The Best of Both Worlds? Integrating Sentinel-2 Images and airborne LiDAR to Characterize Forest Regeneration

Sustainable forest management relies on practices ensuring vigorous post-harvest regeneration. Data on regeneration structure and composition are often collected through intensive field surveys. Remote sensing technologies (e.g., Light Detection and Ranging (LiDAR), satellite imagery) can cover a much larger spatial extent, but their ability to estimate regeneration characteristics is often challenged by the obstruction associated with canopy foliage. Here, we determined whether the integration of LiDAR and Sentinel-2 images can increase the accuracy of sapling density estimates and whether this accuracy decreased with canopy cover in the Acadian forest of New Brunswick, Canada. Using random forest regression, we compared the accuracy of three models (LiDAR and Sentinel-2 images alone or combined) to estimate sapling density for two species groups: saplings of all species or commercial species only. The integration of both sensors did not increase the accuracy of sapling density estimates, nor did it reduce the negative influence of canopy cover for either species group compared to LiDAR, but it increased the accuracy by approximately 15% relative to Sentinel-2 images. Under very high canopy cover, the accuracy of density estimates for all species combined was significantly lower with Sentinel-2 images only. We recommend using LiDAR and high-resolution satellite images acquired in the fall to obtain more accurate estimates of sapling density.

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Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia

Land ◽

10.3390/land7030092 ◽

2018 ◽

Vol 7 (3) ◽

pp. 92 ◽

Cited By ~ 3

Author(s):

Zerihun Asrat ◽

Habitamu Taddese ◽

Hans Ørka ◽

Terje Gobakken ◽

Ingunn Burud ◽

...

Keyword(s):

Satellite Images ◽

Forest Cover ◽

Image Interpretation ◽

Canopy Cover ◽

Visual Image ◽

Forest Area ◽

Sample Sizes ◽

High Resolution Satellite Images ◽

Visual Image Interpretation ◽

Sentinel 2

Forests, particularly in the tropics, are suffering from deforestation and forest degradations. The estimation of forest area and canopy cover is an essential part of the establishment of a measurement, reporting, and verification (MRV) system that is needed for monitoring carbon stocks and the associated greenhouse gas emissions and removals. Information about forest area and canopy cover might be obtained by visual image interpretation as an alternative to expensive fieldwork. The objectives of this study were to evaluate different types of satellite images for forest area and canopy cover estimation though visual image interpretation, and assess the influence of sample sizes on the estimates. Seven sites in Ethiopia with different vegetation systems were subjectively identified, and visual interpretations were carried out in a systematical design. Bootstrapping was applied to evaluate the effects of sample sizes. The results showed that high-resolution satellite images (≤5 m) (PlanetScope and RapidEye) images produced very similar estimates, while coarser resolution imagery (10 m, Sentinel-2) estimates were dependent on forest conditions. Estimates based on Sentinel-2 images varied significantly from the two other types of images in sites with denser forest cover. The estimates from PlanetScope and RapidEye were less sensitive to changes in sample size.

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Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

Remote Sensing ◽

10.3390/rs12111876 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1876 ◽

Cited By ~ 1

Author(s):

Katsuto Shimizu ◽

Tetsuji Ota ◽

Nobuya Mizoue ◽

Hideki Saito

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Time Series Data ◽

Airborne Lidar ◽

Canopy Height ◽

Forest Monitoring ◽

Series Data ◽

Landsat 8 ◽

Multi Temporal ◽

Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

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Empirical Modeling of Leaf Chlorophyll Content in Coffee (Coffea Arabica) Plantations With Sentinel-2 MSI Data: Effects of Spectral Settings, Spatial Resolution, and Crop Canopy Cover

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2017.2750325 ◽

2017 ◽

Vol 10 (12) ◽

pp. 5541-5550 ◽

Cited By ~ 14

Author(s):

Abel Chemura ◽

Onisimo Mutanga ◽

John Odindi

Keyword(s):

Chlorophyll Content ◽

Spatial Resolution ◽

Coffea Arabica ◽

Canopy Cover ◽

Empirical Modeling ◽

Crop Canopy ◽

Leaf Chlorophyll Content ◽

Leaf Chlorophyll ◽

Sentinel 2

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A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b1-2020-9-2020 ◽

2020 ◽

Vol XLIII-B1-2020 ◽

pp. 9-14

Author(s):

F. Pineda ◽

V. Ayma ◽

C. Beltran

Keyword(s):

High Resolution ◽

Satellite Images ◽

Resolution Enhancement ◽

Super Resolution ◽

Structural Similarity ◽

Generative Adversarial Network ◽

Acceptable Result ◽

Adversarial Network ◽

High Resolution Satellite Images ◽

Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

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Fusing Sentinel-2 Imagery and ALS Point Clouds for Defining LULC Changes on Reclaimed Areas by Afforestation

Sustainability ◽

10.3390/su11051251 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1251 ◽

Cited By ~ 5

Author(s):

Marta Szostak ◽

Kacper Knapik ◽

Piotr Wężyk ◽

Justyna Likus-Cieślik ◽

Marcin Pietrzykowski

Keyword(s):

Laser Scanning ◽

Canopy Cover ◽

Point Clouds ◽

Test Site ◽

Vegetation Growth ◽

Landsat 7 ◽

Forested Areas ◽

Primary Focus ◽

Year 2000 ◽

Sentinel 2

The study was performed on two former sulphur mines located in Southeast Poland: Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The areas were characterized by its terrain structure and vegetation cover resulting from the reclamation process. The types of reclamation applied in these areas were forestry in Jeziórko and agroforestry in the Machów post-sulphur mine. The study investigates the possibility of applying the most recent Sentinel-2 (ESA) satellite imageries for land cover mapping, with a primary focus on detecting and monitoring afforested areas. Airborne laser scanning point clouds were used to derive precise information about the spatial (3D) characteristics of vegetation: the height (95th percentile), std. dev. of relative height, and canopy cover. The results of the study show an increase in afforested areas in the former sulphur mines. For the entire analyzed area of Jeziórko, forested areas made up 82.0% in the year 2000 (Landsat 7, NASA), 88.8% in 2009 (aerial orthophoto), and 95.5% in 2016 (Sentinel-2, ESA). For Machów, the corresponding results were 46.1% in 2000, 57.3% in 2009, and 60.7% in 2016. A dynamic increase of afforested area was observed, especially in the Jeziórko test site, with the presence of different stages of vegetation growth.

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Improving Estimation of Forest Canopy Cover by Introducing Loss Ratio of Laser Pulses Using Airborne LiDAR

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2019.2938017 ◽

2020 ◽

Vol 58 (1) ◽

pp. 567-585 ◽

Cited By ~ 5

Author(s):

Qingwang Liu ◽

Liyong Fu ◽

Guangxing Wang ◽

Shiming Li ◽

Zengyuan Li ◽

...

Keyword(s):

Forest Canopy ◽

Canopy Cover ◽

Laser Pulses ◽

Airborne Lidar ◽

Loss Ratio

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Hardwood Density and Species Composition in Bottomland Areas Treated for Regeneration

Southern Journal of Applied Forestry ◽

10.1093/sjaf/4.3.122 ◽

1980 ◽

Vol 4 (3) ◽

pp. 122-127

Author(s):

George A. Hurst ◽

Thomas R. Bourland

Keyword(s):

Species Composition ◽

Abundant Species ◽

National Forest ◽

Green Ash ◽

Commercial Species ◽

Sapling Density

Abstract Seedling and sapling density of high- and low-commercial species varied from 994 to 2,792/acre on regenerated areas, age three to eight years, on the Delta National Forest, Mississippi. The highest densities were in the oldest regenerated stands, on low flat sites and on areas that had been hand-injected. Most seedlings and saplings were not suppressed by vines and the number growing in the open increased with age. The most abundant species were green ash, water hickory, overcup oak, sugarberry, Nuttall oak, and persimmon. The highest densities of subcommercial species were on sheared areas.

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Integrating Sentinel-2 Imagery with AquaCrop for Dynamic Assessment of Tomato Water Requirements in Southern Italy

Agronomy ◽

10.3390/agronomy9070404 ◽

2019 ◽

Vol 9 (7) ◽

pp. 404 ◽

Cited By ~ 7

Author(s):

Anna Dalla Marta ◽

Giovanni Battista Chirico ◽

Salvatore Falanga Bolognesi ◽

Marco Mancini ◽

Guido D’Urso ◽

...

Keyword(s):

Irrigation Water ◽

Dynamic Assessment ◽

Canopy Cover ◽

Development Stage ◽

Crop Water ◽

Water Requirements ◽

Tomato Crop ◽

Crop Water Requirements ◽

Aquacrop Model ◽

Sentinel 2

A research study was conducted in an open field tomato crop in order to: (i) Evaluate the capability of Sentinel-2 imagery to assess tomato canopy growth and its crop water requirements; and (ii) explore the possibility to predict crop water requirements by assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model. The pilot area was in Campania, a region in the south west of Italy, characterized by a typical Mediterranean climate, where field campaigns were conducted in seasons 2017 and 2018 on processing tomato. Crop water use and irrigation requirement were estimated by means of three different methods: (i) The AquaCrop model; (ii) an irrigation advisory service based on Sentinel-2 imagery known as IRRISAT and (iii) assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model Sentinel-2 imagery proved to be effective for monitoring canopy growth and for predicting irrigation water requirements during mid-season stage of the crop, when the canopy is fully developed. Conversely, the integration of the Sentinel-2 imagery with a crop growth model can contribute to improve the irrigation water requirement predictions in the early and development stage of the crop, when the soil evaporation is not negligible with respect to the total evapotranspiration.

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Estimation of crown and canopy cover from airborne lidar data

Forestry Studies / Metsanduslikud Uurimused ◽

10.2478/v10132-011-0079-5 ◽

2010 ◽

Vol 52 (1) ◽

pp. 5-17 ◽

Cited By ~ 2

Author(s):

Mait Lang

Keyword(s):

Norway Spruce ◽

Scots Pine ◽

Stand Structure ◽

Canopy Cover ◽

Airborne Lidar ◽

Silver Birch ◽

Lidar Data ◽

Crown Cover ◽

Pine Trees ◽

Airborne Lidar Data

Metsa katvuse ja liituse hindamine lennukilt laserskanneriga Tests were carried out in mature Scots pine, Norway spruce and Silver birch stands at Järvselja, Estonia, to estimate canopy cover (K) and crown cover (L) from airborne lidar data. Independent estimates Kc and Lc for K and L were calculated from the Cajanus tube readings made on the ground at 1.3 m height. Lidar data based cover estimates depended on the inclusion of different order returns significantly. In all the stands first order return based estimate K1 was biased positively (3-10%) at the reference height of 1.3 m compared to ground measurements. All lidar based estimates decreased with increasing the reference height. Single return (Ky) and all return (Kk) based canopy cover estimates depended more on the sand structure compared to K1. The ratio of all return count to the first return count D behaved like crown cover estimate in all stands. However, in spruce stand D understimated Lc significantly. In the Scots pine stand K1(1.3) = 0.7431 was most similar canopy cover estimate relative to the ground estimate Kc = 0,7362 whereas Ky(1.3) and Kk(1.3) gave significant underestimates (>15%) of K. Caused by the simple structure of Scots pine stand - only one layer pine trees, the Cajanus tube based canopy cover (Kc), crown cover (Lc) and lidar data based canopy density D(1.3) values were rather similar. In the Norway spruce stand and in the Silver birch stand second layer and regeneration trees were present. In the Silver birch stand Kk(1.3) and Ky(1.3) estimated Kc rather well. In the Norway spruce stand Ky(1.3) and K1(1.3) were the best estimators of Kc whereas Kk(1.3) underestimated canopy cover. Lidar data were found to be usable for canopy cover and crown cover assessment but the selection of the estimator is not trivial and depends on the stand structure.

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