Peatland restoration age (Scotland, UK) can be better reproduced by a classification model based on Sentinel-2 than with high resolution aerial imagery

<p>Damage to peatland globally causes significant contributions to the current net greenhouse gas emissions and pose a further future risk as such damaged peatlands are vulnerable to future climatic stress. Globally, peatland restoration efforts are rapidly increasing in scale as natural climate solutions, yet relatively little effort has been it into effective monitoring of landscape scale restoration projects. We developed a classification model that uses remote observations (Sentinel-2 or national scale aerial imagery from Getmapping) to detect restoration efficacy by training it against a dataset from a chronosequence of spatially collocated peatland restoration sites that had previously been converted to plantation forestry. The Sentinel-2 based model greatly outperformed the aerial imagery-based model (RGB and IR, 25 and 50 cm, respectively). Adding slope to the classification improved kappa by less than 0.02. Prediction of the starting (forestry) and target (restored) state was very robust, and both recent and the oldest restoration sites were spatially well predicted. The main model uncertainties lie with sites of intermediate age, where on-the-ground restoration trajectories based on vegetation composition also differ the most, and with sites where additional layers of management after the initial restoration management have been applied.</p>

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Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah

10.1101/2020.04.14.040956 ◽

2020 ◽

Author(s):

David M. Makori ◽

Elfatih M. Abdel-Rahman ◽

Tobias Landmann ◽

Onisimo Mutanga ◽

John Odindi ◽

...

Keyword(s):

Cost Effective ◽

Landscape Scale ◽

Flowering Plants ◽

Aerial Imagery ◽

Data Sets ◽

Pollination Services ◽

Multispectral Data ◽

Independent Test ◽

Honeybee Health ◽

Sentinel 2

AbstractPollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees’ foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.

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Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Remote Sensing ◽

10.3390/rs13122301 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2301

Author(s):

Zander Venter ◽

Markus Sydenham

Keyword(s):

Land Cover ◽

Atmospheric Correction ◽

Google Earth ◽

Tree Planting ◽

Training Data ◽

Classification Model ◽

Night Time ◽

Continental Scale ◽

Land Cover Maps ◽

Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

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An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

10.5194/egusphere-egu21-5310 ◽

2021 ◽

Author(s):

Luojia Hu ◽

Wei Yao ◽

Zhitong Yu ◽

Yan Huang

Keyword(s):

Time Series ◽

High Resolution ◽

Mangrove Forest ◽

Google Earth ◽

Series Data ◽

National Scale ◽

Mangrove Area ◽

Google Earth Engine ◽

Forest Maps ◽

Sentinel 2

<p>A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (< 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area < 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (< 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.</p>

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Modeling of current and future state of biodiversity in Central America using GLOBIO3 methodology

Land Use, Climate Change and Biodiversity Modeling ◽

10.4018/978-1-60960-619-0.ch017 ◽

2011 ◽

pp. 349-375

Author(s):

Denisse McLean R.

Keyword(s):

Central America ◽

Regional Scale ◽

The State ◽

Central American ◽

Regional Assessment ◽

National Scale ◽

American Experience ◽

Future State ◽

Environment And Development ◽

Main Model

The modeling of the state of biodiversity in Central America using GLOBIO3 methodology was carried out by the Regional Biodiversity Institute for the Central American Commission on Environment and Development. For each country, current and future states of biodiversity under three socio-economic scenarios were explored. The country results were integrated into one regional assessment. The aim of this chapter is to explain how GLOBIO3 was adapted to the national scale. The main issues and the approaches adopted to solve them are described. The results from the Central American experience are presented followed by a discussion on main model limitations and derived recommendations. Finally, the challenges countries face to integrate the results into their government agendas are analyzed. This chapter is expected to be helpful for potential users of GLOBIO3 who are interested in the application of the methodology on a national and sub regional scale.

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Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

Remote Sensing ◽

10.3390/rs12193120 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3120

Author(s):

Luojia Hu ◽

Nan Xu ◽

Jian Liang ◽

Zhichao Li ◽

Luzhen Chen ◽

...

Keyword(s):

Time Series ◽

High Resolution ◽

Mangrove Forest ◽

Time Series Data ◽

Landsat Imagery ◽

Google Earth ◽

Series Data ◽

National Scale ◽

Google Earth Engine ◽

Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

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Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm

Remote Sensing ◽

10.3390/rs12233933 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3933

Author(s):

Anggun Tridawati ◽

Ketut Wikantika ◽

Tri Muji Susantoro ◽

Agung Budi Harto ◽

Soni Darmawan ◽

...

Keyword(s):

Random Forest ◽

Remote Sensing Data ◽

Agroforestry System ◽

Classification Model ◽

Sensor Data ◽

Kappa Statistics ◽

Random Forest Algorithm ◽

Coffee Plantations ◽

Multi Temporal ◽

Sentinel 2

Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

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Evaluating Image Normalization via GANs for Environmental Mapping: A Case Study of Lichen Mapping Using High-Resolution Satellite Imagery

Remote Sensing ◽

10.3390/rs13245035 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5035

Author(s):

Shahab Jozdani ◽

Dongmei Chen ◽

Wenjun Chen ◽

Sylvain G. Leblanc ◽

Julie Lovitt ◽

...

Keyword(s):

High Resolution ◽

Auxiliary Information ◽

Extreme Case ◽

Training Data ◽

Classification Model ◽

Generative Adversarial Networks ◽

Detection Accuracy ◽

Large Area ◽

Environmental Mapping ◽

Sentinel 2

Illumination variations in non-atmospherically corrected high-resolution satellite (HRS) images acquired at different dates/times/locations pose a major challenge for large-area environmental mapping and monitoring. This problem is exacerbated in cases where a classification model is trained only on one image (and often limited training data) but applied to other scenes without collecting additional samples from these new images. In this research, by focusing on caribou lichen mapping, we evaluated the potential of using conditional Generative Adversarial Networks (cGANs) for the normalization of WorldView-2 (WV2) images of one area to a source WV2 image of another area on which a lichen detector model was trained. In this regard, we considered an extreme case where the classifier was not fine-tuned on the normalized images. We tested two main scenarios to normalize four target WV2 images to a source 50 cm pansharpened WV2 image: (1) normalizing based only on the WV2 panchromatic band, and (2) normalizing based on the WV2 panchromatic band and Sentinel-2 surface reflectance (SR) imagery. Our experiments showed that normalizing even based only on the WV2 panchromatic band led to a significant lichen-detection accuracy improvement compared to the use of original pansharpened target images. However, we found that conditioning the cGAN on both the WV2 panchromatic band and auxiliary information (in this case, Sentinel-2 SR imagery) further improved normalization and the subsequent classification results due to adding a more invariant source of information. Our experiments showed that, using only the panchromatic band, F1-score values ranged from 54% to 88%, while using the fused panchromatic and SR, F1-score values ranged from 75% to 91%.

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