Search

Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The m A P of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects. [ABSTRACT FROM AUTHOR]

• This paper analyzes whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. • There is a nonlinear relationship between financial support for ecological restoration and grassland vegetation coverage. • Different fiscal expenditure structure has a heterogeneous response. • The degree of fiscal decentralization has a significant positive regulatory effect on the ecological benefits. Grassland ecological restoration is an important cornerstone to maintain the sustainable development of grassland ecosystem. One of the goals of financial support for grassland ecological protection and restoration is whether the ecological benefits of grassland are fully realized, and grassland vegetation coverage is the main index to measure the ecological benefits. This paper aims to analyze whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. and then verify whether there is room for improvement in the fiscal effect of grassland ecological protection and restoration? Based on the panel data of 54 animal husbandry and semi-agricultural and semi-animal husbandry counties in Inner Mongolia Autonomous Region of China, this paper adopts a bidirectional fixed effect model and cross-fixed effect model to introduce natural factors affecting grassland vegetation coverage into the model and systematically examines the financial effects of ecological benefits of financial support for grassland ecological protection and restoration. The main findings are as follows: (1) there is a nonlinear relationship between financial support for grassland ecological protection and restoration and grassland vegetation coverage when natural factors are taken into account in the model. (2) different fiscal expenditure structure has a heterogeneous response to the impact of grassland ecological protection and restoration on ecological benefits. (3) the degree of fiscal decentralization and the relative performance of the government have a significant positive moderating effect on the ecological benefits of fiscal support for grassland ecological protection and restoration. [ABSTRACT FROM AUTHOR]

In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation. [ABSTRACT FROM AUTHOR]

A method for predicting the dynamic spatio-temporal variations of the normalized difference vegetation index (NDVI) based on precipitation is proposed using combined nonlinear autoregressive with exogenous input (NARX) networks and artificial neural networks (ANNs). The proposed method is validated by applying to predict the spatio-temporal NDVI for the Hulunbuir grassland located in Inner Mongolia, China. The results show the good predictive ability for the spatio-temporal variations of NDVI with the mean absolute percentage error of 11.59%, mean absolute error of 7.11 × 10−2 and root mean square error of 8.06 × 10−2, respectively. The approach presented in the paper can be further used as the guidance to reduce the occurrence of overgrazing in the arid and semi-arid grasslands. [ABSTRACT FROM AUTHOR]

Biomass is an important indicator for monitoring vegetation degradation and productivity. This study tests the applicability of Hyperspectral Remote-Sensing in situ measurements for high-precision estimation aboveground biomass (AGB) on regional scales of Khorchin grassland in Inner Mongolia, China. In order to improve prediction accuracy of AGB which is frequently used as an indicator of aboveground net primary productivity (ANPP), this paper combined ground measurement with remote sensing inversion to build the spectral model. The ground normalized difference vegetation index (SOC_NDVI) calculated from ground spectral of grassland vegetation which was measured by a portable visible/NIR hyperspectral spectrometer (SOC 710). Meanwhile, the remote normalized difference vegetation index (TM_NDVI) calculated from remote spectral of grassland vegetation which was measured by Thematic Mapper (TM) from Landsat 8 which launched by National Aeronautics and Space Administration (NASA). According to regression analysis for the relationship between AGB and SOC_NDVI, SOC_NDVI and TM_NDVI, the evaluation model for aboveground biomass was developed (AGB = 12.523×e3.370×(0.462×TM_NDVI+0.413), standard error = 24.74 g m-2, R2 = 0.636, p < 0.001). The model accuracy verification results show that the correlation between the measured value and the predicted value of biomass was better with low model standard error. The model could make up for the lack of timeliness and comprehensiveness of conventional ground biomass survey, and provide technical support for high-precision large-area productivity estimation and ecological degradation diagnosis of regional scale grassland. [ABSTRACT FROM AUTHOR]

Fault identification in vegetated area (e.g., grassland) is a major challenge compared to that in outcrop areas. To identify concealed faults in a grassland covered area, a hybrid method combining the temperature vegetation dryness index (TVDI) and the singularity index was proposed in this paper to extract TVDI anomalies associated with concealed fractures in the Sonid Left Banner Grassland, Inner Mongolia, North China. In the triangle space of LST/NDVI (land surface temperature/normalized difference vegetation index), scattered points were concentrated in the areas of partial and full cover, while few data points were in the areas of bare soil with low values of NDVI and high values of LST; this result is consistent with the semiarid grassland landscape of the study area. Although TVDI imaging shows an obvious linear pattern with an NE-NNE trend, which indicates the existence of concealed faults, the surrounding background weakened and reduced the significance of the anomalies. To better delineate the concealed faults, the singularity index was employed to remove the influence of the background and enhance the TVDI anomalies associated with the concealed faults. The TVDI imaging and singularity index mapping showed NNE and NE orientations; this finding is consistent with the regional tectonic framework. Geological mapping footprints showed that the hybrid method is useful to identify concealed faults in covered areas of grassland. [ABSTRACT FROM AUTHOR]

• DPSIRM model was used to evaluate the ecological security of Xilingol grassland. • Remote sensing, meteorology, socio-economic and survey data are used in this paper. • We use grid GIS method to obtain more accurate evaluation results. • We analyzed the spatial-temporal evolution characteristics of ecological security. • Changes in the center of gravity in different security levels are also explored. The quantitative evaluation of the ecological security can reflect the changes in the quality of ecological environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. In this study, we selected Xilingol League in Inner Mongolia, China, as the research area, and established the driving force-pressure-state-impact-response-management (DPSIRM) model to evaluate grassland ecological security. The results were as follows: (1) During 2005–2015, the overall trend of ecological security level in the study area increased, with the western region being relatively ecologically insecure and the eastern region being more secure. (2) The spatial distribution trend of ecological security level from west to east was " very serious alerts " → " serious alerts " → " moderate alerts " → " light alerts "→ " no alert ". (3) The " very serious alerts " and " serious alerts " are mainly concentrated in Sonid Left Banner and Sonid Right Banner, but from the perspective of spatial migration of ecological security, unsafe and less safe tend to shift to the northeast. This study provides a new idea for grassland ecological security evaluation research, has important practical significance for understanding the ecological security level of Xilingol grassland area, and will guide the reasonable development and utilization of Xilingol grassland. [ABSTRACT FROM AUTHOR]

Techniques for mapping and monitoring vegetation status under human disturbance (e.g., grazing livestock) can contribute to sustainable development of grasslands. The main objective of this paper is to investigate whether the effects of differences in grazing intensity (measured by stocking rate (SR)) could be discriminated using in situ spectral data. The experiment was conducted with controlled SRs on the Sino-German experimental farm in the Xilingol grassland, Inner Mongolia, P.R. China. Canopy spectral measurements were made on experimental paddocks that varied by SRs, terrain and management type. Eight vegetation indices (VIs) were calculated from ASD field spectrometer data for each paddock. We modelled the variability within and between paddocks using a linear mixed model to isolate the effect of grazing intensity on VI response. All VIs showed significant negative relationships to grazing intensity that were ameliorated on paddocks with haying compared to traditional direct grazing. The best VIs for detecting differences in paddocks due to the levels of SR were photochemical reflectance index (PRI), broadband normalized difference vegetation index (bNDVI) and narrowband NDVI (nNDVI1). The responses of VIs to vegetation conditions as affected by different SRs offer promise for the use of airborne and satellite remote sensing to map vegetation status and to evaluate the effects of grazing intensities at regional scales. [ABSTRACT FROM AUTHOR]

Coal is an important energy resource in the world, especially in China. Extensive coal exploitation seriously damaged the grassland and its fragile ecosystem. However, temporal and spatial impact laws of open-pit coal exploitation on Landscape Ecological Health (LEH) of semi-arid grasslands are still not clear. Therefore, the main objective of this paper is to study impact of Large-scale Open-pit Coal Base (LOCB) on the LEH of semi-arid grasslands from the perspectives of temporal and spatial. Taking Shengli LOCB of Xilinguole grassland in Inner Mongolia as an example, we demonstrate a conceptual model of LOCB impact on LEH of semi-arid grasslands, and establish a research system called landscape Index-pattern Evolution-Driving force-Spatial statistics (IEDS). A complete process integrated from investigation, monitoring, and evaluation to the analysis of impact laws was developed. Result indicated that coal mining causes gradual increase of landscape patches, landscape fragmentation, gradual decline of landscape connectivity, complexity and irregularity of landscape shape, enhancement of landscape heterogeneity and complexity, gradual decline of landscape stability, gradual decrease of grassland landscape and annual increase of unhealthy grassland landscape. The LEH of grassland basically belongs to the state of slight deterioration. In the past 15 years, the spatial and temporal distribution characteristics of LEH in the study area are similar. This study provides scientific reference for ecological disturbance research, environmental protection, landscape planning, restoration and renovation of ecological environment in mining areas. At the same time, future research should integrate geological, hydrological, soil, vegetation, microorganisms, animals, climate, and other perspectives to study the impact of mining on landscape ecology deeply. [ABSTRACT FROM AUTHOR]