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Typhoons are recognized as one of the most destructive meteorological phenomena, exerting significant influences on marine ecosystems. Sea surface chlorophyll-a concentration (CHL)an essential indicator of phytoplankton biomass, can be utilized to characterize the disturbances of typhoons on the marine ecosystem. However, it is challenging to investigate this impact at a daily scale due to the missing CHL remote sensing data caused by cloud cover. Given that concurrent passing typhoons may interact with CHL, this study analyzes the effect of the simultaneous passage of binary typhoons Tembin and Bolaven on CHL by using daily CHL reconstruction data, and investigates the role of ocean environmental factors in driving the dynamics of CHL, including sea surface temperature (SST), mixed layer depth (MLD), and sea surface height anomaly (SSHA). The results show that typhoons Tembin and Bolaven increase CHL with the maximum increment of ∼3.2 mg∙m-3 during 4-6 days after typhoons passage. The maximum change areas of CHL are distributed near the intersection of typhoon track of (32°N, 125.2°E), corresponding to the regions of greater variation in SST and MLD. During 15 days before and after typhoons (i.e., from 15 August to 15 September 2012), SST is negatively correlated with CHL (the correlation coefficient of -0.85) and MLD is positively correlated with CHL (the correlation coefficient of -0.80). SST immediately declines after typhoons with a maximum cooling of 7.8 deg. C, showing the decreased SST from ∼28 deg. C to ∼23 deg. C can promote phytoplankton growth. MLD deepens from 10 m to >25 m caused by typhoon-induced strong winds, allowing more nutrients to be transported from the subsurface layer to the euphotic layer for phytoplankton blooms. Furthermore, oceanic eddies captured by SSHA change from cyclonic to anticyclonic eddies accompanied by the beginning of CHL increases, and the largest CHL increases correspond to the distribution of pre-existing cyclonic eddies. It suggests that Tembin and Boravin promote phytoplankton growth to increase CHL by enhancing vertical mixing and upwelling to transport nutrients to the sea surface. These findings inspire us to rethink the daily effects of typhoons on CHL, with critical importance for predicting and managing the ecological consequences of typhoons in the ocean.

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Climate change mitigation is a pressing global challenge that requires reducing CO2 emissions without hindering economic growth. Using an extended Kaya identity, Logarithmic Mean Divisia Index (LMDI), and Tapio decoupling indicator, this paper investigates the spatio-temporal variations, drivers, and decoupling of CO2 emissions from economic growth in 150 countries from 1990 to 2019, considering regional disparities and income-based inequalities. The findings reveal increasing CO2 emissions between 1990 and 2019, with notable fluctuations in certain 5-year intervals. CO2 emission growth varied significantly by region, with countries like China, the USA, India, and Japan experiencing rapid increases. Economic growth emerged as the primary driver of CO2 emission growth, and its impact strengthened over time. Population growth also contributed significantly to CO2 emissions, particularly in middle- and low-income countries. The study identifies energy and carbon intensity as crucial mitigating factors that weaken CO2 emissions, offering hope for effective climate change mitigation. Furthermore, the degree of decoupling between economic growth and CO2 emissions varied among countries in the same region, with high-income countries demonstrating stronger decoupling compared to upper-middle-income countries, which accounted for 71% of global CO2 emission increase. These findings underline the imperative of accounting for income levels and regional differences in formulating CO2 emission mitigation strategies. Also, the study emphasizes the pressing necessity for cohesive global coordination to facilitate the transition toward a low-carbon economy. Such collaborative endeavors are paramount in our collective pursuit to combat climate change effectively, safeguarding the well-being and sustenance of our planet for future generations. As policymakers, it is imperative to integrate these insights into decision-making processes to chart a sustainable and resilient course forward.

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Understanding the gap between potential productivity and the actual productivity of vegetation (vegetation productivity gap, VPG) is the basis to explore the potential productivity improvement and identify its constraints. In this study, we used the classification and regression tree model to simulate the potential net primary productivity (PNPP) based on the flux-observational maximum net primary productivity (NPP) across different vegetation types, that is, potential productivity. The actual NPP (ANPP) is obtained from the grid NPP averaged over five terrestrial biosphere models, and the VPG is subsequently calculated. On this basis, we used the variance decomposition method to separate the effects of climate change, land-use change, CO2, and nitrogen deposition on the trend and the interannual variability (IAV) of VPG from 1981 to 2010. Meanwhile, the spatiotemporal variation characteristics and influencing factors of VPG under future climate scenarios are analyzed. The results showed an increasing trend in PNPP and ANPP, while there was a decreasing trend of VPG in most parts of the world and this trend is more significant under representative concentration pathways (RCPs). The turning points (TP) of VPG variation are found under RCPs and the reduction trend of VPG before TP is more than that after TP. The VPG reduction in most regions was caused by the combined effects of PNPP and ANPP (41.68 %) from 1981 to 2010. However, the dominant factors of global VPG reduction are changing under RCPs, and the increment of NPP (39.71 % - 49.3 %) has become the dominating factor of VPG variation. CO2 plays a decisive role in the multi-year trend of VPG, while climate change is the main factor determining the IAV of VPG. Under changing climate, temperature and precipitation are negatively correlated with VPG in most parts of the world, and the relationship between radiation and VPG from weak negative to positive correlation.

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Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. This study proposes the application of a spatial filling method using the machine learning-based Extreme Gradient Boosting (BST) to reconstruct missing pixels of daily MODIS Chl-a data from 2007 to 2018. The approach is applied to different trophic biogeographical subregions of the Northwestern Pacific where it has complex phytoplankton dynamics and frequent data missing. Various environmental variables are taken into consideration, including meteorological forcing, geographic and topographic features, and oceanic physical components. The BST-reconstructed Chl-a (BST Chl-a) is validated using in-situ Chl-a measurements, VIIRS and Himawari-8 Chl-a products. The results show that the BST model is highly adaptive in reconstructing Chl-a data, and it performs well in pelagic, offshore and coastal with the best performance in pelagic. BST Chl-a improves coverage without significant quality degradation compared to the original MODIS Chl-a. BST Chl-a agrees better with in-situ data than that of MODIS, with CC of 0.742, RMSE of 0.247, MAE of 0.202 and Bias of 0.089. Cross-satellite validation using VIIRS and Himawari-8 Chl-a also shows promising results with the CC of 0.861 and 0.765, respectively, suggesting the high accuracy of BST Chl-a. The inter-annual trend of BST Chl-a decreases in coastal and increases in offshore and pelagic. BST Chl-a images present similar spatial patterns to MODIS Chl-a under different missing rates, with gradual decreases from coastal to pelagic. It indicates that phytoplankton bloom patterns can be identified by daily BST Chl-a images.

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Dust deposition can supply nutrients to the ocean and affect phytoplankton growth. However, the impact of dust deposition on phytoplankton biomass in varying trophic regions remains poorly evaluated. The Northwestern Pacific is located in the downwind area of East Asian dust and includes eutrophic regions (Yellow Sea, YS; East China Sea, ECS), high-nutrient low-chlorophyll waters (subarctic Northwestern Pacific, SNWP) and low-nutrient low-chlorophyll waters (Northwestern Pacific subtropical gyre, NWPSG), which is an ideal region to explore the spatial heterogeneity of the dust fertilization effect. Here, the distribution and variation of dust deposition, high dust deposition events (HDDE) and Chlorophyll-a concentration (Chl-a, mg m-3) in the Northwestern Pacific during spring from 1998 to 2020 were investigated. The differences in the response of phytoplankton biomass (using Chl-a as a proxy) to HDDE in the YS, the ECS, the SNWP and the NWPSG were explored. Our results indicated that a large amount of dust was deposited into the Northwest Pacific during spring, resulting in numerous HDDE. The HDDE could stimulate the increase of phytoplankton biomass in the whole area of the Northwestern Pacific during spring. The response probabilities of Chl-a to HDDE were most significant (~80%) in the SNWP and the duration of response was the longest, even lasting for up to 40 days. While the response probabilities of Chl-a to HDDE were lowest in the YS and ECS (~65%), increasing from north to south, and most of the responses were less than 20 days. The response of Chl-a to HDDE was also detected in NWPSG, confirming the dust fertilization effect in oligotrophic waters, with response probabilities of 70% and duration less than 30 days. Overall, this study provides a more comprehensive understanding of the differences of phytoplankton response to dust deposition in varying trophic regions.

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Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.

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Evaluating the climate potential productivity (CPP) of terrestrial vegetation is crucial to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, and to fully display the productivity application level. In this study, the maximum net primary productivity (NPPmax) representing the highest possible productivity of vegetation was calculated using the FLUXNET maximum gross primary productivity (GPPmax) from 177 flux towers. The relationships between NPPmax and a set of climate variables were established using the classification and regression tree (CART) modeling framework. The CART algorithm was used to upscale the CPP to the global scale under the current climate baseline (1980-2018) and future climate scenarios. The spatiotemporal variations in CPP over the globe were analyzed and the impacts of climate factors on it were assessed. The results indicate that global CPPs range from 0 to 2000 g C/m2. The tropical rainforest area is the region with the highest CPP, whereas the lowest CPP occurs in arid/semiarid areas. These two regions were identified as the areas with the largest CPP reductions in the future. The findings reveal that CPP shows signs of productivity saturation and that future climate is not conducive to the increases in vegetation productivity in these regions. The increases in average annual temperature, minimum temperature, and solar radiation are beneficial to CPP increase in most parts of the globe under climate change. However, the negative contribution of maximum temperature increase and precipitation reduction to CPP is higher than the positive contribution of the above three rising factors to CPP in tropical and arid/semiarid areas. Our study is important to aid in creating targeted policies for future sustainable development, resource allocation, and vegetation management.

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Dust storms are one of the major disasters in arid and semi-arid regions. Understanding the impact factors is crucial for early warning and disaster mitigation. Many factors have been affecting the spatiotemporal patterns of dust storms. However, the relative importance of those factors to dust emissions in recent 40 years over the whole dust belt has not been well documented. This study explored the relative importance of those factors to the interannual variation in dust emissions over the whole dust belt. The difference in the primary contributors over two global warming phases was compared to investigate the association of dust emission trend with global warming. The results indicated that the wind regimes, such as the nocturnal low-level jet, were key factors to the wintertime dust emissions over the Sahel. The springtime dust storms related to cold air and cyclones primarily occurred in the southern coast of the Mediterranean and northwestern China. The cold high and heat low were typical mechanisms for the summertime dust emissions, which frequently formed in western North Africa, the Middle East, and northwestern Indian subcontinent. Whereas the land cover and drought conditions play significant roles in the relatively wetter regions, i.e., the southern coast of the Mediterranean, the Ustyurt Plateau, the northwest coast of Indian Ocean, the Thar desert and the Taklimakan desert. The wintertime global warming coincided well with the decreasing trend of dust emissions over the dust sources inland with a more significant effect seen in Asia. The positive anomalies of summertime dust emissions were primarily found over the dust sources in the low-lying coastal areas on the foot of high mountains. Understanding the relative importance of those drivers to dust emission trends and their variation under different warming periods can improve the prediction of dust storm evolutions and mitigate their impacts under future climate change.

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In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15 years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15 years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted.

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Drought is the most complex climate-related disaster issue in South Asia, because of the various land-cover changes, vegetation dynamics, and climates. The aims of the current research work were to analyze the performance of AVHRR Normalized Difference Vegetation Index (NDVI) and spatiotemporal differences in vegetation dynamics on a seasonal basis by correlating the results with NASA's MERRA precipitation and air temperature for monitoring vegetation dynamics and drought over South Asia. Our approach is based on the use of AVHRR NDVI data and NASA's MERRA rainfall and air temperature data (1990-2011). Due to the low vegetation and dryness, the NDVI is more helpful in describing the drought condition in South Asia. There were rapid increases in NDVI, VHI, and VCI from April to October. Monthly NDVI, VHI, and VCI stabilize in September and improved once more in October and then show a declining trend in December. The monthly PCI, TCI, VHI, and VCI values showed that the South Asia goes through an extreme drought in 2000, which continues up to 2002, which lead the highest water stress. Spatial correlation maps among NDVI, precipitation, air temperature, VHI, and VCI on a seasonal basis. The correlation between NDVI and precipitation showed a significantly higher correlation value in JJA and SON seasons; the spatial correlation between NDVI and air temperature showed significant high values in DJF, JJA, and SON periods, while the correlation between VHI and TCI showed a significantly higher values in MAM and SON seasons, which indicated a good sign for dryness monitoring, mainly for farming regions during these seasons in South Asia. It was confirmed that these indexes are a comprehensive drought monitoring indicator and a step to monitoring the climate change in South Asia, which will play a relevant role ongoing studies on vegetation types, monitoring climate change, and drought over South Asia.

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Maize is one of the major cultivated crops of China, having a central role in ensuring the food security of the country. There has been a significant increase in studies of maize under interactive effects of elevated CO2 concentration ([CO2]) and other factors, yet the interactive effects of elevated [CO2] and increasing precipitation on maize has remained unclear. In this study, a manipulative experiment in Jinzhou, Liaoning province, Northeast China was performed so as to obtain reliable results concerning the later effects. The Open Top Chambers (OTCs) experiment was designed to control contrasting [CO2] i.e., 390, 450 and 550 µmol·mol(-1), and the experiment with 15% increasing precipitation levels was also set based on the average monthly precipitation of 5-9 month from 1981 to 2010 and controlled by irrigation. Thus, six treatments, i.e. C550W+15%, C550W0, C450W+15%, C450W0, C390W+15% and C390W0 were included in this study. The results showed that the irrigation under elevated [CO2] levels increased the leaf net photosynthetic rate (Pn) and intercellular CO2 concentration (Ci) of maize. Similarly, the stomatal conductance (Gs) and transpiration rate (Tr) decreased with elevated [CO2], but irrigation have a positive effect on increased of them at each [CO2] level, resulting in the water use efficiency (WUE) higher in natural precipitation treatment than irrigation treatment at elevated [CO2] levels. Irradiance-response parameters, e.g., maximum net photosynthetic rate (Pnmax) and light saturation points (LSP) were increased under elevated [CO2] and irrigation, and dark respiration (Rd) was increased as well. The growth characteristics, e.g., plant height, leaf area and aboveground biomass were enhanced, resulting in an improved of yield and ear characteristics except axle diameter. The study concluded by reporting that, future elevated [CO2] may favor to maize when coupled with increasing amount of precipitation in Northeast China.

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The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area. The result showed that for a pure snow spectrum, the snow reflectance peaks appeared from visible to 800 nm band locations; there was an obvious absorption valley of snow spectrum near 1 030 nm wavelength. Compared with fresh snow, the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300-1 300, 1 700-1 800 and 2 200-2 300 nm, the lowest was from the compacted snow and frozen ice. For the vegetation and snow mixed spectral characteristics, it was indicated that the spectral reflectance increased for the snow-covered land types (including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350-1 300 nm. However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic. In the end, based on the spectrum analysis of snow, vegetation, and mixed snow/vegetation pixels, the mixed spectral fitting equations were established, and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones (correlation coefficient R2 = 0.950 9).

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Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

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The present paper reviews the progress in the methods of retrieving vegetation water content using remote sensing spectral information, including vegetation spectral reflectance information (VIR, SWIR, and NIR) to directly extract vegetation water content and establish vegetation water indices (WI), i. e. NDWI = (R860 - R1 240)/(R860 + R1 240) and PWI = R970/R900; and using radiation transfer (RT) model such as PROSPAIL to detect plant water content information. The authors analyze the method of retrieving vegetation water content under low crop coverage condition. The plant water can be estimated by using canopy physiological parameters firstly, and using vegetation indices and radiation transfer model secondly, which can eliminate soil background effect. The estimated agricultural drought and vegetation water content by using multi-angle polarized reflectance and bi-directional reflectance (BRDF) was discussed in this paper. In the end, the possible development trend of retrieval methods for plant water information under plant low coverage conditions was discussed.

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Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

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The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationshipsamong Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climatevariables were quantified for six counties within the NTP. The notable and unevenalterations of the grassland in response to variation of climate and human impact in theNTP were revealed. Over the last two decades of the 20th century, the maximum greennessof the grassland has exhibited high increase, slight increase, no-change, slight decrease andhigh decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area ofthe NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in thecentral-eastern (eastern) NTP whereas little change was observed in the western andnorthwestern NTP. A strong negative relationship between VP-NDVI and ET0 was foundin sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgonand Amdo counties), suggesting that the ET0 is one limiting factor affecting grasslanddegradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chaliand Sokshan counties), a significant inverse correlation between VP-NDVI and populationindicates that human activities have adversely affected the grassland condition as waspreviously reported in the literature. Results from this research suggest that the alterationand degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.