SMOS L-VOD shows that post-fire recovery of dense forests is slower than what is depicted with X- and C-VOD and optical indices

Anthropogenic climate change is now considered to be one of the main factors causing an increase in both frequency and severity of wildfires. These fires are prone to release substantial quantities of CO2 in the atmosphere and to destroy natural ecosystems while reducing biodiversity. Depending on the ecosystem and climate regime, fires have distinct triggering factors and impacts. To better analyse and describe fire impact on different biomes, we investigated pre and post fire vegetation anomalies at global scale. The study was performed using several remotely sensed quantities ranging from optical vegetation indices (the enhanced vegetation index (EVI)) to vegetation opacities obtained at several microwave wavelengths (X-band, C-band, and L-band vegetation optical depth (X-VOD, C-VOD, and L-VOD)), ranging from 2 to 20 cm. It was found that C- and X-VOD are mostly sensitive to fire over low vegetation areas (grass and small bushes) or over tree leaves; while L-VOD depicts better the fire impact on tree trunks and branches. As a consequence, L-VOD is probably a better way of assessing fire impact on biomass. The study shows that L-VOD can be used to monitor fire affected areas as well as post-fire recovery, especially over densely vegetated areas.

Computer Mathematical Statistics Analysis of downscaling data of different vegetation index inversion TRMM

This paper uses enhanced vegetation index (EVI) data, normalized vegetation index (NDVI) data, DEM, aspect data, and TRMM3B43 (V7) data, based on a geographically weighted regression model (GWR), and uses a statistical downscaling method to achieve Central China Downscaling of regional TRMM data from 2010 to 2019. The research results show: (1) TRMM data has good applicability in Central China, and the R2of TRMM data and weather station measured data is above 0.8. (2) Improve the ground resolution from 0.25°×0.25° (approximately 27.5km×27.5km) to 1km×1km while ensuring the same accuracy as the original data. (3) Overall, the accuracy of EVI downscaled precipitation data in Central China is better than that of NDVI downscaled precipitation data.

Annual dynamic dataset of global cropping intensity from 2001 to 2019

The cropping intensity has received growing concern in the agriculture field in applications such as harvest area research. Notwithstanding the significant amount of existing literature on local cropping intensities, research considering global datasets appears to be limited in spatial resolution and precision. In this paper, we present an annual dynamic global cropping intensity dataset covering the period from 2001 to 2019 at a 250-m resolution with an average overall accuracy of 89%, exceeding the accuracy of the current annual dynamic global cropping intensity data at a 500-m resolution. We used the enhanced vegetation index (EVI) of MOD13Q1 as the database via a sixth-order polynomial function to calculate the cropping intensity. The global cropping intensity dataset was packaged in the GeoTIFF file type, with the quality control band in the same format. The dataset fills the vacancy of medium-resolution, global-scale annual cropping intensity data and provides an improved map for further global yield estimations and food security analyses.

Characterising the vegetation-rainfall relationship in the Northeast Himalaya, India

Ecosystems are complex systems and are characterised by positive and negative feedbacks between the abiotic and biotic components. The response of an ecosystem to its environment can be determined by examining state diagrams, which are plots of the state variable as a function of the environmental driver. For instance, tree cover as a function of rainfall is widely used to characterise vegetation patterns. Previous studies have shown that tree cover shows bimodal distributions for intermediate rainfall regimes in Africa and South America. In this study, we construct a vegetation state diagram by plotting vegetation cover as a function of mean annual rainfall for Northeast India, which is part of the Eastern Himalaya and the Indo-Burma biodiversity hotspot. We use remotely sensed satellite data of Enhanced Vegetation Index (EVI) as a proxy for vegetation cover. We obtain Mean Annual Precipitation (MAP) from the CHIRPS data (Climate Hazards Group InfraRed Precipitation with Station data). We find that EVI increases monotonically as a function of MAP in the range 1000-2000 mm, after which it plateaus. The 1000 to 2000 mm MAP corresponds to the vegetation transitional zone (1200-3700 m), whereas >2000 MAP region covers the greater extent of the tropical forest (<1200 m) of NEI. In other words, we find no evidence for bimodality in tree cover or vegetation states at coarser scales in North Eastern India. Our characterisation of the state diagram for vegetation in northeast India is important to understand response to ongoing change in rainfall patterns. Keywords: spatial ecology, remotely sensed data, Enhanced Vegetation Index, State diagram, northeast India

Different Temporal Stability and Responses to Droughts between Needleleaf Forests and Broadleaf Forests in North China during 2001–2018

Droughts can affect the physiological activity of trees, damage tissues, and even trigger mortality, yet the response of different forest types to drought at the decadal time scale remains uncertain. In this study, we used two remote sensing-based vegetation products, the MODIS enhanced vegetation index (EVI) and MODIS gross primary productivity (GPP), to explore the temporal stability of deciduous needleleaf forests (DNFs) and deciduous broadleaf forests (DBFs) in droughts and their legacy effects in North China from 2001 to 2018. The results of both products showed that the temporal stability of DBFs was consistently much higher than that of DNFs, even though the DBFs experienced extreme droughts and the DNFs did not. The DBFs also exhibited similar patterns in their legacy effects from droughts, with these effects extending up to 4 years after the droughts. These results indicate that DBFs have been better acclimated to drought events in North China. Furthermore, the results suggest that the GPP was more sensitive to water variability than EVI. These findings will be helpful for forest modeling, management, and conservation.

Long Term Spatio-Temporal Trends In Aerosol Optical Depth And Its Relationship With Enhanced Vegetation Index And Meteorological Parameters Over South Asia

Satellite-based Aerosol optical depth (AOD) is columnar light extinction by aerosol absorption and scattering and has become the most important variable for the assessment of the spatiotemporal distribution of aerosols at a regional and global level. In this paper, we have used AOD observations from multi-angle imaging spectroradiometer (MISR), moderate resolution imaging spectroradiometer (MODIS) and sea viewing wide field-of-view Sensor (SeaWiFS). We have observed the association of AOD with enhanced vegetation index (EVI) and meteorological variables (temperature (TEMP), WS and relative humidity (RH)). The findings show that AOD in eastern Pakistan is higher than in the western Pakistan due to increase in population density and biomass burning. Mean annual peak AOD (˃0.7) has been observed over the IGB region because of the significant increase in economical, industrial and agricultural activities. The highest seasonal mean AOD (0.8) has been seen over Bihar, India during the winter season. However, the mean AOD over eastern Pakistan is maximum in both monsoon and post-monsoon season but relatively low in pre-monsoon and winter. The higher mean AOD anomaly value is found to be 0.2 over eastern Pakistan and western India. In northeastern Pakistan and central India, AOD and RH are positively correlated (R ˃0.54) while negatively correlated over southwestern Pakistan. AOD is negatively correlated (R= ~-0.3) with EVI over northeastern and southeastern Pakistan. The correlation coefficient (R) obtained among Aqua and Terra AOD is 0.97 over south Asia. The satellite observations of Aqua-AOD was also compared with SeaWiFS and MISR AOD.

Komparasi Luas Tutupan Lahan di Kota Bandar Lampung Berdasarkan Algoritma NDVI (Normalized Difference Vegetation Index) dan EVI (Enhanced Vegetation Index)

Lahan adalah salah satu sumber daya alam penting yang sangat dibutuhkan oleh makhluk hidup baik hewan, tumbuhan, dan manusia untuk berpijak, sebagai tempat hidup serta melakukan kegiatan kehidupan serta untuk memenuhi kebutuhannya. Ibukota Provinsi Lampung menjadi lokasi penelitian ini yaitu Kota Bandar Lampung. Untuk menentukan luasan wilayah tutupan lahan salah satunya yaitu dengan cara mengidentifikasi kumpulan vegetasi. Vegetasi yang bervariatif bisa disebabkan karena bermacam-macam faktor, yaitu penyebaran tumbuhan, iklim dan jenis tanah. Vegetasi yang merupakan penyusunan lahan memiliki jenis yang beraneka ragam sehingga akan memiliki kelas kerapatan vegetasi berbeda untuk setiap daerah. Beberapa metode citra satelit telah dikembangkan untuk mengestimasi wilayah tutupan lahan di suatu daerah, antara lain metode Algoritma NDVI dan EVI yang dipakai pada penelitian kali ini. Penelitian menunjukkan perbedaan hasil antara NDVI dan EVI, di mana metode EVI lebih baik dalam mengidentifikasi sebuah vegetasi. Hasil klasifikasi atau kerapatan vegetasi Kota Bandar Lampung bahwa vegetasinya sudah kurang yang baik dikarenakan untuk wilayah non vegetasi yang sudah tinggi dibandingkan dengan daerah vegetasi tinggi. Dengan begitu daerah yang bukan vegetasi atau non vegetasi mungkin dapat diciptakan vertical garden (taman vertikal) untuk menjaga vegetasi tumbuhan.