Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests.

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400-600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

Novel phosphate functionalized sodium alginate hydrogel for efficient adsorption and separation of Nd and Dy from Co.

The recovery of rare earth elements (REEs) including neodymium (Nd) and dysprosium (Dy) from NdFeB permanent magnets has become one of the main ways to solve the increased demand for rare earth. Herein, n-dodecyl phosphate (DPPA) was used for the first time as the adsorption functional group donor, sodium alginate as the substrate, and calcium chloride solution as the reactive solvent, a hybrid hydrogel adsorbent DPPA/CaALG was synthesized by sol-gel method for application in the adsorption and separation of Nd and Dy from the Co-Nd-Dy ternary system. SEM-EDS, and N2 adsorption-desorption analysis showed the successful preparation of DDPA/CaALG with mesoporous structure. Batch experiments showed the superiority of the hybrid hydrogel for the good selective adsorption of Nd and Dy, such as large adsorption capacity (Nd: 162.5 mg/g, Dy: 183.5 mg/g), and no adsorption for Co. FT-IR, XPS showed that PO and P-O groups are involved in the adsorption process of Nd and Dy as electron acceptors, where the ion exchange of P-OH is dominant. Furthermore, the chemical properties of ligands and complexes were analyzed by Density Functional Theory (DFT) calculations and revealed their adsorption behaviors as well as the competition between different metal ions.

Precise recognition and efficient recovery of Pd(II) from high-level liquid waste by a novel aminothiazole-functionalized silica-based adsorbent.

Efficient recognition, separation and recovery of palladium from high-level liquid waste (HLLW) not only helps the safe, green and environmentally friendly disposal of nuclear waste, but also is an essential important supplement to overcome the growing shortage of natural palladium resources. Herein, a novel silica-based functional adsorbent named 2AT-SiAaC was prepared by a two-step method, i.e., grafting of 2-aminothiazole (2AT) via the amidated reaction after in-situ polymerization of acrylic monomers on porous silica. SEM, EDS, TG-DSC, BET and PXRD all proved the successful preparation of 2AT-SiAaC, and it exhibited ultrahigh adsorption selectivity for Pd(II) (Kd (distribution coefficient) ≥ 10,344.2 mL/g, SFPd/M (separation factor) ≥ 613.7), fast adsorption kinetics with short equilibrium time (t ≤ 1 h) and good adsorption capacity (Q ≥ 62.1 mg Pd/g). The dynamic column experiments shows that 2AT-SiAaC achieved efficiently separation of Pd(II) from simulated HLLW, and the enrichment coefficients (C/C0) of Pd(II) was as high as about 14 with the recovery rate nearly 99.9% and basically kept the same performance in three adsorption-desorption column cycle experiments. The adsorption mechanism was analyzed by FT-IR, XPS and DFT calculations, and the ultrahigh selectivity of 2AT-SiAaC was attributed to the preferred affinity of the soft N-donor atoms in 2AT for Pd(II). NO3- ions participated in the adsorption reaction to keep charge balance, and the frontier orbital electron density distribution diagram shows the charge transfer in the process of material preparation and adsorption. To sum up, 2AT-SiAaC adsorbent provided a new insight for precise recognition and efficient separation of Pd(II) from HLLW.

Precise stepwise recovery of platinum group metals from high-level liquid wastes based on SDB polymer-modified SiO2.

Accurate separation and efficient recovery of platinum group metals (PGMs, mainly Ru, Rh and Pd) from high level liquid waste (HLLW) is a good choice for clean production and sustainable development of nuclear energy. Herein a novel SDB polymer modified silica-based amine-functionalized composite (dNbpy/SiO2-P) was synthesized for the separation and recovery of PGMs. Laser particle size analysis and BET results clarified the regular spherical and highly interconnected mesoporous structure of dNbpy/SiO2-P which is critical for the separation of PGMs. The removal percent of PGMs were over 99% on the optimized conditions. In addition, dNbpy/SiO2-P showed excellent selectivity (SFPd/M > 3805, SFRu/M > 1705, SFRh/M > 336) and repeatability (≥5). Interestingly, based on the different adsorption and desorption kinetics of PGMs, a double-column strategy is designed to solve the challenge of separating and recovering PGMs from HLLW. The enrichment factors of Pd(II), Ru(III) and Rh(III) reached 36.7, 8.2, and 1.2. The adsorption of PGMs was coordination mechanism and required the involvement of NO3- to maintain charge balance. The specific distribution of elements within the adsorbents and the changes in valence state were analyzed using depth-profiling XPS. Both depth-profiling XPS results and slope analysis revealed that the complex of dNbpy and PGMs is a 1 : 1 coordination structure. Overall, this work fills the gap that PGMs cannot be effectively separated and enriched from HLLW.

Control temperature of greenhouse for higher yield and higher quality grapes production by combining STB in situ service with on time sensor monitoring.

The use of plastic film to promote early cultivation is common by small farmers in Northern China for out of season facility fresh grape production, but the lack of effective technical indicators, sensors and temperature control techniques for facility temperature management has resulted in high cost and low yields. To explore effective ways of enhancing grape yield and quality through temperature monitoring and precise temperature control by sensors under the current plastic covering systems of small farmers. By providing a resident service in the Science and Technology Backyard (STB) and using intelligent sensors to monitor and manage the temperature in small farmers' facilities in real-time (on an hourly basis). We found that the phenological and effective accumulated temperature in plastic film-covered facilities was significantly different from those in open field cultivation, with a 15.9% advance in the phenological process and 19.5% reduction in effective accumulated temperature requirements, as well as a 51.4% increase in effective accumulated temperature requirements during the vegetative stage. In the case of the delay in temperature regulation of the plastic film cover system, it is necessary to control the minimum temperature and high temperature to match the effective accumulated temperature demand. By installing the Pycno temperature sensor, using units of minutes, accurately monitoring the temperature changes inside and outside the facility, and deploying smoke to prevent low temperature and open the air outlet to control the high temperature at the right time, plastic film can reduce the proportion of effective accumulated temperature distribution during the vegetative stage by 3.2% and reduce the vigorous growth of new shoots by 22.2%. The result had shown 40.2% increase in spike weight and 30.1% increase in yield. By combining real-time sensor monitoring with grape growth and development indicators, we have quantified the difference in effective temperature requirements between the vegetative and reproductive growth periods of grapes in current smallholder plastic cover systems and open field cultivation systems. By combining sensor monitoring and technical services in a precise manner, the production of grapes in facilities under smallholder plastic cover systems can also achieve smart agriculture and gain yield and quality improvements.

Influence of climatic and geographic factors on the spatial distribution of Qinghai spruce forests in the dryland Qilian Mountains of Northwest China.

The effect of climate variables (temperature and precipitation) on forest spatial distribution is more prominent in dryland high mountains, where forest distribution is inherently very sensitive to and strongly limited by the substantial spatial heterogeneity of site conditions. Thus, a more reliable prediction of forest distribution under changing environment depends upon an understanding of the joint influence of climatic and topographic factors and their thresholds. This study was conducted on Qinghai spruce forests as dominant tree species in the Qilian Mountains of northwest China. The spruce forest distribution was surveyed by remote sensing in Dayekou watershed and by field investigation in a nested smaller watershed. Analyses showed that mean annual air temperature and precipitation, which vary with elevation, are the key climatic factors determining forest distribution, but slope aspect also plays an essential role. The potential core distribution area of denser forests and potential distribution area including sparse forests are between the axes of elevation (2635.5-3302.5 and 2603.4-3325.8m a.s.l.) and slope aspect (-74.4-61.2° and -162.6-147.1° deviated from north). The corresponding threshold of mean annual air temperature at the upper elevation boundary is -2.59 and -2.73°C, while the threshold of mean annual precipitation at the lower elevation boundary is 378.1 and 372.3 mm, respectively. Using these thresholds and the elevation gradients of climatic factors, the shifting of elevation boundaries under climate change scenarios can be predicted. However, the forest distribution is also limited by a soil thickness of ≥40cm; and by slope position of lower-, lower- and middle-, and entire-slope within the elevation ranges of <2800, 2800-2900, and >2900m a.s.l., respectively. This study showed that adding geographic factors will greatly improve the prediction of changes in forest distribution area in dryland mountains, in addition to the influence of climatic factors.

Preparation of black-pearl reduced graphene oxide-sodium alginate hydrogel microspheres for adsorbing organic pollutants.

The black-pearl reduced graphene oxide-sodium alginate (rGO-SA) hydrogel microspheres are prepared by the external emulsification and thermal reduction method, which are characterized by scanning electron microscope (SEM) and X-ray Diffraction (XRD). Sodium alginate (SA) serves as a template to form a 3D porous network structure, which can prevent the agglomeration and restacking of rGO sheets efficiently. The size of hydrogel microsphere can be controlled by adjusting the size of the liquid drop. The effects of rGO content (wt%), contact time, initial concentration of phenol, adsorption temperature and adsorption dose on the adsorption capacity of rGO-SA microspheres are investigated. The kinetics and isotherm data are well described by the pseudo-second-order kinetic model and the Langmuir equation, respectively. Thermodynamic results demonstrate the spontaneous and endothermic nature of adsorption. This rGO-SA microsphere exhibits the favorable adsorption performance for phenol, BPA and tetracycline. The rGO-SA microsphere might be a potential candidate for efficient adsorbents in water treatment.

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE).

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.

Estimation for sparse vegetation information in desertification region based on Tiangong-1 hyperspectral image.

In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared (NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. R2 of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.

[Analysis of sensitive spectral bands for burning status detection using hyper-spectral images of Tiangong-01].

To obtain the sensitive spectral bands for detection of information on 4 kinds of burning status, i. e. flaming, smoldering, smoke, and fire scar, with satellite data, analysis was conducted to identify suitable satellite spectral bands for detection of information on these 4 kinds of burning status by using hyper-spectrum images of Tiangong-01 (TG-01) and employing a method combining statistics and spectral analysis. The results show that: in the hyper-spectral images of TG-01, the spectral bands differ obviously for detection of these 4 kinds of burning status; in all hyper-spectral short-wave infrared channels, the reflectance of flaming is higher than that of all other 3 kinds of burning status, and the reflectance of smoke is the lowest; the reflectance of smoke is higher than that of all other 3 kinds of burning status in the channels corresponding to hyper-spectral visible near-infrared and panchromatic sensors. For spectral band selection, more suitable spectral bands for flaming detection are 1 000.0-1 956.0 and 2 020.0-2 400.0 nm; the suitable spectral bands for identifying smoldering are 930.0-1 000.0 and 1 084.0-2 400.0 nm; the suitable spectral bands for smoke detection is in 400.0-920.0 nm; for fire scar detection, it is suitable to select bands with central wavelengths of 900.0-930.0 and 1 300.0-2 400.0 nm, and then to combine them to construct a detection model.

[The changes of forest canopy spectral reflectance with seasons in Xiaoxing'anling].

The ASD FieldSpec portable spectrometer was adopted to collect canopy reflectance spectrum data of the 9 main tree species in study area by a long-term observation to get the data of the four seasons Then the smoothed reflectance curve and the first derivation curve from 350 to 1400 nm and several commonly used vegetation spectral characteristic parameters were generated to analyse seasonal change characteristics and variation of the 9 tree species in visible and near-infrared band and to explore the best band characteristics and period for species identification. The results showed that different trees had different and rather unique spectral features during the four seasons. The spectral characteristics of the deciduous trees have regular changes with the cycle of the seasons, whereas those of the evergreen tree species have no significant changes in one year. As well changes in the spectral characteristics could effectively reflect forest phenology changes, and it is proposed that the optimal strategy for tree species classification may be the integration and analysis of multi-seasonal spectral data. Evergreen trees and deciduous trees in the winter have obvious differences in the canopy spectral characteristics and the best single-season remote sensing data for tree species recognition is in summer.

[Estimation of sparse vegetation coverage in arid region based on hyperspectral mixed pixel decomposition].

Based on Hyperion hyperspectral image data, the image-derived shifting sand, false-Gobi spectra, and field-measured sparse vegetation spectra were taken as endmembers, and the sparse vegetation coverage (< 40%) in Minqin oasis-desert transitional zone of Gansu Province was estimated by using fully constrained linear spectral mixture model (LSMM) and non-constrained LSMM, respectively. The results showed that the sparse vegetation fraction based on fully constrained LSMM described the actual sparse vegetation distribution. The differences between sparse vegetation fraction and field-measured vegetation coverage were less than 5% for all samples, and the RMSE was 3.0681. However, the sparse vegetation fraction based on non-constrained LSMM was lower than the field-measured vegetation coverage obviously, and the correlation between them was poor, with a low R2 of 0.5855. Compared with McGwire's corresponding research, the sparse vegetation coverage estimation in this study was more accurate and reliable, having expansive prospect for application in the future.

Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data.

Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data.