Spatial Expansion and Soil Organic Carbon Storage Changes of Croplands in the Sanjiang Plain, China

Inversion of soil organic carbon content based on the two-point machine learning method

Soil organic carbon (SOC) is vital for the global carbon cycle and environmentally sustainable development. Meanwhile, the fast, convenient remote sensing technology has become one of the notable means to monitor SOC content. Nowadays, limitations are found in the inversion of SOC content with high-precision and complex spatial relationships based on scarce ground sample points. It is restrained by the spatial difference in the relationship between SOC content and remote sensing spectra due to the problem of different spectra for the same substance and the influence of topographic and environment (e.g. vegetation and climate). In this regard, the two-point machine learning (TPML) method, which can overcome above problems and deal with complex spatial heterogeneity of relationships between SOC and remote sensing spectra, is used to invert the SOC content in Hailun County, Heilongjiang Province, combined with derived variables from Sentinel-1, Sentinel-2, topography and environment. Based on 10-fold cross-validation and t-test, results indicate that the TPML method boasts the highest inversion accuracy, followed by random forest, gradient boosting regression tree, partial least squares regression and support vector machine. The average r, MAE, RMSE, and RPD of TPML are 0.854, 0.384 %, 0.558 %, and 1.918. Further, the TPML method has been proven to be equal to evaluating the uncertainty of inversion results, by comparing the actual and theoretical error of the inversion result in one subset. The spatial inversion result of SOC content with 10 m resolution by TPML is smoother and has more real details than other models, which are consistent with the distribution of SOC content in different land use types. This study provides both theoretical and technical guidance for using TPML method combined with spectral information of remote sensing to predict soil attributes and offer accurate uncertainty estimation, thereby opening up the opportunity for low-cost, high-precision, and large-scale SOC inversion.

Using Multisource Geospatial Data to Identify Potential Wetland Rehabilitation Areas: A Pilot Study in China’s Sanjiang Plain

Wetland rehabilitation, highlighted in the United Nations (UN) Sustainable Development Goals (SDGs), is imperative for responding to decreased regional biodiversity and degraded ecosystem functions and services. Knowing where the most suitable wetland rehabilitation areas are can strengthen scientific planning and decision-making for natural wetland conservation and management implementation. Therefore, we integrated multisource geospatial data characterizing hydrological, topographical, management, and policy factors, including maximum surface water coverage, farming time, anthropogenic disturbance, and wetland protection level, to identify potential wetland rehabilitation areas in the Sanjiang Plain (SJP), the largest marsh distribution and a hotspot wetland loss region in China. Our results indicate that a total of 11,643 km2 of wetlands were converted into croplands for agricultural production from 1990 to 2018. We estimated that 5415 km2 of the croplands were suitable for wetland rehabilitation in the SJP, of which 4193 km2 (77%) have high rehabilitation priority. Specifically, 63% of the potential areas available for wetland rehabilitation are dry croplands (3419 km2), the rest (37%) being paddy fields. We argue that the selected indicators and approach used in this study to determine potential wetland rehabilitation areas could guide their investigation, at either the provincial or national scale and would be beneficial to conservation and sustainable management of wetlands in the SJP.

What did China's National Wetland Conservation Program Achieve？Observations of changes in land cover and ecosystem services in the Sanjiang Plain

China implemented the National Wetland Conservation Program (NWCP) from 2002 to protect and rehabilitate wetlands. Under the background of sustainable development, assessment on the effectiveness of the NWCP is important to ecosystem management, especially in the Sanjiang Plain, the largest marsh distribution area and hotspot area with wetland loss. To achieve this aim, this study examined the changes in land cover and ecosystem services (ESs) from 1990 to 2000 and from 2000 to 2015 in the Sanjiang Plain as well as the nine national nature reserves for wetlands (NNRWs) by means of Landsat series images and the InVEST model. Results reveal that the NWCP played critical roles in reducing wetland loss and improving regional ESs. The shrinkage rate of wetlands in the Sanjiang Plain has been decreased remarkably, with a declined rate of wetland loss from 750 km² yr^-1 to 189 km² yr^-1. The reduction rate of habitat area in good suitable grade and ecosystem carbon stock declined notably during the period 2000-2015 compared to the period 1990-2000. The amount of water retention increased by 5.4%, while the grain production capacity was enhanced by nine times from 1990 to 2015. Specifically, since 2000, the reduction rate of wetland area in NNRWs (33 km² yr^-1) was obviously lower than that in the entire Sanjiang Plain, whilst various ESs in NNRWs were better than that in the whole Sanjiang Plain. This study is expected to provide an example for evaluating the effectiveness of the NWCP at other regions and support regional wetland conservation management.

Investigating Spatial and Vertical Patterns of Wetland Soil Organic Carbon Concentrations in China’s Western Songnen Plain by Comparing Different Algorithms

Investigating the spatial and vertical patterns of wetland soil organic carbon concentration (SOCc) is important for understanding the regional carbon cycle and managing the wetland ecosystem. By integrating 160 wetland soil profile samples and environmental variables from climatic, topographical, and remote sensing data, we spatially predicted the SOCc of wetlands in China’s Western Songnen Plain by comparing four algorithms: random forest (RF), support vector machine (SVM) for regression, inverse distance weighted (IDW), and ordinary kriging (OK). The predicted results of the SOCc from the different algorithms were validated against independent testing samples according to the mean error, root mean squared error, and correlation coefficient. The results show that the measured SOCc values at depths of 0–30, 30–60, and 60–100 cm were 15.28, 7.57, and 5.22 g·kg−1, respectively. An assessment revealed that the RF algorithm was most accurate for predicting SOCc; its correlation coefficients at the different depths were 0.82, 0.59, and 0.51, respectively. The attribute importance from the RF indicates that environmental variables have various effects on the SOCc at different depths. The land surface temperature and land surface water index had a stronger influence on the spatial distribution of SOCc at the depths of 0–30 and 30–60 cm, whereas topographic factors, such as altitude, had a stronger influence within 60–100 cm. The predicted SOCc of each vertical depth increased gradually from south to north in the study area. This research provides an important case study for predicting SOCc, including selecting factors and algorithms, and helps with understanding the carbon cycles of regional wetlands.

Monitoring and Assessment of Wetland Loss and Fragmentation in the Cross-Boundary Protected Area: A Case Study of Wusuli River Basin

Comparative evaluation of cross-boundary wetland protected areas is essential to underpin knowledge-based bilateral conservation policies and funding decisions by governments and managers. In this paper, wetland change monitoring for the Wusuli River Basin in the cross-boundary zone of China and Russia from 1990 to 2015 was quantitatively analyzed using Landsat images. The spatial-temporal distribution of wetlands was identified using a rule-based object-oriented classification method. Wetland dynamics were determined by combining annual land change area (ALCA), annual land change rate (ALCR), landscape metrics and spatial analysis in a geographic information system (GIS). A Mann–Kendall test was used to evaluate changing climate trends. Results showed that natural wetlands in the Wusuli River Basin have declined by 5625.76 km2 in the past 25 years, especially swamp/marsh, which decreased by 26.88%. Specifically, natural wetlands declined by 49.93% in the Chinese section but increased with an ALCA of 16.62 km2/y in the Russian section during 1990–2015. Agricultural encroachment was the most important reason for the loss and degradation of natural wetlands in the Wusuli River Basin, especially in China. Different population change trends and conservation policies in China and Russia affected natural wetland dynamics. The research offers an efficient and effective method to evaluate cross-boundary wetland change. This study provides important scientific information necessary for developing future ecological conservation and management of cross-boundary wetlands.

Conversions between natural wetlands and farmland in China: A multiscale geospatial analysis

Agricultural activity is widely recognized as a leading driver of natural wetland loss in many parts of the world. However, little is known about the spatiotemporal patterns of conversion between natural wetlands and farmland in China. This information deficiency has limited decision-making for the sustainable management of natural wetland ecosystems. In this study, we explicitly quantified bidirectional natural wetland-farmland conversions during the periods of 1990-2000 and 2000-2010 at multiple spatiotemporal scales. Our results revealed that about 60% (15,765km²) of China's lost natural wetlands were due to agricultural encroachment for grain production, 74.7% (11,778km²) of which occurred from 1990 to 2000. Natural wetland conversion to farmland was highest in Northeast China (13,467km² or 85.4%), whereas the natural wetlands in Northwest China demand extra attention because of a notable increase of agricultural encroachment. Natural wetlands in the humid zone experienced tremendous agricultural encroachment, leading to a loss of 10,649km², accounting for 67.5% of the total agriculture-induced natural wetland loss in China. On the other hand, a total of 1369km² of natural wetlands were restored from farmland, with 66.3% of this restoration occurring between 2000 and 2010, primarily in Northeast China and the humid zone. Although a series of national policies and population pressure resulted in agricultural encroachment into natural wetlands, there are also policies and management measures protecting and restoring natural wetlands in China. The spatial differences in natural wetland-farmland conversions among different geographic regions and climatic zones suggest that China must develop place-based sustainable management policies and plans for natural wetlands. This study provides important scientific information necessary for developing such policies and implementation plans.

Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region

Wetlands in the mid- and high-latitudes are particularly vulnerable to environmental changes and have declined dramatically in recent decades. Climate change and human activities are arguably the most important factors driving wetland distribution changes which will have important implications for wetland ecological functions and services. We analyzed the importance of driving variables for wetland distribution and investigated the relative importance of climatic factors and human activity factors in driving historical wetland distribution changes. We predicted wetland distribution changes under climate change and human activities over the 21st century using the Random Forest model in a mid- and high-latitude region of Northeast China. Climate change scenarios included three Representative Concentration Pathways (RCPs) based on five general circulation models (GCMs) downloaded from the Coupled Model Intercomparison Project, Phase 5 (CMIP5). The three scenarios (RCP 2.6, RCP 4.5, and RCP 8.5) predicted radiative forcing to peak at 2.6, 4.5, and 8.5 W/m2 by the 2100s, respectively. Our results showed that the variables with high importance scores were agricultural population proportion, warmness index, distance to water body, coldness index, and annual mean precipitation; climatic variables were given higher importance scores than human activity variables on average. Average predicted wetland area among three emission scenarios were 340,000 ha, 123,000 ha, and 113,000 ha for the 2040s, 2070s, and 2100s, respectively. Average change percent in predicted wetland area among three periods was greatest under the RCP 8.5 emission scenario followed by RCP 4.5 and RCP 2.6 emission scenarios, which were 78%, 64%, and 55%, respectively. Losses in predicted wetland distribution were generally around agricultural lands and expanded continually from the north to the whole region over time, while the gains were mostly associated with grasslands and water in the most southern region. In conclusion, climatic factors had larger effects than human activity factors on historical wetland distribution changes and wetland distributions were predicted to decline remarkably over time under climate change scenarios. Our findings have important implications for wetland resource management and restoration because predictions of future wetland changes are needed for wetlands management planning.