Restoring disturbed and over-exploited ecosystems is important to mitigate human pressures on natural ecosystems. China has launched an ambitious national ecosystem restoration program called Grain ...to Green Program (GTGP) over the last decade. By using remote sensing techniques and ecosystem modelling, we quantitatively evaluated the changes in ecosystem carbon sequestration since China's GTGP program during period of 2000-2008. It was found the NPP and NEP in this region had steadily increased after the initiative of the GTGP program, and a total of 96.1 Tg of additional carbon had been sequestered during that period. Changes in soil carbon storage were lagged behind and thus insignificant over the period, but was expected to follow in the coming decades. As a result, the Loess Plateau ecosystem had shifted from a net carbon source in 2000 to a net carbon sink in 2008. The carbon sequestration efficiency was constrained by precipitation, and appropriate choices of restoration types (trees, shrubs, and grasses) in accordance to local climate are critical for achieving the best benefit/cost efficiency.
Assessment of soil loss and understanding its major drivers are essential to implement targeted management interventions. We have proposed and developed a Revised Universal Soil Loss Equation ...framework fully implemented in the Google Earth Engine cloud platform (RUSLE-GEE) for high spatial resolution (90 m) soil erosion assessment. Using RUSLE-GEE, we analyzed the soil loss rate for different erosion levels, land cover types, and slopes in the Blue Nile Basin. The results showed that the mean soil loss rate is 39.73, 57.98, and 6.40 t ha−1 yr−1 for the entire Blue Nile, Upper Blue Nile, and Lower Blue Nile Basins, respectively. Our results also indicated that soil protection measures should be implemented in approximately 27% of the Blue Nile Basin, as these areas face a moderate to high risk of erosion (>10 t ha−1 yr−1). In addition, downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 km to 1 km spatial resolution significantly impacts rainfall erosivity and soil loss rate. In terms of soil erosion assessment, the study showed the rapid characterization of soil loss rates that could be used to prioritize erosion mitigation plans to support sustainable land resources and tackle land degradation in the Blue Nile Basin.
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•We proposed a framework to map and assess soil erosion by water in the GEE environment.•RUSLE model was adopted and validated against previous findings in the Blue Nile Basin.•The impact of downscaling precipitation on the R-factor and soil loss rate was studied.•Areas of moderate to very severe susceptibility to water erosion cover 27%.•Downscaling precipitation significantly affects R-factor and soil loss rate.
In response to ecosystem degradation from rapid economic development, China began investing heavily in protecting and restoring natural capital starting in 2000. We report on China's first national ...ecosystem assessment (2000–2010), designed to quantify and help manage change in ecosystem services, including food production, carbon sequestration, soil retention, sandstorm prevention, water retention, flood mitigation, and provision of habitat for biodiversity. Overall, ecosystem services improved from 2000 to 2010, apart from habitat provision. China's national conservation policies contributed significantly to the increases in those ecosystem services.
The spatial distribution of water resources largely influences Earth ecosystems and human civilization. Being a major component of the global water cycle, evapotranspiration (ET) serves as an ...indicator of the availability of water resources. Understanding the actual ET (ETa) variation mechanism at different spatial and temporal scales can improve management of water use within the sustainable development limits. In this study, remote sensing derived ETa data were used to study water resource fluctuations in the Loess Plateau, China. This region covers diverse climate types from humid to arid and experienced large changes in vegetation cover during a revegetation project between 2000 and 2015. The relations between spatiotemporal variation of ETa, climate factors and vegetation change were explored using statistical methods. The results show that cropland, forestland and grassland take the largest percentage of total ETa. Total ETa exhibited a marginally increasing trend (p < 0.1) during 2000–2010 and no trend during 2011–2015. Windspeed and vegetation cover index highly influenced ETa, followed by atmospheric pressure, air humidity, precipitation, bright sunshine duration and temperature. Temperature has little effect on ETa throughout the Loess Plateau. The monitoring of water resources based upon water balance between precipitation, ETa and river flow changes shows that water consumption deficit is consistent with vegetation changes: it was large during 2000–2010 when vegetation increased rapidly and decreased after 2010. These results could help to develop different water saving strategies across the Loess Plateau and build a better monitoring system of water resources.
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•Actual evapotranspiration in the Loess Plateau is quantified and validated.•Windspeed is the dominant influential climate factor to actual evapotranspiration in research area.•Evapotranspiration variation is consistent with vegetation coverage change in the Loess Plateau.•Water deficit of the Loess Plateau fluctuated during revegetation period (2000–2015).
As one of the key tools for regulating human-ecosystem relations, environmental conservation policies can promote ecological rehabilitation across a variety of spatiotemporal scales. However, ...quantifying the ecological effects of such policies at the regional level is difficult. A case study was conducted at the regional level in the ecologically vulnerable region of the Loess Plateau, China, through the use of several methods including the Universal Soil Loss Equation (USLE), hydrological modeling and multivariate analysis. An assessment of the changes over the period of 2000–2008 in four key ecosystem services was undertaken to determine the effects of the Chinese government's ecological rehabilitation initiatives implemented in 1999. These ecosystem services included water regulation, soil conservation, carbon sequestration and grain production. Significant conversions of farmland to woodland and grassland were found to have resulted in enhanced soil conservation and carbon sequestration, but decreased regional water yield under a warming and drying climate trend. The total grain production increased in spite of a significant decline in farmland acreage. These trends have been attributed to the strong socioeconomic incentives embedded in the ecological rehabilitation policy. Although some positive policy results have been achieved over the last decade, large uncertainty remains regarding long-term policy effects on the sustainability of ecological rehabilitation performance and ecosystem service enhancement. To reduce such uncertainty, this study calls for an adaptive management approach to regional ecological rehabilitation policy to be adopted, with a focus on the dynamic interactions between people and their environments in a changing world.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In situ ground truth data are an important requirement for producing accurate cropland type map, and this is precisely what is lacking at vast scales. Although volunteered geographic information ...(VGI) has been proven as a possible solution for in situ data acquisition, processing and extracting valuable information from millions of pictures remains challenging. This paper targets the detection of specific crop types from crowdsourced road view photos. A first large, public, multiclass road view crop photo dataset named iCrop was established for the development of crop type detection with deep learning. Five state-of-the-art deep convolutional neural networks including InceptionV4, DenseNet121, ResNet50, MobileNetV2, and ShuffleNetV2 were employed to compare the baseline performance. ResNet50 outperformed the others according to the overall accuracy (87.9%), and ShuffleNetV2 outperformed the others according to the efficiency (13 FPS). The decision fusion schemes major voting was used to further improve crop identification accuracy. The results clearly demonstrate the superior accuracy of the proposed decision fusion over the other non-fusion-based methods in crop type detection of imbalanced road view photos dataset. The voting method achieved higher mean accuracy (90.6-91.1%) and can be leveraged to classify crop type in crowdsourced road view photos.
•ca showed major contribution to tree growth but inter site variations are observed.•Different tree species demonstrated diverse responses to elevated ca.•Quadratic relationship between tree growth ...and water use efficiency is identified.•Intensified water stress due to climate change decreased tree growth rate recently.
Understanding the responses of local forests to the gradually rising atmospheric CO2 concentrations (ca) and changing environment is critical for appropriate management activities. This work used tree ring width measures (i.e. basal areal increment, BAI) and carbon (C) and nitrogen (N) signals to explore the intrinsic water use efficiency (iWUE) and tree growth dynamics of three major tree species (Pinus massoniana, P.tabuliformis and Larix gmelinii) in the Miyun Reservae Basin (MRB) of Beijing. The results indicate that ca was a primary contributor to tree growths, especially at the remote site where rising ca accounted for 92% and 74% of BAI changes for P. tabuliformis and L. gmelinii, respectively. N deposition was found to have a positive effect on BAI at this site. The controlling effect of ca on tree growth at the close-to-city site was smaller (52% and 44% of the contributions for P. tabuliformis and P. massoniana, respectively), while the negative influences of N deposition on BAI tends to be intensified. iWUE showed consistent increase during the entire growth period at all sites. Quadratic relationships between iWUE and BAI were observed, which indicated that the rising ca stimulated photosynthesis, contributing to the initial BAI and iWUE increase. However, the intensified water stress resulting from reduced precipitation and increased temperature led to a reduction in tree stomatal conductance causing the subsequent increase in iWUE but decrease in BAI. Of the site- and species-related responses of tree growth to ca, climatic and environmental changes in the MRB, the site-related variation dominated. The non-linear relationship between BAI and ca combined with the quadratic relationship between BAI and iWUE indicate a decreased ability of forests to capture atmospheric CO2 once the CO2 tipping point has passed.
► The soil erosion control service of regional ecosystems was evaluated based on land cover change and USLE modeling. ► Results indicated that soil erosion control service has been greatly enhanced ...through vegetation restoration in the Loess Plateau region. ► The spatiotemporal variations of the soil erosion control service were determined, which may be crucial for regional ecological restoration.
Soil erosion in terrestrial ecosystems, as an important global environmental problem, significantly impacts on environmental quality and social economy. By protecting soil from wind and water erosion, terrestrial ecosystems supply human beings with soil erosion control service, one of the fundamental ecosystem services that ensure human welfare. The Loess Plateau was one of the regions in the world that suffered from severe soil erosion. In the past decades, restoration projects were implemented to improve soil erosion control in the region. The Grain-to-Green project, converting slope croplands into forest or grasslands, launched in 1999 was the most massive one. It is needed to assess the change of soil erosion control service brought about by the project. This study evaluated the land cover changes from 2000 to 2008 by satellite image interpretation. Universal Soil Loss Equation (USLE) was employed for the soil erosion control assessment for the same period with localized parameters. Soil retention calculated as potential soil erosion (erosion without vegetation cover) minus actual soil erosion was applied as indicator for soil erosion control service. The results indicate that ecosystem soil erosion control service has been improved from 2000 to 2008 as a result of vegetation restoration. Average soil retention rate (the ratio of soil retention to potential soil loss in percentage) was up to 63.3% during 2000–2008. Soil loss rate in 34% of the entire plateau decreased, 48% unchanged and 18% slightly increased. Areas suffering from intense erosion shrank and light erosion areas expanded. Zones with slope gradient of 8°–35° were the main contribution area of soil loss. On average, these zones produced 82% of the total soil loss with 45.5% of the total area in the Loess Plateau. Correspondingly, soil erosion control capacity was significantly improved in these zones. Soil loss rate decreased from 5000
t
km
−2
yr
−1 to 3600
t
km
−2
yr
−1, 6900
t
km
−2
yr
−1 to 4700
t
km
−2
yr
−1, and 8500
t
km
−2
yr
−1 to 5500
t
km
−2
yr
−1 in the zones with slope gradient of 8°–15°, 15°–25°, and 25°–35° respectively. However, the mean soil erosion rate in areas with slope gradient over 8° was still larger than 3600
t
km
−2
yr
−1, which is far beyond the tolerable erosion rate of 1000
t
km
−2
yr
−1. Thus, soil erosion is still one of the top environmental problems that need more ecological restoration efforts.
Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or ...dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.
Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) ...satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.
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