**1. Introduction**

Carbon Dioxide Removal (CDR) comprises anthropogenic activities that remove CO2 from the atmosphere and store it in a durable way in geological, oceanic and terrestrial reservoirs or in products. Such activities include existing and potential anthropogenic enhancement of biological or geochemical sinks and direct air capture and storage, but they exclude natural absorption of CO2 not directly caused by human activities. The latest report from Working Group III (WG III) of the

Intergovernmental Panel on Climate Change (IPCC) includes CDR to offset hardto-abate residual emissions [1]. In the European Union (EU), the European Climate Law commits the Union to reach climate neutrality by 2050. Both greenhouse gas (GHG) emission reductions and CDR will be needed to achieve the objective of climate neutrality by 2050. The European Green Deal includes rules on certifying carbon removals to expand sustainable carbon removals and encourage the use of innovative solutions to capture, recycle and store CO2 by farmers, foresters and industries [2]. Carbon removal certification is proposed as a potential preamble to establishing a carbon trading system for land sector removals, as from 2030 [3]. In the USA, Assembly Bill A8597NYS Enacts the carbon dioxide removal leadership act. § 76–0103, establishing a market for certified CDR of a minimum of 0.1 M tCO2e in 2025 and which can reach up to 60 M tCO2e/year, at a maximum price of US\$ 350/ tCO2e [4]. Industrial tropical hardwood timber is a clear example of a high-quality CDR, coming from forestry, one of the best available Nature Based Solutions NBS.

In the next four decades, population is expected to grow by 40%, the world economy by a factor of 3, and agricultural production by 60–100%. The capacity of land to produce biomass is one critical limiting resource, and humans can influence through inputs and management, resulting on large Net Primary Production (NPP). Biomass provides humans with food, fiber, and fuel, and generates the terrestrial carbon sink that helps to mitigate climate change. The total Human Appropriation of Net Primary Production (HANPP) grew from 13% in 1910 to 25% in 2005. Global biomass harvest (HANPPharv) and consumption of biomass products have risen in almost perfect correlation with global population growth.1 Over the twentieth century, human induced land use productivity was the main anthropogenic action responsible for reducing global HANPP from 2.1 to 1.6 t. Thus, less production land may be needed to supply 1 ton of biomass for human consumption. By increasing yields over the last 50 years, farmers brought cropland closer to replicating the productivity of native vegetation.2 In cultivated pastures, in 1961 there were 29 tons of grazed biomass (dry matter) used for the production of 1 ton of animal products; by 2005, this ratio was down to 17. Asia, Africa, and Latin America experienced3 the expansion of agriculture and cropland yield gains4 , increased from 41–69%5 ([5] By contrast, with cropland and cultivated pastures, land use efficiency of natural pasture and native forest did not increase, with a HANPPluc = zero for woodland and for non-degraded natural grasslands. Although the importance of rising yields has been well known, HANPP

2 Which meant that HANPPluc decreased.

1 "HANPP" is the total carbon produced annually by plant growth. Total HANPP, measured in units of carbon, is the sum of two subcategories: HANPPluc and HANPPharv. HANPPharv is the quantity of carbon in biomass harvested or otherwise consumed by people, including crops, timber, harvested crop residues, forest slash, forages consumed by livestock, and biomass lost to human-induced fires. HANPPluc is the change in NPP, also measured as annual carbon flow, as a result of human-induced land use change. The calculation of HANPPluc requires the estimation of the NPP that would be generated by the potential natural vegetation if vegetation were left unaltered—NPPpot. From NPPpot, we can also calculate HANPP as a percentage of the potential productivity. Global HANPP measured in GtC/y grew by 116% and by 2005 reached 14.8 GtC/y. As a percentage of the potential plant growth of native vegetation (NPPpot).

3 Very high growth rates in HANPP; as a percentage, HANPP doubled or even tripled in these regions during the last century.

4 Measured in HANPPharv on cropland as a ratio of NPPpot.

5 That increase, spread-out over-all cropland in 2005, generated 2.5 GtC/y of crops, which met 49% of the total increase in human consumption from 1910.

provides a useful measure of these efficiency gains because it equates all crops based on their carbon content, relates it to the productivity in global land ecosystems and, hence, demonstrates the magnitude of human-induced changes to the global carbon cycle [5].

Human induced land use improves HANPP also in the tropics. At the tropics, there is large availability of hardwoods. Tropical hardwoods are more durable, rot and marine animals resistant, stronger and cheaper than overall global hardwoods [1], which makes them standout when competing on the global markets. They have characteristic longer lifespans among timber species making them very attractable to consumers wishing lumber with special qualities such as durable, colorful, fragrant wood at their homes, offices and industries. When it comes to forestry and the tropics, the role of timber in removing atmospheric CO2 and their transformation into industrial and energy wood might offer opportunity in accounting emission removals in tropical countries. An acknowledgement of such roles might influence national policies and decision making towards including CDR as part of goals to reach carbon neutrality and in accounting NDC (National Determined Contributions).

The consumption of tropical timber products has potentials in reducing the overall carbon footprint of construction globally, and the supply of woods with long lifespan will enhance the stocks of carbon in society or urban settlements. Tropical forest productivity is directly linked to applied silvicultural practices. In managed forests, forest biological processes react to silvicultural treatments that determine the shortand long-term productivity and stock increase or decrease. Replacing natural regeneration by human induced silviculture practices increases standing stocks and the positive effects of contemporary silvicultural techniques is improvement in harvesting volumes. Globally, about ¾ of forest plantation are established using country's native species [6]. Increasing productivity is a way to remove atmospheric CO2 and transform it into industrial and energy wood. Both processes can be certified as CDR. This tropical industrial and energy hardwoods certified as CDRs can contribute to reduce emissions at the consumers level. Tropical wood CDRs are goods which include potential carbon credits that can, therefore, be used by consumers to reduce their overall negative GHG balance due to consumption rates and value chains.

In this study, we address the uncertainties and challenges of GHG accounting and monitoring in the forestry sector by jointly reviewing different components that may contribute to effective GHG assessment in tropical forests context, especially with consideration of local needs and spatial dynamics of land use activities, vegetation and forest transitions fueled by climate change and increasing atmospheric CO2 stock, and aligning sustainable forest management models to both ecosystem enhancement and economic opportunities of CDR in NDCs.


*2.1. Participants*

Five adult participants aged between 21 and 35 (all females) took part in this experiment on a voluntary basis and gave informed consent prior to the beginning of the experiments. They had normal or corrected-to-normal vision and had no history of neurological diseases. This study was conducted in accordance with the principles of the Declaration of Helsinki and the "Comitato Etico San Ra ffaele".

#### *2.2. Stimuli and Tasks*

Observers were seated 57 cm in front of a laptop screen. Visual stimuli (Figure 1) consisted of (i) a video clip (1280 × 720 @ 30 Hz) depicting sea ripples on a beach that we had previously used [2]; and (ii) a random dot kinematogram (RDK) (3000 black and white dots, 67 ms lifetime, 5 deg/s maximal speed, 0% coherence). In order to modulate video speed, the frame-rate of the visual stimulus was sinusoidally modulated (frequency, 1, 2, or 4 Hz; amplitude, 10%, 30%, or 50% of the original frame-rate) by controlling in real time the video frame flipping on the graphics board (Nvidia GTX 1060, Santa Clara, CA, USA). As a result, videos acquired a pulsatory rhythm. To ensure uniform conditions in extracting cortical oscillations (see below), in each trial there were 50 speed-modulation cycles regardless of the modulation frequency. Therefore, trials lasted 50, 25, or 12.5 s, respectively, for speed modulations of 1, 2, and 4 Hz. Observers kept their gaze in central fixation and watched the videos while wearing an electroencephalographic (EEG) recording headset (18 trials: 3 frequencies × 3 amplitudes for each visual stimulus, randomly interleaved). They were asked to mentally focus on the pulsation of the visual stimulus. Overall, the session lasted about 20 min.

**Figure 1.** A snapshot of the dynamic visual stimuli used in this study. They were displayed with various sinusoidal speed modulations, which conferred to them a pulsating appearance. In the experiment, there was also a central fixation dot. RDK—random dot kinematogram.

In a second experimental session, administered on a di fferent day, observers rated in a 9-point scale the perceived strength of the pulsation (108 trials: 3 frequencies × 4 amplitudes × 9 repetitions—including catch trials with zero amplitude, i.e., no speed modulation). Observers were specifically instructed to rate the perceived intensity (strength) of sinusoidal speed modulation. Phenomenally, such rhythmic speed modulation appeared as a pulsation, periodically speeding up and slowing down the stimulus at the experimentally imposed frequency (i.e., 1, 2 or 4 Hz). Stimuli and other conditions were the same as in the first session, except that observers could respond whenever they wished. Overall, the session lasted about 30 min.


*2.3. Proposed Feature Vector Extraction Scheme*

As explained above, because the maximum Doppler frequency of human vital signs is less 1 Hz, in our design, the update time of vital sign detection is 1 sec. In a previous study [13], vital signs were analyzed every one second. Thus, in this paper, we generate a micro-Doppler image for human motion detection every second to synchronize the update time with the vital sign extraction point.

Figure 8 shows the timing diagram used to synchronize the micro-Doppler image for motion detection and the Doppler spectrum for vital sign detection. In this case, the data collection time is initially set to 8 s to generate the first frame's Doppler spectrum for vital sign detection.

using NI's LabVIEW tool For a detailed explanation of the motion feature extraction process, we define the data stored in FIFO as *Xm*(*i*, *j*), *i* = 1 ∼ *Km*, *j* = 1 ∼ *L* , where *Km* is the Doppler-bin size and *L* is the number of sub-frames. In this paper, because we set *Km* to 2048 and *L* to 10, the size of the micro-Doppler image in one frame is 2048 by 10.

To extract the first and second feature vectors, we first count the number of scattering points stored in FIFO memory every sub-frame, which is expressed as Equation (1). From *Y*(*j*), we can obtain the corresponding count value in the *j*th sub-frame.

$$Y(j) = \text{Count}[X\_m(i, j) > 0] \text{ for } i = 1 \sim K\_m \tag{1}$$

While the vital sign signals of a non-moving passenger or the Doppler signal of another moving object have a narrow distribution of the frequency spectrum, a wide Doppler spectrum can appear when a human moves on the seat of the vehicle. In this paper, to express these characteristics, we define the 'extended degree of scattering points' as the first feature, *x*1,. This is expressed by Equation (2), which indicates the average number of scattering points of all sub-frames.

*Sensors* **2020**, *20*, 6202

In addition, while the Doppler signals from the breathing of a still human are mostly maintained over time, the passenger's movements may not be continuous. Thus, the distribution of the Doppler scattering points can vary over the sub-frames. Thus, in this paper, we define these characteristics as the 'different degree of scattering points', i.e., as the second feature *x*2. Equation (3) is used to solve *x*2, which is obtained by averaging the differences between the numbers of scattering points of two consecutive sub-frames.

These two feature vectors make it possible to determine whether or not a moving object on a seat in a vehicle is human.

$$\mathbf{x}\_1 = \left\{ \sum\_{j=1}^{L} \mathbf{y}(j) \right\} / L \tag{2}$$

$$\mathbf{x}\_2 = \left\{ \sum\_{j=1}^{L-1} \left| Y(j+1) - Y(j) \right| \right\} / (L-1) \tag{3}$$

Next, we define the Doppler frequency spectrum as *Xv*(*i*), *i* = 1 ∼ *Kv* to process the results of the Doppler frequency-based vital sign feature extraction, where *Kv* is the Doppler-bin size. That is, we count the number of scattering points with values greater than reference value for noise thresholding. The reference value can be obtained as the measured noise signal in blank space.

$$Z = \text{Count}[X\_{\overline{v}}(i) > \text{reference value}] \text{ for } i = 1 \sim K\_{\overline{v}} \tag{4}$$

Using the results of Equation (4), we define *x*3 as the 'presence of vital signs', and the third feature is expressed in Equation (5). When using the feature vector *x*3, we can determine whether or not a non-moving or slightly moving object is a human.

$$\text{tr}\_3 = \begin{cases} \text{if } Z > 0, \text{ then } \text{logic}'1' \\ \text{else } \text{logic}'0' \end{cases} \tag{5}$$

The three extracted feature vectors are fed into the machine learning engine for learning and testing. In this paper, we employ a BDT (Binary Decision Tree) as the machine learning method.

The BDT is a popular and simple machine learning algorithm based on a sequential decision process because a feature is evaluated as one of two branches, which is selected starting from the root of the tree. Thus, we can easily implement the BDT in an embedded system for machine learning based on the three features proposed in this paper, using only the "if–else" syntax in real time [15].

#### **3. Measurement Results**