Medium Spatial Resolution Satellite Imagery to Estimate Gross Primary Production in an Urban Area

As-syakur, Abd. Rahman; Osawa, Takahiro; Adnyana, I Wayan Sandi

doi:10.3390/rs2061496

Cited by 23 publications

(17 citation statements)

References 21 publications

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“…Previously, Ciu et al [97] tried to compare GPP-based VPM estimates (using MODIS data) and MODIS GPP data products (MOD17A2) with solar-induced chlorophyll fluorescence (SIF) over the most populous megacity area with better results compared to those produced with the MOD17A2 product. On the other hand, the total annual GPP estimated using the VPM in the current study was not very different from the GPP estimate produced by other LUE models or from the MODIS GPP product, as presented by As-syakur et al [46] in the same location. This study can be used as an initial source of information related to land use changes and their impact on terrestrial carbon uptake by vegetation.…”

Section: Annual Changes In Gppsupporting

confidence: 51%

“…In general, the rainfall patterns of the Denpasar area are influenced by monsoons, with the maximum amount of precipitation occurring during the peak of the wet season from December to February and decreasing to a minimum during the valley of the dry season from June to August [44,45]. Moreover, the annual average temperature of Denpasar is 27.6 • C, the average annual rainfall is 1803.62 mm year −1 [44], and settlement was the dominant land use in Denpasar city in 2006 followed by rice field areas [46].…”

Section: Research Locationmentioning

confidence: 99%

“…In addition, the use of 30-m Landsat data can cause errors in detecting vegetation in heterogeneous settlement areas. As shown by As-syakur et al [46], different spatial resolutions in the satellite data used for analyses of GPP in urban areas will lead to different averages for settlement areas, and more detailed spatial resolutions will give larger average values. Increased pixel sizes (or decreased spatial resolution) result in the loss of image detail [95].…”

Section: Annual Changes In Gppmentioning

confidence: 99%

See 2 more Smart Citations

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Nuarsa

As-syakur

Gunadi

et al. 2018

IJGI

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Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and Landsat 8 and meteorological data are used to calculate annual GPP for 1995, 2003 and 2014, respectively, using the vegetation production model (VPM) in the tourism city Denpasar, Bali, Indonesia. Five land use types generated from topographic maps in three different years over the past two decades are used to quantify the impacts of land use changes on GPP estimation values. Analysis was performed for two periods to determine changes in land use and GPP value as well as their speed. The results demonstrated that urban land development, namely, the increase of settlement areas due to tourism activity, had overall negative effects on terrestrial GPP. The total GPP of the whole area decreased by 7793.96 tC year −1 (12.65%) during the study period. The decline is due to the conversion of agriculture and grassland area into settlements, which caused the city to lose half of its ability to uptake carbon through vegetation. However, although forest area is declining, forest maintenance and restoration by making them protection areas has been helpful in preventing a drastic decline in GPP value over the past two decades. This study provides information that is useful for carbon resource management, tourism, policy making and scholars concerned about carbon reduction in a tourism city.

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Section: Annual Changes In Gppsupporting

confidence: 51%

Section: Research Locationmentioning

confidence: 99%

Section: Annual Changes In Gppmentioning

confidence: 99%

See 1 more Smart Citation

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Nuarsa

As-syakur

Gunadi

et al. 2018

IJGI

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“…Additionally, the NDVI value for soil surfaces (when fAPAR = 0) derived from this relationship (i.e., 0.22) was closer to that estimated with measured soil reflectances (i.e., 0.31) than if using relationships in the literature (e.g., 0.048 [32]; 0.087 [41]). This also suggests that it may not be appropriate to assign model parameters defined for other vegetation types (e.g., irrigated agricultural crops) to suburban turfgrass or other urban vegetation [43].…”

Section: Turfgrass Net Primary Productionmentioning

confidence: 99%

Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery

Bauer

2012

Remote Sensing

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Vegetation is a basic component of urban-suburban environments with significant area coverage. As a major vegetation type in US cities, urban turfgrass provides a range of important ecological services. This study examined the biological carbon fixation of turfgrass in a typical residential neighborhood by linking ground-based measurements, high resolution satellite remote sensing, and ecological modeling. The spatial distribution of turfgrass and its vegetative conditions were mapped with QuickBird satellite imagery. The significant amount of shadows existing in the imagery were detected and removed by taking advantage of the high radiometric resolution of the data. A remote sensing-driven production efficiency model was developed and parameterized with field biophysical measurements to estimate annual net primary production of turfgrass. The results indicated that turfgrass accounted for 38% of land cover in the study area. Turfgrass assimilated 0-1,301 g·C·m ). However, lawn grass contributed more to the total net primary production than golf course grass due to its larger area coverage, although with higher spatial variability.

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“…It is a key parameter of LUE-based models for modeling the vegetation productivity at regional to global scales [2][3][4][5], and is considered a constant, rather than a variable for certain vegetation types or even entire eco-regions. However, the maximal LUE currently used in these models still gives rise to extensive controversy [6], especially in the mountain area which is normally covered by high heterogeneous vegetation.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Maximal Light Use Efficiency for Different Vegetation through the CASA Model Combined with Time-Series Remote Sensing Data and Ground Measurements

Bian

Lei

et al. 2012

Remote Sensing

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Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.

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Medium Spatial Resolution Satellite Imagery to Estimate Gross Primary Production in an Urban Area

Cited by 23 publications

References 21 publications

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery

Estimating the Maximal Light Use Efficiency for Different Vegetation through the CASA Model Combined with Time-Series Remote Sensing Data and Ground Measurements

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