The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Isbaex, Crismeire; Coelho, Ana Rita

doi:10.5772/intechopen.93363

Cited by 9 publications

(2 citation statements)

References 112 publications

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“…Large territorial coverages make remote sensing technologies a particularly helpful tool for spatially mapping forest features, by enabling continuous predictions and extrapolations in areas where hand-collected data is unavailable [5]. Among the various active and passive sensors available for forest monitoring, satellite imagery is the most widespread due to its relative ease of acquisition and broad spatial and temporal scopes [25,26]. In the context of forest monitoring in China, satellite imagery has been increasingly used to estimate variables such as aboveground biomass [27], alpha diversity [28], and species presence and abundance [29], being the detection of forests' spatial drivers a less explored task.…”

Section: Introductionmentioning

confidence: 99%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

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Understanding the spatial variations of forest features and their underlying drivers is crucial for managing and preserving forest ecosystems. Remote sensing techniques, due to their vast spatial coverage, are proving an essential tool for mapping and analyzing the complex spatial patterns of forests characteristics. In the current study, we use high-resolution Gaofen 1 satellite imagery to map features, such as biomass, biodiversity, and habitat type, of the subtropical forests in the Nanling area, a major biodiversity hot spot in the south of China; for subsequently analyzing the association between forest features and environmental drivers. We based our modeling approach on a semi-supervised fully convolutional neural network consisting of a ResNet architecture and a masking and aggregation module for computing predictions for forest survey plots of varied sizes. The fitted convolutional models yielded very high performance statistics, with test R2 values above 0.90 for basal area, aboveground biomass and Shannon diversity index regressions, slightly lower values for the regression of species richness (R2=0.83), and average test classification accuracy of forest types of 0.89, with lower performance for infrequent forest types, such as dwarf forests. The association analysis between predicted maps of forest features and environmental drivers revealed significant correlations being the strongest drivers the altitude, nitrogen content and climate variables, such as mean annual precipitation and temperature. Altitude was strongly associated with dwarf forests and high basal area and high aboveground biomass locations. Basal area and aboveground biomass were also positively influenced by annual rainfall and soil nitrogen and carbon, and negatively by temperature and radiation variables. Species richness was associated with high nitrogen and carbon contents, while the Shannon diversity index showed overall mild correlations with most drivers, with no remarkable patterns. We concluded that our semi-supervised convolutional approach using Gaofen 1 imagery proved useful for modeling the spatial distribution of the subtropical forests in Nanling; however, more research and data collection is necessary to improve the performance of species richness modeling and segmentation of infrequent forest types. Moreover, the strong associations found between climate, and predicted maps of biomass and forest types, provided key insights into the potential repercussions of climate change on Nanling forests.

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Section: Introductionmentioning

confidence: 99%

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

González-Rodríguez,

2023

Preprint

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“…Sentinel-2 imagery was used, which provides free and open services and data with a high spatial resolution of 10-60 m (depending on the band). It has a temporal resolution of about 5 days (depending on the latitude) (Escolà et al, 2017a;Isbaex and Margarida Coelho, 2021). Such spatial and temporal resolutions and the availability of images free of charge make Sentinel-2 very appealing for crop monitoring.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Winter Wheat Using Sentinel-2 NDVI Data. A Case of Mashonaland Central Province in Zimbabwe

et al. 2021

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A robust early warning system can alert to the presence of food crises and related drivers, informing decision makers on food security. To date, decision-makers in Zimbabwe still rely on agriculture extension personnel to generate information on wheat production and monitor the crop. Such traditional methods are subjective, costly and their accuracy depends on the experience of the assessor. This study investigates Sentinel-2 NDVI and time series utility as a wheat-monitoring tool over the wheat-growing areas of Zimbabwe's Bindura, Shamva, and Guruve districts. NDVI was used to classify and map the wheat fields. The classification model's evaluation was done by creating 100 reference pixels across the classified map and constructing a confusion matrix with a resultant kappa coefficient of 0.89. A sensitivity test, receiver operating characteristic (ROC) and area under the curve (AUC) were used to measure the model's efficiency. Fifty GPS points randomly collected from wheat fields in the selected districts were used to identify and compute the area of the fields. The correlation between the area declared by farmers and the calculated area was positive, with an R2 value of 0.98 and a Root Mean Square Error (RMSE) of 2.23 hectares. The study concluded that NDVI is a good index for estimating the area under wheat. In this regard, NDVI can be used for early warning and early action, especially in monitoring programs like ‘Command Agriculture’ in Zimbabwe. In current and future studies, the use of high-resolution images from remote sensing is essential. Furthermore, ground truthing is always important to validate results from remote sensing at any spatial scale.

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Ecological potential assessment and land use area estimation of agricultural lands based on multi-time images of Sentinel-2 using ANP-WLC and GIS in Bastam, Iran

Navidi

Chatrenour

Seyedmohammadi

et al. 2022

Environ Monit Assess

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The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

Cited by 9 publications

References 112 publications

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Mapping and analyzing the spatial drivers of Chinese subtropical forests features from Gaofen 1 satellite imagery

Mapping of Winter Wheat Using Sentinel-2 NDVI Data. A Case of Mashonaland Central Province in Zimbabwe

Ecological potential assessment and land use area estimation of agricultural lands based on multi-time images of Sentinel-2 using ANP-WLC and GIS in Bastam, Iran

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