Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area

Cao, Xin; Chen, Jin; Matsushita, Bunkei; Imura, Hidefumi

doi:10.1080/01431160903475274

Cited by 69 publications

(40 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these methods, based on the hyperspectral sensors, face a great challenge for land degradation surveillance due to the shortage of data acquisition ability for large regions. Considering multispectral sensors, some spectral indexes sensitive to NPV-such as the normalized difference senescent vegetation index (NDSVI) [11], a ratio of moderate resolution imaging spectrometer (MODIS) bands 7 and 6 [1], and the dead fuel index (DFI) [12] -were proposed in different environments. However, this approach is area-specific, and not well validated in other environments.…”

Section: Discussionmentioning

confidence: 99%

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

et al. 2017

Data

View full text Add to dashboard Cite

Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the study area, and utilized the linear spectral mixture model for generating the fractional cover of PV, NPV, and bare soil, with endmember spectra retrieved from the field measured endmember spectral library, SummaryFractional cover of vegetation plays a key role in resisting wind and water erosion, thus it has been widely used as the indicator for land degradation monitoring and assessment. From a functional perspective, vegetation can be categorized as photosynthetic (green leaves) and non-photosynthetic (wood, senescent material, and litter) material [1]. Unlike the well-developed multiple datasets of the fractional cover of PV, the datasets of the fractional cover of NPV are relatively rare, mainly because of the difficulty in differentiating NPV from the soil background, particularly when multispectral sensors are considered. However, NPV is common and widely distributed in the drylands due to the scarce, variable rainfall and low soil fertility. Therefore, simultaneously acquiring the fractional cover of PV and NPV would provide new insights for land degradation surveillance and land management.In this context, an archive of fractional cover of PV and NPV was computed for the Beijing and Tianjin Sandstorm Source Region (BTSSR), with monthly temporal resolution covering 2001

show abstract

Section: Discussionmentioning

confidence: 99%

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

et al. 2017

Data

View full text Add to dashboard Cite

show abstract

“…He et al [2006] therefore proposed the litter-corrected ATSAVI (L-ATSAVI) to minimize litter influence by incorporating cellulose absorption index (CAI) in the ATSAVI. The CAI was developed by Daughtry et al [1996] to estimate crop residue coverage based on lignocellulose absorption feature near 2100 nm [Daughtry et al, 2004[Daughtry et al, , 2005[Daughtry et al, , 2006, and was also found effective for estimating litter coverage and litter mass in grassland [Cao et al 2010;Ren and Zhou, 2012]. Results of He et al [2006] showed that, the L-ATSAVI improved leaf area index estimation accuracy in a mixed grassland ecosystem by about 10%.…”

Section: Introductionmentioning

confidence: 99%

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Ren

Zhou

2014

European Journal of Remote Sensing

View full text Add to dashboard Cite

Accurate estimation of green aboveground biomass in arid and semiarid grassland is essential for a variety of studies, such as sustainable grassland management, fire risk assessment, climate change, and environmental degradation. A great need exists for the establishment of robust method for estimating green aboveground biomass in arid and semiarid grassland due to the influences of soil background and litter. In the study, a new index (litter-soil-adjusted vegetation index, L-SAVI) was proposed to estimate green aboveground biomass in arid and semiarid grassland. The L-SAVI was also evaluated based on biomass and spectra in situ measurements in the desert steppe of Inner Mongolia. Results showed that, the performance of the new index was better than that of NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MSAVI (modified soil-adjusted vegetation index), OSAVI (optimised soil-adjusted vegetation index), TSAVI (transformed soil-adjusted vegetation index), ATSAVI (adjusted transformed soil-adjusted vegetation index), PVI (perpendicular vegetation index), GSAVI (green-adjusted vegetation index), and L-ATSAVI (litter-corrected ATSAVI) in our study site. The logic behind the L-SAVI was to enable the SAVI to be less sensitive to litter by incorporating the CAI (cellulose absorption index) in the SAVI. In conclusion, the L-SAVI is a suitable predictor for complementing existing vegetation indices on green aboveground biomass estimation in arid and semiarid grassland.

show abstract

“…Different objectives were pursued such as assessment of soil tillage intensity and soil conservation [38,46,47], evaluation of soil erosion risk and runoff [48][49][50][51], evaluation of the risk of wildfire in relation to dead fuel proportion [41,52,53], and improvement in land cover mapping [39,54]. Recently the study of Jacques et al [33], demonstrated the value of SWIR (Short Wave Infra Red) MODIS bands to monitor dry vegetation in a Sahelian region.…”

Section: Rationalementioning

confidence: 99%

A Dynamic Vegetation Senescence Indicator for Near-Real-Time Desert Locust Habitat Monitoring with MODIS

et al. 2015

View full text Add to dashboard Cite

Desert locusts (Schistocerca gregaria) represent a major threat for agro-pastoral resources and food security over almost 30 million km 2 from northern Africa to the Arabian peninsula and India. Given the differential food preferences of this insect pest and the extent and remoteness of the their distribution area, near-real-time remotely-sensed information on potential habitats support control operations by narrowing down field surveys to areas favorable for their development and prone to gregarization and outbreaks. The development of dynamic greenness maps, which detect the onset of photosynthetic vegetation, allowed national control centers to identify potential habitats to survey, as locusts prefer green and fresh vegetation. Their successful integration into the daily control operations led to a new need: the near-real-time identification of the onset of dryness, a synonym for the loss of habitat attractiveness, likely to be abandoned by locusts. The timely availability of this information would enable control centers to focus their surveys on areas more prone to gregarization, leading to more efficiency in the allocation of resources and in decision making. In this context, this work developed an original method to detect in near-real-time Remote Sens. 2015, 7 7546 the onset of vegetation senescence. The design of the detection relies on the temporal behavior of two indices: the Normalized Difference Vegetation Index, depending on the green vegetation, and the Normalized Difference Tillage Index, sensitive to both green and dry vegetation. The method is demonstrated in Mauritania, an ever-affected country, with 10-day MODIS mean composites for the years 2010 and 2011. The discrimination performance of three classes ("growth", "density reduction" and "drying") were analyzed for three classification methods: maximum likelihood (61.4% of overall accuracy), decision tree (71.5%) and support vector machine (72.3%). The classification accuracy is heterogeneous in both time and space and is affected by several factors, such as vegetation density, the north-south climatic gradient and the relief. Smoothing the vegetation time series resulted in an increase of the overall accuracy of about 5% at the expense of a loss in timeliness of ten days. To simulate near-real-time monitoring conditions, the decision tree was applied to the decade of 2010. Overall, the seasonal vegetation cycle appeared clear and consistent. The results obtained pave the way for an operational implementation of the senescence dynamic mapping and, consequently, to further strengthen the capacity of the locust control management.

show abstract

Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area

Cited by 69 publications

References 25 publications

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

A Dynamic Vegetation Senescence Indicator for Near-Real-Time Desert Locust Habitat Monitoring with MODIS

Contact Info

Product

Resources

About