Remote sensing for vegetation monitoring in carbon capture storage regions: A review

Chen, Yun; Guerschman, Juan Pablo; Cheng, Zhibo; Guo, Lei

doi:10.1016/j.apenergy.2019.02.027

Cited by 68 publications

(39 citation statements)

References 58 publications

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“…Remote sensing technology has been commonly used in monitoring forest AGC over broad areas due to its wide coverage of observation, timeliness, and repetitive data availability [26,27]. As spectral characteristics of land cover show great differences [28][29][30][31][32], it is one of the important links in current research to accurately quantify various indicators of forest resources [33,34]. Moreover, studies have found that remote sensing data and its derived bands have good practicability for simulating forest AGC [35][36][37]; this is especially the case when combined with machine learning algorithms that allow for large scale automated analysis of high dimensional data from satellites [38].…”

Section: Introductionmentioning

confidence: 99%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

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Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.

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Section: Introductionmentioning

confidence: 99%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

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“…Ecophysiological applications of satellite remotely sensed methods have improved ecosystem-scale predictions of productivity, carbon exchange, water use, leaf area, canopy water content, and species distributions (Waring et al, 1982;Pierce et al, 1990;Running et al, 1995;Renzullo et al, 2008;Adam et al, 2010;Landsberg et al, 2017;Xue and Su, 2017;Moreno-Martínez et al, 2018;Chen et al, 2019). However, current process-based models are hindered by the lack of ground-truthing approaches to validate canopy and leaf-level responses to environmental changes (Liu and Zhou, 2004;Govender et al, 2009;Köhler and Huth, 2010).…”

Section: Discussionmentioning

confidence: 99%

Biophysically Informed Imaging Acquisition of Plant Water Status

Beverly

Ewers

2020

Front. For. Glob. Change

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Vegetation controls carbon and water fluxes because of the fundamental tradeoff between carbon dioxide uptake and water loss occurring when stomata are open. Quantifying the rates of this exchange typically requires either intensive gas exchange or destructive harvesting of tissues and mass spectrometry analyses. Recent developments in high-throughput methods have enhanced our capacity to empirically test plant-environmental interactions. The vast integration characterizing satellite remote sensing methods masks organ-level physiological mechanisms limiting the predictive capability of current process models. Hence, more ground truth studies are necessary to determine the amount of mechanistic information needed to improve our understanding of forest, crop, and land management. Imaging methodologies, such as thermal and chlorophyll a fluorescence, are currently used to collect information for relevant traits such as water use, growth, and stress response. We tested these techniques during progressive drought across species with different susceptibility in controlled greenhouse conditions. We chose two highly represented tree species in North America: the gymnosperm Pinus ponderosa and the angiosperm Populus tremuloides. To better explore the whole drought response parameter space, we also tested a crop (Brassica rapa) and desert shrub (Artemisia tridentata). Thermal and fluorescence images of the canopy were coupled with leaf-level measurements as we performed three tests to predict drought response using (1) leaf temperature, (2) chlorophyll a fluorescence, and (3) the combination of the two. At 5 days of drought, leaf temperature increased 7 and 10%, accounting for 63 and 73% of the variation in stomatal conductance for both tree species, respectively. The fluorescence signal from images decreased ∼12% and ∼83% in moderately and severely droughted leaves respectively, reaching zero at mortality. Leaf water status was then predicted using a Bayesian approach that incorporated measurements' uncertainty and parsimony in the analysis of the parameters. Changes in canopy temperature provided confident predictions for the reductions of daily evapotranspiration at the onset of drought. Empirically combining thermal and fluorescence measurements improved predictions (R 2 = 0.81) of midday leaf water potential compared to univariate models. Our results represent an important step toward quantifying plant water status during drought using first principles that do not require species-specific information.

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“…The sensors with medium and high spatial resolution differ in terms of the number of bands, temporal resolution, scale, and costs. Depending on the classification objective, the scale is a factor to consider, because, when choosing a sensor with a high spatial resolution (<5 m), the cost and complexity of the classification can increase [16]. In addition, with a larger set of data with spectral variability for the same class, the training time can affect the computational cost [17].…”

Section: The Potential Of Image Classification and The Sentinel-2 Satmentioning

confidence: 99%

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

Isbaex¹,

Coelho²

2021

Forest Biomass - From Trees to Energy

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Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

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Remote sensing for vegetation monitoring in carbon capture storage regions: A review

Cited by 68 publications

References 58 publications

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Biophysically Informed Imaging Acquisition of Plant Water Status

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

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