Geologic factors leadingly drawing the macroecological pattern of rocky desertification in southwest China.

Rocky desertification (RD) is a special process of land deterioration in karst topography, with a view of bedrock exposure and an effect of ecological degradation. Among the three largest karst regions in the world, southwest China boasts the largest RD area and highest diversity of karst landscapes. However, inefficient field surveying tends to restrict earlier studies of RD to local areas, and the high complexity of karst geomorphology in southwest China further lead to the shortage of the knowledge about its macroecological pattern so far. To address this gap, this study innovatively took county as the unit to statistically explore the links between the 2008-censused distributions of county-level RD in southwest China and its potential impact factors of three kinds (geologic, climatic, and anthropogenic), all transformed into the same mapping frame. Spatial pattern analyses based on spatial statistics and artificial interpretation unveiled the macroscopic characteristics of RD spatial patterns, and attribution analyses based on correlation analysis and dominance analysis exposed the links of the impact factors to RD and their contributions in deciding the macroscopic pattern of RD. The results suggested that geologic factors play a first role in drawing the macroecological pattern of RD, also for the slight-, moderate-, and severe-level RD scenarios, in southwest China. Despite this inference somehow collides with the popular awareness that anthropogenic factors like human activities are leadingly responsible for the RD-relevant losses, the findings are of practical implications in guiding making the macroscopic policies for mitigating RD degradation and advancing its environmental restoration.

Ecological policy benefit valuation based on public feedback: Forest ecosystem services in Wuyishan nature reserve, China.

The spontaneous expansion of tea cultivation has led to the degradation of forest ecosystem services in the Wuyishan national nature reserve (WNNR). In 2008, the local government put forward the policy of "returning tea to forests" (RTTF) to protect the forest ecosystem. However, in order to measure its effects over the past ten years, it is necessary to accurately quantify the economic benefits of this ecological policy. This study tracked the land use changes in WNNR during the last 17 years and estimated the ecosystem service value caused by the RTTF policy. We used virtual market methods to convert different types of public feedback into a unified monetary value, and estimated the economic benefits of RTTF by combining the land use changes. Results showed that the added value of forest ecosystem services not only compensated for the loss of tea profits, but also brought about remarkable economic benefits (approximately US$140 million). Through the combination of ecological changes and economic benefits, we proposed a future direction of the RTTF policy adjustment. More broadly, we provided a method to quantify economic effects (or economic losses) from the perspective of public feedback on the basis of ecological changes. This attempt has contributed to the solving of econometric problems related to ecological policy by combining bioinformatics with ecological economics.

Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data.

The upcoming space-borne LiDAR satellite Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in 2018. Different from the waveform LiDAR system onboard the ICESat, ICESat-2 will use a micro-pulse photon-counting LiDAR system. Thus new data processing algorithms are required to retrieve vegetation canopy height from photon-counting LiDAR data. The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height. The new proposed method consists of three key steps: 1) filtering out the noise photons by an effective noise removal algorithm based on localized statistical analysis; 2) separating ground returns from canopy returns using an iterative photon classification algorithm, and then determining ground surface; 3) generating canopy-top surface and calculating vegetation canopy height based on canopy-top and ground surfaces. This automatic vegetation height estimation approach was tested to the simulated ICESat-2 data produced from Sigma Space LiDAR data and Multiple Altimeter Beam Experimental LiDAR (MABEL) data, and the retrieved vegetation canopy heights were validated by canopy height models (CHMs) derived from airborne discrete-return LiDAR data. Results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data (R2 and RMSE values ranging from 0.639 to 0.810 and 4.08 m to 4.56 m respectively). This means our new proposed approach is appropriate for retrieving vegetation canopy height from micro-pulse photon-counting LiDAR data.

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

The Influences of Drought and Land-Cover Conversion on Inter-Annual Variation of NPP in the Three-North Shelterbelt Program Zone of China Based on MODIS Data.

Terrestrial ecosystems greatly contribute to carbon (C) emission reduction targets through photosynthetic C uptake.Net primary production (NPP) represents the amount of atmospheric C fixed by plants and accumulated as biomass. The Three-North Shelterbelt Program (TNSP) zone accounts for more than 40% of China's landmass. This zone has been the scene of several large-scale ecological restoration efforts since the late 1990s, and has witnessed significant changes in climate and human activities.Assessing the relative roles of different causal factors on NPP variability in TNSP zone is very important for establishing reasonable local policies to realize the emission reduction targets for central government. In this study, we examined the relative roles of drought and land cover conversion(LCC) on inter-annual changes of TNSP zone for 2001-2010. We applied integrated correlation and decomposition analyses to a Standardized Evapotranspiration Index (SPEI) and MODIS land cover dataset. Our results show that the 10-year average NPP within this region was about 420 Tg C. We found that about 60% of total annual NPP over the study area was significantly correlated with SPEI (p<0.05). The LCC-NPP relationship, which is especially evident for forests in the south-central area, indicates that ecological programs have a positive impact on C sequestration in the TNSP zone. Decomposition analysis generally indicated that the contributions of LCC, drought, and other Natural or Anthropogenic activities (ONA) to changes in NPP generally had a consistent distribution pattern for consecutive years. Drought and ONA contributed about 74% and 23% to the total changes in NPP, respectively, and the remaining 3% was attributed to LCC. Our results highlight the importance of rainfall supply on NPP variability in the TNSP zone.

Estimating FPAR of maize canopy using airborne discrete-return LiDAR data.

The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize.