter crops. The start of season (SOS) timings, as derived from Sentinel-1 and -2, are significantly correlated (average r of 0.78 for winter and 0.46 for summer crops). The correlation is lower for end of season retrievals (EOS, r of 0.62 and 0.34). Agreement between LSP derived from Sentinel-1 and -2 varies among crop types, ranging from r = 0.89 and mean absolute error MAE = 10 days (SOS of dry pulses) to r = 0.15 and MAE = 53 days (EOS of sugar beet). Observed deviations revealed that Sentinel-1 and -2 LSP retrievals can be complementary; for example for winter crops we found that SAR detected the start of the spring growth while multispectral data is sensitive to the vegetative growth before and during winter. To test if our results correspond reasonably to in-situ data, we compared average crop-specific LSP for Germany to average phenology from ground phenological observations of 2018 gathered from the German Meteorological Service (DWD). Our study demonstrated that both Sentinel-1 and -2 can provide relevant and at times complementary LSP information at field- and crop-level.

Mapping Burn Extent of Large Wildland Fires from Satellite Imagery Using Machine Learning Trained from Localized Hyperspatial Imagery

Wildfires burn 4–10 million acres annually across the United States and wildland fire related damages and suppression costs have exceeded $13 billion for a single year. High-intensity wildfires contribute to post-fire erosion, degraded wildlife habitat, and loss of timber resources. Accurate and temporally adequate assessment of the effects of wildland fire on the environment is critical to improving the of wildland fire as a tool for restoring ecosystem resilience. Sensor miniaturization and small unmanned aircraft systems (sUAS) provide affordable, on-demand monitoring of wildland fire effects at a much finer spatial resolution than is possible with satellite imagery. The use of sUAS would allow researchers to obtain data with more detail at a much lower initial cost. Unfortunately, current regulatory and technical constraints prohibit the acquisition of imagery using sUAS for the entire extent of large fires. This research examined the use of sUAS imagery to train and validate burn severity and extent mapping of large wildland fires from various satellite images. Despite the lower resolution of the satellite image, the research utilized the advantages of satellite imagery such as global coverage, low cost, temporal stability, and spectral extent while leveraging the higher resolution of hyperspatial sUAS imagery for training and validating the mapping analytics.

Assessment of subsidence phenomena for the Thatta, Jamshoro Districts, Southern Pakistan

The focus of this study is the monitoring and analysis of the most recent crustal deformation phenomena in Southern Pakistan. For subsidence evaluation, the longer temporal monitoring is recommended; therefore, the time period chosen for this measurement is between two separate 2005 to 2008 and 2014 to 2018 time series analysis. Remote sensing, synthetic aperture radar interferometry techniques (InSAR) technique is employed for the understanding of localized phenomena of crustal deformation in spatio-temporal coverage. Thatta and Jamshoro districts of the Southern Pakistan region are selected as areas of interest (AOI). These districts are notable for the subsidence phenomena in Pakistan. ENVISAT and central sub swath of Sentinal-1A satellites and InSAR datasets are utilized for quantification of subsidence rate of the area of interest because of the accuracy of their sensors up to millimeters. These satellite missions have widespread spatial coverage, continuous temporal sampling, and free data accessibility due to open data policy of European Space Agency. These two different satellite datasets for different periods of acquisitions provided the comparison of the subsidence rate between temporal periods of these two satellite imageries for area of interest. The long-term data monitoring enables to understand temporal-spatial characteristic to the AOI and understanding of the localized trend. Computation of localized subsidence pattern from ENIVSAT and central sub swath IW2 of Sentinel-1A had been carried out from period Jan 2005 to Dec 2009 with 32 acquisitions and from April 2014 to Nov 2018 with 49 imagery acquisitions, respectively, for ascending orbit. For this Line of Sight (LOS) displacement measurement, InSAR time-series analysis is utilized with the application of the small baseline subset (SBAS) interferogram technique. SBAS time series analysis emerged as strong algorithm with capability to reduce temporal decorrelation for deformation investigation within last couple of years. A total of 53 interferograms from ENVISAT and 141 interferograms from Sentinel-1A are formed. Relative temporal baseline positions are of ± 300 m for ENVISAT and ± 150 m for Sentinel-1A with respect to master acquisition. Subsidence of average 4 mm per year is measured along the cross section line from ENVISAT in the area of interest with total subsidence of 24 mm over a period of 6 years. During the Sential-1A acquisition period, the subsidence rate is the same but the area of subsidence is smaller in size as compared with the previous period of monitoring with ENVISAT. Maximum subsidence noted for the period from May 2005 to Aug 2006. It was the same period when over pumping of water was carried out in the area of interest. The larger deformation zone is due to the intense extraction of subsurface freshwater, because the subsidence correlated with the data of tube wells provided by the country water management agency. The data showed the increase in numbers of tube wells installed in the locality over the first phase study time period with extraction of water up to 7000 ha per year (Development Statistics of Sindh 2001-2016) with depletion of groundwater table up to 122 m from the surface.

ANALISIS PERUBAHAN LUAS HUTAN MANGROVE DAN FAKTOR YANG MEMPENGARUHINYA DI PULAU TANAKEKE KAB. TAKALAR

Knowing how the changes in the area of mangrove forest on Tanakeke Island, Takalar Regency in 1998 - 2018, Knowing the factors that influence changes in the area of mangrove forests on Tanakeke Island, Takalar Regency.Analysis of changes used is remote sensing image analysis. The imagery used is LANDSAT 5 TM satellite imagery acquisition in 1998, LANDSAT 7 + ETM satellite imagery acquisition in 2008 and LANDSAT 8 imagery OLI acquisition in 2018. Guided classification method with maximum like-lihood method is used to determine changes in mangrove forest area, while for activity factors humans that affect changes in mangrove area are analyzed in terms of percentage descriptions. The area of Tanakeke Island's mangrove forests in 1998 was 3,309.93 hectares, in 2008 it was 2,699.2 hectares, which was reduced by 621.91 hectares. And in 2018 the area will be 2,421.38 hectares and the total change in the area of mangove forests will be reduced by 908.55 hectares. The factors of human activity that affect changes in the area of mangrove forests most importantly is the conversion of mangrove forest land into ponds and uncontrolled collection of firewood for firewood.

Effect of photogrammetric RPAS flight parameters on plani-altimetric accuracy of DTM

AbstractRemotely piloted aerial systems (RPASs) are gaining fast and wide application around the world due to its relative low-cost advantage in the acquisition of high-resolution imagery. However, standardized protocols for the construction of cartographic products are needed. The aim of this paper is to optimize the generation of digital terrain models (DTMs) by using different RPAS flight parameters. An orthogonal design L18 was used to measure the effect of photogrammetric flight parameters on the DTM generated. The image data were acquired using a DJI Phantom 4 Pro drone and six flight parameters were evaluated: flight mode, altitude, flight speed, camera tilt, longitudinal overlap and transversal overlap. Fifty-one ground control points were established using a global positioning system. Multivision algorithms were used to obtain ultra-high resolution point clouds, orthophotos and 3D models from the photos acquired. Root mean square error was used to measure the geometric accuracy of DTMs generated. The effect of photogrammetric flight parameters was carried out by using analysis of variance statistical analysis. Altimetric and planimetric accuracies of 0.38 and 0.11 m were achieved, respectively. Based on these results, high-precision cartographic material was generated using low-cost technology.