Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series

Martínez, Beatriz; Sánchez-Ruíz, Sergio; Campos-Taberner, Manuel; Garcı́a-Haro, Francisco Javier

doi:10.3390/rs14061310

Cited by 16 publications

(7 citation statements)

References 60 publications

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“…Through comparative analysis, we found that our MRCNN downscaling algorithm produced results that aligned significantly more with the MODIS NDVI for the selected five features. This finding suggests that our algorithm can more accurately represent the true characteristics of these features, providing substantial practical significance [92][93][94]. When we conducted individual examinations of each year for the five features as discrete intervals, our method demonstrated marginally superior correspondence with MODIS NDVI compared to the other four methods, despite overall improvements.…”

Section: B Comparison With Previous Studiesmentioning

confidence: 90%

A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network

Sun,

Zhao,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring vegetation dynamics is essential for ecological processes, environmental changes, and natural resource protection. Fine-scale representation of vegetation indices is necessary for regions with complex topography and high species diversity. However, the advanced very-high-resolution radiometer (AVHRR), which covers a large time range with high temporal resolution, does not provide normalized difference vegetation index (NDVI) data with sufficient spatial resolutions for a detailed analysis of vegetation changes. While the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a higher temporal and spatial resolution, is only limited to the last few decades. In order to deal with these isues, we propose an approach called Multi-scale Residual Convolutional Neural Net-work (MRCNN) that utilizes a multi-scale structure together with and residual convolutional neural network to combine MODIS NDVI and AVHRR NDVI data. The MRCNN algorithm improved Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) by 0.026 and 0.032, respectively, resulting in a 64.38% improvement for MAE and 62.79% improvement for RMSE compared to AVHRR NDVI. It also increased the Peak-Signal-to-Noise Ratio (PSNR) by 28.5% and the Structural Similarity index (SSIM) by 16.2% . The MRCNN method accurately captures the actual state of MODIS NDVI and consistently tracks changing trends in the vegetation index. It is exact in complex terrain and diverse vegetation areas. This method enhances the spatial resolution of AVHRR NDVI and significantly improves the accuracy of monitoring nationwide vegetation index changes over 30 years. The findings establish a solid scientific foundation for implementing ecological conservation measures and promoting sustainable vegetation growth.

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Section: B Comparison With Previous Studiesmentioning

confidence: 90%

A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network

Sun,

Zhao,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Concurrently, escalating human activities, such as uncontrolled resource extraction, human-induced forest fires, the invasion of alien species, and forest clearance due to population expansion, exert considerable adverse effects on forest ecosystems [13,14]. Research on the global carbon cycle, especially in the context of climate change and human activities, has become a core issue in the field of environmental science [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al referred to the drought-stress improved-EC-LUE model to estimate the spatiotemporal distribution pattern of GPP in the Mao bamboo forest of Anji City [28]. Martínez et al applied the GLO-PEM model to estimate the net primary productivity of the Iberian Peninsula for 15 years and studied its spatial and temporal variations and the quantitative relationship between NPP, precipitation, and air temperature [21]. Although many scientific studies have used light use efficiency models to study vegetation productivity, currently most LUE models directly simulate the GPP, with only the CASA and GLO-PEM models capable of directly simulating the NPP [23].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Distribution and Driving Effects of Net Primary Productivity along an Elevation Gradient in Subtropical Regions of China

Xu,

Feng,

Chen

et al. 2024

Forests

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Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle.

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“…From the research content, there were long-term NDVI temporal and spatial evolution and future sustainability analysis [20,21], the detection of driving factors [20], and the correlation analysis of NDVI, terrain, climate, and other elements [17,22], as well as mutation analysis of vegetation NDVI [23]. The commonly used research methods mainly include the linear regression analysis method [24,25], correlation analysis method [17,21,26], trend analysis method combining Theil-Sen slope and Mann-Kendall test [7,27,28], and Hurst exponent method [21,29]. Typical studies using these methods are as follows: Zhang et al detected the long-term trends and abrupt changes of urbanized area by the Theil-Sen estimator and Mann-Kendall test [30], Yan et al employed the Mann-Kendall test to test the significance of LAI [31], Tehrani et al used the Mann-Kendall test to perform a correlation analysis of two months during the epidemic of COVID-19 [32], etc.…”

Section: Introductionmentioning

confidence: 99%

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Zhong

Wang

et al. 2022

Remote Sensing

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Vegetation is the main body of the terrestrial ecosystem and is a significant indicator of environmental changes in the regional ecosystem. As an essential link connecting South Asia and Southeast Asia, the Lancang-Mekong River Basin(LMRB) can provide essential data support and a decision-making basis for the assessment of terrestrial ecosystem environmental changes and the research and management of hydrology and water resources in the basin by monitoring changes in its vegetation cover. This study takes the Lancang-Mekong River Basin as the study area, and employs the Sen slope estimation, Mann–Kendall test, and Hurst exponent based on the MODIS NDVI data from 2000 to 2021 to study the spatial and temporal evolution trend and future sustainability of its NDVI. Besides, the nonlinear characteristics such as mutation type and mutation year are detected and analyzed using the BFAST01 method. Results demonstrated that: (1) In the past 22 years, the NDVI of the Lancang-Mekong River Basin generally exhibited a fluctuating upward trend, and the NDVI value in 2021 was the largest, which was 0.825, showing an increase of 4.29% compared with 2000. However, the increase rate was different: China has the most considerable NDVI growth rate of 7.25%, followed by Thailand with an increase of 7.21%, Myanmar and Laos as the third, while Cambodia and Vietnam have relatively stable vegetation changes. The overall performance of NDVI is high in the south and low in the north, and is dominated by high and relatively high vegetation coverage, of which the area with vegetation coverage exceeding 0.8 accounts for 62%. (2) The Sen-MK trend showed that from 2000 to 2021, the area where the vegetation coverage in the basin showed a trend of increase and decrease accounted for 66.59% and 18.88%, respectively. The Hurst exponent indicated that the areas where NDVI will continue to increase, decrease, and remain unchanged in the future account for 60.14%, 25.29%, and 14.53%, respectively, and the future development trend of NDVI is uncertain, accounting for 0.04%. Thus, more attention should be paid to areas with a descending future development trend. (3) BFAST01 detected eight NDVI mutation types in the Lancang-Mekong River Basin over the past 22 years. The mutations mainly occurred in 2002–2018, while 2002–2004 and 2014–2018 were the most frequent periods of breakpoints. The mutation type of “interruption: increase with negative break” was changed the most during this period, which accounts for 36.54%, and the smallest was “monotonic decrease (with negative break)”, which only accounts for 0.65%. This research demonstrates that combining the conventional trend analysis method with the BFAST mutation test can more accurately analyze the spatiotemporal variation and nonlinear mutation of NDVI, thus providing a scientific reference to develop ecological environment-related work.

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Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series

Cited by 16 publications

References 60 publications

A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network

A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network

Assessing the Distribution and Driving Effects of Net Primary Productivity along an Elevation Gradient in Subtropical Regions of China

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

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