scholarly journals Comprehensive Assessment Indicator of Ecosystem Resilience in Central Asia

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 124
Author(s):  
Xue Fan ◽  
Xingming Hao ◽  
Haichao Hao ◽  
Jingjing Zhang ◽  
Yuanhang Li
Keyword(s):  
Central Asia ◽  
Net Primary Productivity ◽  
Ecosystem Resilience ◽  
Mountainous Areas ◽  
Grassland Vegetation ◽  
High Coverage ◽  
Casa Model ◽  
Assessment Indicator ◽  
Multiple Dimensions ◽  
Resilience Index

The ecosystems in the arid inland areas of Central Asia are fragile and severely degraded. Understanding and assessing ecosystem resilience is a challenge facing ecosystems. Based on the net primary productivity (NPP) data estimated by the CASA model, this study conducted a quantitative analysis of the ecosystem’s resilience and comprehensively reflected its resilience from multiple dimensions. Furthermore, a comprehensive resilience index was constructed. The result showed that plain oasis’s ecosystem resilience is the highest, followed by deserts and mountainous areas. From the perspective of vegetation types, the highest resilience is artificial vegetation and the lowest is forest. In warm deserts, the resilience is higher in shrubs and meadows and lower in grassland vegetation. High coverage and biomass are not the same as the strong adaptability of the ecosystem. Moderate and slightly inelastic areas mainly dominate the ecosystem resilience of the study area. The new method is easy to use. The evaluation result is reliable. It can quantitatively analyze the resilience latitude and recovery rate, a beneficial improvement to the current ecosystem resilience evaluation.

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals Spatiotemporal Variation in Ecosystem Services and Their Drivers among Different Landscape Heterogeneity Units and Terrain Gradients in the Southern Hill and Mountain Belt, China

2021 ◽  
Vol 13 (7) ◽  
pp. 1375
Author(s):  
Liang-Jie Wang ◽  
Shuai Ma ◽  
Jiang Jiang ◽  
Yu-Guo Zhao ◽  
Jin-Chi Zhang
Keyword(s):  
Ecosystem Services ◽  
Soil Conservation ◽  
Net Primary Productivity ◽  
Landscape Heterogeneity ◽  
Driving Factors ◽  
Water Yield ◽  
Invest Model ◽  
Casa Model ◽  
Mountain Belt ◽  
Integrated Valuation

Understanding the spatiotemporal heterogeneity of ecosystem services (ESs) and their drivers in mountainous areas is important for sustainable ecosystem management. However, the effective construction of landscape heterogeneous units (LHUs) to reflect the spatial characteristics of ESs remains to be studied. The southern hill and mountain belt (SHMB) is a typical mountainous region in China, with undulating terrain and obvious spatial heterogeneity of ESs, and was selected as the study area. In this study, we used the fuzzy k-means (FKM) algorithm to establish LHUs. Three major ESs (water yield, net primary productivity (NPP), and soil conservation) in 2000 and 2015 were quantified using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and Carnegie Ames-Stanford approach (CASA) model. Then, we explored the spatial variation in ESs along terrain gradients and LHUs. Correlation analysis was used to analyze the driving factors of ESs in each terrain region and LHU. The results showed that altitude and terrain niche increased along LHUs. Water yield and soil conservation increased from 696.86 mm and 3920.19 t/km2 to 1061.12 mm and 5117.90 t/km2, respectively, while NPP decreased from 666.95 gC/m2 to 648.86 gC/m2. The ESs in different LHUs differed greatly. ESs increased first and then decreased along LHUs in 2000. In 2015, water yield decreased along LHUs, while NPP and soil conservation showed a fluctuating trend. Water yield was mainly affected by precipitation, temperature and NDVI were the main drivers of NPP, and soil conservation was greatly affected by precipitation and slope. The driving factors of the same ES were different in different terrain areas and LHUs. The variation and driving factors of ESs in LHUs were similar to some terrain gradients. To some extent, LHUs can represent multiple terrain features. This study can provide important support for mountain ecosystem zoning management and decision-making.

scholarly journals Simulating Spatiotemporal Dynamics of Sichuan Grassland Net Primary Productivity Using the CASA Model and In Situ Observations

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chuanjiang Tang ◽  
Xinyu Fu ◽  
Dong Jiang ◽  
Jingying Fu ◽  
Xinyue Zhang ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Alpine Meadow ◽  
Growth Period ◽  
Temporal Variations ◽  
Light Use Efficiency ◽  
Important Indicator ◽  
Casa Model ◽  
Use Efficiency

Net primary productivity (NPP) is an important indicator for grassland resource management and sustainable development. In this paper, the NPP of Sichuan grasslands was estimated by the Carnegie-Ames-Stanford Approach (CASA) model. The results were validated with in situ data. The overall precision reached 70%; alpine meadow had the highest precision at greater than 75%, among the three types of grasslands validated. The spatial and temporal variations of Sichuan grasslands were analyzed. The absorbed photosynthetic active radiation (APAR), light use efficiency (ε), and NPP of Sichuan grasslands peaked in August, which was a vigorous growth period during 2011. High values of APAR existed in the southwest regions in altitudes from 2000 m to 4000 m. Light use efficiency (ε) varied in the different types of grasslands. The Sichuan grassland NPP was mainly distributed in the region of 3000–5000 m altitude. The NPP of alpine meadow accounted for 50% of the total NPP of Sichuan grasslands.

Spatial Analysis on the Fluctuation of Regional NPP in the Typical Steppe, in China

2012 ◽  
Vol 518-523 ◽  
pp. 5126-5129 ◽  
Author(s):  
Su Ying Li ◽  
Xiu Mei Wang ◽  
Ying Chang ◽  
Xiao Xia Wu ◽  
Qiang Fan
Keyword(s):  
Energy Efficiency ◽  
Primary Productivity ◽  
Inner Mongolia ◽  
Annual Variation ◽  
Net Primary Productivity ◽  
Average Rate ◽  
Landscape Scale ◽  
Typical Steppe ◽  
Casa Model ◽  
Grassland Productivity

Assessing the inter-annual variation of regional grassland productivity is imperative to meet the local requirements of grassland adaptive management at regional- or landscape- scale. For the semiarid grassland of Inner Mongolia, the improved CASA model, a kind of light-energy-efficiency model, was used to simulate the Net Primary Productivity (NPP) of the regional grassland. And this study further calculated the Standard Deviation (SD) and Coefficient of Variation (CV) of the regional NPP. Both of SD and CV were used to reflect the fluctuations of regional NPP in the study area among years. Approximately 1/3 of the regional NPP over the years were dramatically changed, frequently up to large amplitude by an average rate of 1 times or more.

A new synonym and a new combination in Stipa aliena Keng (Poaceae: Stipa sect. Regelia)

Phytotaxa ◽  
2015 ◽  
Vol 195 (3) ◽  
pp. 236 ◽  
Author(s):  
Marcin Nobis ◽  
Arkadiusz Nowak ◽  
Polina D Gudkova
Keyword(s):  
Central Asia ◽  
New Combination ◽  
Mountainous Areas ◽  
Key To Species ◽  
New Synonym ◽  
Typical Variety

Stipa section Regelia comprises three species occurring in mountainous areas of Central Asia. One of them, S. smithii, was described by Martinovský in 1970, but the taxon has been overlooked in later taxonomical studies. The species was described with two varieties, var. smithii and var. macrocarpa. As a result of our taxonomical studies, we find the typical variety of the taxon to be conspecific with Stipa aliena, and propose that the second one be recognized as Stipa aliena var. macrocarpa comb. nov. Remarks on species belonging to section Regelia and micromorphological patterns of their lemma morphologies are discussed. A key to species close to S. aliena is provided.

Assessment of ecosystem resilience in Central Asia

2021 ◽  
Vol 195 ◽  
pp. 104625
Author(s):  
Jingxiu Qin ◽  
Xingming Hao ◽  
Ding Hua ◽  
Haichao Hao
Keyword(s):  
Central Asia ◽  
Ecosystem Resilience

scholarly journals A global indicator of the capacity of terrestrial ecosystems to retain biological diversity under climate change: the Bioclimatic Ecosystem Resilience Index

2019 ◽  
Author(s):  
Simon Ferrier ◽  
Thomas D Harwood ◽  
Chris Ware ◽  
Andrew J Hoskins
Keyword(s):  
Climate Change ◽  
Biological Diversity ◽  
Spatial Configuration ◽  
Natural Habitat ◽  
Terrestrial Ecosystems ◽  
Ecosystem Resilience ◽  
Forest Change ◽  
Habitat Condition ◽  
Resilience Index ◽  
Projected Changes

AbstractAn important element of the Convention on Biological Diversity’s Aichi Target 15 – i.e. to enhance “ecosystem resilience … through conservation and restoration” – remains largely unaddressed by existing indicators. We here develop an indicator addressing just one of many possible dimensions of ecosystem resilience, by focusing on the capacity of ecosystems to retain biological diversity in the face of ongoing, and uncertain, climate change. The Bioclimatic Ecosystem Resilience Index (BERI) assesses the extent to which a given spatial configuration of natural habitat will promote or hinder climate-induced shifts in biological distributions. The approach uses existing global modelling of spatial turnover in species composition within three broad biological groups (plants, invertebrates and vertebrates) to scale projected changes in composition under a plausible range of climate scenarios. These projections serve as filters through which to analyse the configuration of habitat observed at a given point in time (e.g. for a particular year) – represented as a grid in which cells are scored in terms of habitat condition. BERI is then calculated, for each cell in this grid, as a function of the connectedness of that cell to areas of natural habitat in the surrounding landscape which are projected to support a similar composition of species under climate change to that currently associated with the focal cell. All analyses are performed at 30-arcsecond grid resolution (approximately 1km cells at the equator). Results can then be aggregated to report on status and trends for any desired set of reporting units – e.g. ecoregions, countries, or ecosystem types. We present example outputs for the Moist Tropical Forest Biome, based on a habitat-condition time series derived from the Global Forest Change dataset. We also describe how BERI is now being extended to cover all biomes (forest and non-forest) across the entire terrestrial surface of the planet.

scholarly journals Response of net primary productivity to grassland phenological changes in Xinjiang, China

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10650
Author(s):  
Renping Zhang ◽  
Jing Guo ◽  
Gang Yin
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Growing Season ◽  
Casa Model ◽  
Phenological Changes ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Phenological Metrics ◽  
The Impact

Determining the relationship between net primary productivity (NPP) and grassland phenology is important for an in-depth understanding of the impact of climate change on ecosystems. In this study, the NPP of grassland in Xinjiang, China, was simulated using the Carnegie-Ames-Stanford approach (CASA) model with Moderate Resolution Imaging Spectroradiometer (MODIS) grassland phenological (MCD12Q2) data to study trends in phenological metrics, grassland NPP, and the relations between these factors from 2001–2014. The results revealed advancement of the start of the growing season (SOS) for grassland in most regions (55.2%) in Xinjiang. The percentage of grassland area in which the end of the growing season (EOS) was delayed (50.9%) was generally the same as that in which the EOS was advanced (49.1%). The percentage of grassland area with an increase in the length of the growing season (LOS) for the grassland area (54.6%) was greater than that with a decrease in the LOS (45.4%). The percentage of grassland area with an increase in NPP (61.6%) was greater than that with a decrease in NPP (38.4%). Warmer regions featured an earlier SOS and a later EOS and thus a longer LOS. Regions with higher precipitation exhibited a later SOS and an earlier EOS and thus a shorter LOS. In most regions, the SOS was earlier, and spring NPP was higher. A linear statistical analysis showed that at various humidity (K) levels, grassland NPP in all regions initially increased but then decreased with increasing LOS. At higher levels of K, when NPP gradually increased, the LOS gradually decreased.

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