Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR

The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.

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Using Digital Surface Models from UAS Imagery of Fire Damaged Sphagnum Peatlands for Monitoring and Hydrological Restoration

Drones ◽

10.3390/drones2040045 ◽

2018 ◽

Vol 2 (4) ◽

pp. 45 ◽

Cited By ~ 5

Author(s):

Shannon de Roos ◽

Darren Turner ◽

Arko Lucieer ◽

David M.J.S. Bowman

Keyword(s):

Unmanned Aerial Systems ◽

Control Points ◽

Hydrological Models ◽

Slow Recovery ◽

Ground Control Points ◽

Surface Models ◽

Good Potential ◽

Northern Peatlands ◽

Aerial Systems

The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands; however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground- controlled DSMs.

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City-Level Comparison of Urban Land-Cover Configurations from 2000–2015 across 65 Countries within the Global Belt and Road

Remote Sensing ◽

10.3390/rs11131515 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1515 ◽

Cited By ~ 5

Author(s):

Tao Pan ◽

Wenhui Kuang ◽

Rafiq Hamdi ◽

Chi Zhang ◽

Shu Zhang ◽

...

Keyword(s):

Land Cover ◽

Average Rate ◽

Bare Soil ◽

Urban Land ◽

Impervious Surface Area ◽

Capital Cities ◽

Urban Land Cover ◽

Belt And Road ◽

Land Covers ◽

The Belt And Road

The configuration of urban land-covers is essential for improving dwellers’ environments and ecosystem services. A city-level comparison of land-cover changes along the Belt and Road is still unavailable due to the lack of intra-urban land products. A synergistic classification methodology of sub-pixel un-mixing, multiple indices, decision tree classifier, unsupervised (SMDU) classification was established in the study to examine urban land covers across 65 capital cities along the Belt and Road during 2000–2015. The overall accuracies of the 15 m resolution urban products (i.e., the impervious surface area, vegetation, bare soil, and water bodies) derived from Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) images were 92.88% and 93.19%, with kappa coefficients of 0.84 and 0.85 in 2000 and 2015, respectively. The built-up areas of 65 capital cities increased from 23,696.25 km2 to 29,257.51 km2, with an average growth rate of 370.75 km2/y during 2000–2015. Moreover, urban impervious surface area (ISA) expanded with an average rate of 401.92 km2/y, while the total area of urban green space (UGS) decreased with an average rate of 17.59 km2/y. In different regions, UGS changes declined by 7.37% in humid cities but increased by 14.61% in arid cities. According to the landscape ecology indicators, urban land-cover configurations became more integrated (△Shannon’s Diversity Index (SHDI) = −0.063; △Patch Density (PD) = 0.054) and presented better connectivity (△Connectance Index (CON) = +0.594). The proposed method in this study improved the separation between ISA and bare soil in mixed pixels, and the 15 m intra-urban land-cover product provided essential details of complex urban landscapes and urban ecological needs compared with contemporary global products. These findings provide valuable information for urban planners dealing with human comfort and ecosystem service needs in urban areas.

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Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification

Remote Sensing ◽

10.3390/rs11060660 ◽

2019 ◽

Vol 11 (6) ◽

pp. 660 ◽

Cited By ~ 2

Author(s):

Chang-An Liu ◽

Zhongxin Chen ◽

Di Wang ◽

Dandan Li

Keyword(s):

Land Cover ◽

Urban Areas ◽

Agricultural Land ◽

Test Site ◽

Decomposition Methods ◽

Bare Soil ◽

Classification Error ◽

Land Cover Types ◽

Land Covers

We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing.

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Separability Analysis of Back-Scattering Coefficient of NovaSAR-1 S-band SAR datasets for different Land Use Land Cover (LULC) classes

10.21203/rs.3.rs-854337/v1 ◽

2021 ◽

Author(s):

Ashutosh Bhardwaj ◽

Ojasvi Saini ◽

R. S. Chatterjee

Keyword(s):

Land Use ◽

Land Cover ◽

High Frequency ◽

Low Frequency ◽

Bare Soil ◽

Low Noise ◽

Frequency Range ◽

X Band ◽

L Band ◽

Land Covers

Abstract NovaSAR-1 is a joint technology initiative of SSTL (Surrey Satellite Technology Ltd.), UK, and Airbus DS (former EADS Astrium Ltd, Stevenage, UK). The NovaSAR-1 mini-satellite was launched on 16 September 2018 and it is operating on the S-band frequency range, which is less common in Spaceborne Synthetic Aperture Radar (SAR) systems. Both higher and lower SAR frequency bands (L-band & X-band SAR) have their advantages as well as limitations in different kinds of applications. High frequency (X-band) SAR systems are useful for top surface information extraction such as the DSM generation. However, at the same time, more noise and lesser coherence issues are associated with high-frequency SAR systems. Low-frequency SAR (L-band) systems exhibit better ground penetration, high coherence, and low noise, but less precise scatterer level information. The S-band comes approximately in the middle of the X and L-band SAR frequency range and may be used as a trade-off between high and low-frequency SAR systems to have some advantages. In the presented study, the separability analysis of the radar backscattering coefficient of HH polarization (Stripmap and ScanSAR) of NovaSAR-1 S-band datasets corresponding to different land use and land covers (LULCs) has been done to analyze the potential of NovaSAR-1 S-band SAR data. The analysis was carried out for datasets acquired between 9th July 2019 to 15th July 2019 at 5 experimental sites in parts of six different Indian states (West Bengal, Maharashtra, Jharkhand, Odisha, Chhattisgarh, and Uttar Pradesh). The statistical analysis of σ० for five different sites of India for different LULCs, such as bare soil, forest, water, urban, cropland, and road features has been carried out. The range for minimum and maximum mean σ० values for urban, sub-urban, cropland, bare soil, barren land, forest, turbid water, road features, water with a smooth surface (calm water), and road features (airplane runway) were found to be -5.45 to 4.76, -11.14 to -5.21, -17.25 to -5.99, -18.15 to -13.44, -17.64 to -9.34, -17.17 to -14.34, -18.2 to -14.05, -27.29 to -23.76 and − 26.64 to -14.98 respectively. The range of σ० pixel values of calibrated datasets corresponding to different LULCs depicted that the data quality is good for the identification of various land covers. The separability analysis of the different land cover classes depicted that classes have good separability except for a few pairs of LULC. With the availability of fully polarimetric and InSAR data in the planned NISAR mission, the polarimetric scattering behaviour with phase information for InSAR will further be utilized.

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Data post processing

10.1093/oso/9780198787617.003.0007 ◽

2018 ◽

Author(s):

Serge A. Wich ◽

Lian Pin Koh

Keyword(s):

Land Cover ◽

Structure From Motion ◽

Land Cover Classification ◽

Post Processing ◽

Surface Models

This chapter discusses how data that have been collected with drones can be used to derive orthomosaics and digital surface models through structure-from-motion software and how these can be processed further for land-cover classification or into vegetation metrics. Some examples of the various programs are provided as well. The chapter ends with a discussion on the approaches that have been used to automate counts of animals in drone images.

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A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽

10.3390/land10030231 ◽

2021 ◽

Vol 10 (3) ◽

pp. 231

Author(s):

Can Trong Nguyen ◽

Amnat Chidthaisong ◽

Phan Kieu Diem ◽

Lian-Zhi Huo

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Urban Areas ◽

Near Infrared ◽

Urban Landscape ◽

Bare Soil ◽

Critical Element ◽

Landsat 8 ◽

Climate Conditions ◽

Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

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Evaluating and improving the Community Land Model's sensitivity to land cover

Biogeosciences ◽

10.5194/bg-15-4731-2018 ◽

2018 ◽

Vol 15 (15) ◽

pp. 4731-4757 ◽

Cited By ~ 13

Author(s):

Ronny Meier ◽

Edouard L. Davin ◽

Quentin Lejeune ◽

Mathias Hauser ◽

Yan Li ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Land Surface ◽

Soil Column ◽

Plant Functional Type ◽

Model Parameters ◽

Functional Type ◽

Land Surface Models ◽

Surface Models ◽

Open Land

Abstract. Modeling studies have shown the importance of biogeophysical effects of deforestation on local climate conditions but have also highlighted the lack of agreement across different models. Recently, remote-sensing observations have been used to assess the contrast in albedo, evapotranspiration (ET), and land surface temperature (LST) between forest and nearby open land on a global scale. These observations provide an unprecedented opportunity to evaluate the ability of land surface models to simulate the biogeophysical effects of forests. Here, we evaluate the representation of the difference of forest minus open land (i.e., grassland and cropland) in albedo, ET, and LST in the Community Land Model version 4.5 (CLM4.5) using various remote-sensing and in situ data sources. To extract the local sensitivity to land cover, we analyze plant functional type level output from global CLM4.5 simulations, using a model configuration that attributes a separate soil column to each plant functional type. Using the separated soil column configuration, CLM4.5 is able to realistically reproduce the biogeophysical contrast between forest and open land in terms of albedo, daily mean LST, and daily maximum LST, while the effect on daily minimum LST is not well captured by the model. Furthermore, we identify that the ET contrast between forests and open land is underestimated in CLM4.5 compared to observation-based products and even reversed in sign for some regions, even when considering uncertainties in these products. We then show that these biases can be partly alleviated by modifying several model parameters, such as the root distribution, the formulation of plant water uptake, the light limitation of photosynthesis, and the maximum rate of carboxylation. Furthermore, the ET contrast between forest and open land needs to be better constrained by observations to foster convergence amongst different land surface models on the biogeophysical effects of forests. Overall, this study demonstrates the potential of comparing subgrid model output to local observations to improve current land surface models' ability to simulate land cover change effects, which is a promising approach to reduce uncertainties in future assessments of land use impacts on climate.

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Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms

Drones ◽

10.3390/drones4020013 ◽

2020 ◽

Vol 4 (2) ◽

pp. 13 ◽

Cited By ~ 4

Author(s):

Margaret Kalacska ◽

Oliver Lucanus ◽

J. Pablo Arroyo-Mora ◽

Étienne Laliberté ◽

Kathryn Elmer ◽

...

Keyword(s):

Low Cost ◽

Ground Control ◽

Unmanned Aerial Systems ◽

Control Points ◽

Model Errors ◽

Positional Accuracy ◽

Ground Control Points ◽

Digital Elevation ◽

High Level ◽

Aerial Systems

The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.

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Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

Environment and Natural Resources Journal ◽

10.32526/ennrj/20/202100110 ◽

2021 ◽

Vol 20 (2) ◽

pp. 1-19

Author(s):

Tahmid Anam Chowdhury ◽

◽

Md. Saiful Islam ◽

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Bare Soil ◽

Land Use Land Cover ◽

Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

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Effects of seasonality and land use on the abundance and distribution of mosquitoes on St. Kitts, West Indies

10.1101/2020.05.11.089037 ◽

2020 ◽

Author(s):

Matthew J. Valentine ◽

Brenda Ciraola ◽

Gregory R. Jacobs ◽

Charlie Arnot ◽

Patrick J. Kelly ◽

...

Keyword(s):

Land Cover ◽

Disease Transmission ◽

Negative Binomial ◽

Mosquito Control ◽

Mosquito Species ◽

Negative Binomial Regression ◽

Common Species ◽

Negative Binomial Regression Model ◽

Local Data ◽

Land Covers

AbstractBackgroundHigh quality mosquito surveys that collect fine resolution local data on mosquito species’ abundances provide baseline data to help us understand potential host-pathogen-mosquito relationships, accurately predict disease transmission, and target mosquito control efforts in areas at risk of mosquito borne diseases.MethodsAs part of an investigation into arboviral sylvatic cycles on the Caribbean island of St. Kitts, we carried out an island wide mosquito survey from November 2017 to March 2019. Using Biogents Sentinel 2 and miniature CDC light traps that were set monthly and run for 48 hour intervals, we collected mosquitoes from a total of 30 sites distributed across the five common land covers on the island (agricultural, mangrove, rainforest, scrub, and urban). We developed a mixed effects negative binomial regression model to predict the effects of land cover, seasonality, and precipitation on observed counts of the most abundant mosquito species we found.ResultsWe captured 10 of the 14 mosquito species reported on the island, the four most abundant being Aedes taeniorhynchus, Culex quinquefasciatus, Aedes aegpyti, and Deinocerites magnus. Sampling in the mangroves yielded the most mosquitoes, with Ae. taeniorhynchus, Cx. quinquefasciatus, and De. magnus predominating. Aedes aegypti was recovered primarily from urban and agricultural habitats, but also at lower frequency in other land covers. Psorophora pygmaea and Toxorhynchites guadeloupensis were only captured in scrub habitat. Capture rates in rainforests were low. Our models indicated the relative abundance of the four most common species varied seasonally and with land cover. They also suggested that the extent to which monthly average precipitation influenced counts varied according to species.ConclusionsThis study demonstrates there is high seasonality in mosquito abundances and that land cover influenced the distribution and abundance of mosquito species on St. Kitts. Further, human-adapted mosquito species (e.g. Ae. aegypti and Cx. quinquefasciatus) that are known vectors for many human relevant pathogens are the most wide-spread (across land covers) and the least responsive to seasonal variation in precipitation.

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