Evaluation of modelled net primary production using MODIS and landsat satellite data fusion

Jay, Steven C.; Potter, Christopher; Crabtree, Robert H.; Genovese, Vanessa; Weiss, Daniel J.; Kraft, Maggi

doi:10.1186/s13021-016-0049-6

Cited by 22 publications

(15 citation statements)

References 35 publications

(49 reference statements)

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“…Similarly, the MOD17 model utilizes a categorical land cover classification to apply biome specific parameters to productivity estimates. Sub-pixel land cover dynamics or land cover changes are not accounted for [18]. To address these limitations, we incorporated a new continuous rangeland plant functional type (PFT) percent cover dataset, developed for western United States rangelands [19], into the MOD17 framework.…”

Section: Introductionmentioning

confidence: 99%

Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types

et al. 2019

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Understanding and monitoring the dynamics of rangeland heterogeneity through time and across space is critical for the effective management and conservation of rangeland systems and the sustained supply of the ecosystem goods and services they provide. Conventional approaches (both field-based and remote sensing) to monitoring rangeland productivity fail to effectively capture important aspects of this heterogeneity. While field methods can effectively capture high levels of detail at fine spatial and temporal resolutions, they are limited in their applicability and scalability to larger spatial extents and longer time periods. Alternatively, remote sensing based approaches that scale broad spatiotemporal extents simplify important heterogeneity occurring at fine scales. We address these limitations to monitoring rangeland productivity by combining a continuous plant functional type (PFT) fractional cover dataset with a Landsat derived gross primary production (GPP) and net primary production (NPP) model. Integrating the annual PFT dataset with a 16-day Landsat normalized difference vegetation (NDVI) composite dataset enabled us to disaggregate the pixel level NDVI values to the sub-pixel PFTs. These values were incorporated into the productivity algorithm, enabling refined estimations of 16-day GPP and annual NPP for the PFTs that composed each pixel. We demonstrated the results of these methods on a set of representative rangeland sites across the western United States. Partitioning rangeland productivity to sub-pixel PFTs revealed new dynamics and insights to aid the sustainable management of rangelands.

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Section: Introductionmentioning

confidence: 99%

Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types

et al. 2019

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“…Since direct field measurements are time-consuming and costly, models based on carbon cycle and plant structures (e.g., Biogeochemical Cycles Model (BIOME-BGC) (Kimball et al, 1997) and Dynamic Global Phytogeography Model (DOLY) (Donmez et al, 2011)), or remote sensing models based on satellite imagery (e.g., Global Production Efficiency Model (GLO-PEM) (Goetz et al, 1999), Carnegie Ames Stanford Approach (CASA) (Biondini et al, 1998;Liang et al, 2015;Bao et al, 2016;Jay et al, 2016), Simple Diagnostic Biosphere Model (SDBM) (Kaminski et al, 2002), Simple Biosphere Model (SIB) (Lokupitiya et al, 2009), and Terrestrial Uptake and Release of Carbon (TURC) model (Xia et al, 2013)), are generally used to estimate the spatial and temporal changes in NPP.…”

Section: Introductionmentioning

confidence: 99%

Estimation of spatial and temporal changes in net primary production based on Carnegie Ames Stanford Approach (CASA) model in semi-arid rangelands of Semirom County, Iran

et al. 2019

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Net primary production (NPP) is an indicator of rangeland ecosystem function. This research assessed the potential of the Carnegie Ames Stanford Approach (CASA) model for estimating NPP and its spatial and temporal changes in semi-arid rangelands of Semirom County, Iran. Using CASA model, we estimated the NPP values based on monthly climate data and the normalized difference vegetation index (NDVI) obtained from the MODIS sensor. Regression analysis was then applied to compare the estimated production data with observed production data. The spatial and temporal changes in NPP and light utilization efficiency (LUE) were investigated in different rangeland vegetation types. The standardized precipitation index (SPI) was also calculated at different time scales and the correlation of SPI with NPP changes was determined. The results indicated that the estimated NPP values varied from 0.00 to 74.48 g C/(m 2 •a). The observed and estimated NPP values had different correlations, depending on rangeland conditions and vegetation types. The highest and lowest correlations were respectively observed in Astragalus spp.-Agropyron spp. rangeland (R 2 =0.75) with good condition and Gundelia spp.-Cousinia spp. rangeland (R 2 =0.36) with poor and very poor conditions. The maximum and minimum LUE values were found in Astragalus spp.-Agropyron spp. rangeland (0.117 g C/MJ) with good condition and annual grassesannual forbs rangeland (0.010 g C/MJ), respectively. According to the correlations between SPI and NPP changes, the effects of drought periods on NPP depended on vegetation types and rangeland conditions. Annual plants had the highest drought sensitivity while shrubs exhibited the lowest drought sensitivity. The positive effects of wet periods on NPP were less evident in degraded areas where the destructive effects of drought were more prominent. Therefore, determining vegetation types and rangeland conditions is essential in NPP estimation. The findings of this study confirmed the potential of the CASA for estimating rangeland production. Therefore, the model output maps can be used to evaluate, monitor and optimize rangeland management in semi-arid rangelands of Iran where MODIS NPP products are not available.

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“…High to very high SR [94], coarse to medium SR [95], hyperspectral [96] In situ activity sensors [97] Nutrient retention 5, 8, 14 Hyperspectral [96] Disturbance regime 5, 7, 9, 10, 11, 14, 15 High to very high SR [98], coarse to medium SR [99] In situ activity sensors [100] Ecosystem Structure…”

Section: Operationalising Ebvs With State-of-the-art Technologiesmentioning

confidence: 99%

Biodiversity Monitoring in Changing Tropical Forests: A Review of Approaches and New Opportunities

Mulatu

Mora²,

Kooistra

et al. 2017

Remote Sensing

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Tropical forests host at least two-thirds of the world's flora and fauna diversity and store 25% of the terrestrial above and belowground carbon. However, biodiversity decline due to deforestation and forest degradation of tropical forest is increasing at an alarming rate. Biodiversity dynamics due to natural and anthropogenic disturbances are mainly monitored using established field survey approaches. However, such approaches appear to fall short at addressing complex disturbance factors and responses. We argue that the integration of state-of-the-art monitoring approaches can improve the detection of subtle biodiversity disturbances and responses in changing tropical forests, which are often data-poor. We assess the state-of-the-art technologies used to monitor biodiversity dynamics of changing tropical forests, and how their potential integration can increase the detail and accuracy of biodiversity monitoring. Moreover, the relevance of these biodiversity monitoring techniques in support of the UNCBD Aichi targets was explored using the Essential Biodiversity Variables (EBVs) as a framework. Our review indicates that although established field surveys were generally the dominant monitoring systems employed, the temporal trend of monitoring approaches indicates the increasing application of remote sensing and in-situ sensors in detecting disturbances related to agricultural activities, logging, hunting and infrastructure. The relevance of new technologies (i.e., remote sensing, in situ sensors, and DNA barcoding) in operationalising EBVs (especially towards the ecosystem structure, ecosystem function, and species population classes) and the Aichi targets has been assessed. Remote sensing application is limited for EBV classes such as genetic composition and species traits but was found most suitable for ecosystem structure class. The complementarity of remote sensing and emerging technologies were shown in relation to EBV candidates such as species distribution, net primary productivity, and habitat structure. We also developed a framework based on the primary biodiversity attributes, which indicated the potential of integration between monitoring approaches. In situ sensors are suitable to help measure biodiversity composition, while approaches based on remote sensing are powerful for addressing structural and functional biodiversity attributes. We conclude that, synergy between the recent biodiversity monitoring approaches is important and possible. However, testing the suitability of monitoring methods across scales, integrating heterogeneous monitoring technologies, setting up metadata standards, and making interpolation and/or extrapolation from observation at different scales is still required to design a robust biodiversity monitoring system that can contribute to effective conservation measures.

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Evaluation of modelled net primary production using MODIS and landsat satellite data fusion

Cited by 22 publications

References 35 publications

Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types

Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types

Estimation of spatial and temporal changes in net primary production based on Carnegie Ames Stanford Approach (CASA) model in semi-arid rangelands of Semirom County, Iran

Biodiversity Monitoring in Changing Tropical Forests: A Review of Approaches and New Opportunities

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