The Impact of Natural Factors on Net Primary Productivity in Heilongjiang Province Under Different Land Use and Land Cover Changes
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. LUCC and NPP
2.2.2. Impact Factor Dataset
2.3. Methods
2.3.1. Identification of Study Land Use Types
2.3.2. Spatiotemporal Trends in NPP Changes
2.3.3. Detection of Impact Factors on NPP
2.4. Research Workflow Chart
3. Results
3.1. Determination of Study Land Types
3.2. Spatiotemporal Distribution of NPP and Differences Across Land Types
3.2.1. Geospatial Dynamics in Multiyear NPP Averages and Land-Type Heterogeneity
3.2.2. Geospatial Variability in NPP Transition Dynamics and Inter-Category Disparities
3.3. Analysis of Factors Influencing NPP Across Land Categories
3.3.1. Assessment of the Separate Influence of Natural Factors on NPP
3.3.2. Analysis of the Interactive Effects of Natural Factors on NPP
4. Discussion
4.1. Land Use Change Analysis
4.2. Analysis of NPP Change Trends
4.3. Response of NPP Changes to Natural Factors
4.4. Limitations and Future Prospects
5. Conclusions
- (1)
- NPP showed clear spatial heterogeneity across land use types. Forest areas—the dominant ecosystem—exhibited the highest mean annual NPP (514.05 g C·m−2·a−1), followed by croplands (359.01 g C·m−2·a−1), while shrub–grass–other areas had the lowest (269.18 g C·m−2·a−1). Among changing land types, forest- and cropland-derived categories showed relatively high NPP, whereas secondary cropland remained the lowest.
- (2)
- Over the study period, vegetation NPP across study area displayed a significant increasing trend. This was especially evident in areas where land use remained stable, underscoring the positive effect of land use consistency on ecosystem carbon sequestration.
- (3)
- Among natural factors, mean annual temperature, precipitation, soil water content, evapotranspiration, and elevation all have significant impacts on variations in vegetation NPP. Specifically, the interaction between mean annual precipitation and mean annual temperature exhibits strong explanatory power across multiple land use types, indicating that the synergistic effect of hydrothermal conditions is a key factor driving changes in vegetation productivity. Additionally, elevation often interacts with other factors to jointly influence NPP, highlighting the importance of topography in regulating the functions of regional ecosystems.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Value | Name | Description |
---|---|---|
1 | Evergreen Needleleaf Forests | Dominated by evergreen conifer trees (canopy > 2 m). Tree cover >60%. |
2 | Evergreen Broadleaf Forests | Dominated by evergreen broadleaf and palmate trees (canopy > 2 m). Tree cover >60%. |
3 | Deciduous Needleleaf Forests | Dominated by deciduous needleleaf (larch) trees (canopy > 2 m). Tree cover >60%. |
4 | Deciduous Broadleaf Forests | Dominated by deciduous broadleaf trees (canopy > 2 m). Tree cover >60%. |
5 | Mixed Forests | Dominated by neither deciduous nor evergreen (40–60% of each) tree type (canopy > 2 m). Tree cover >60%. |
6 | Closed Shrublands | Dominated by woody perennials (1–2 m height) > 60% cover. |
7 | Open Shrublands | Dominated by woody perennials (1–2 m height) 10–60% cover. |
8 | Woody Savannas | Tree cover 30–60% (canopy > 2 m). |
9 | Savannas | Tree cover 10–30% (canopy > 2 m). |
10 | Grasslands | Dominated by herbaceous annuals (<2 m). |
11 | Permanent Wetlands | Permanently inundated lands with 30–60% water cover and >10% vegetated cover. |
12 | Croplands | At least 60% of area is cultivated cropland. |
13 | Urban and Built-up Lands | At least 30% impervious surface area including building materials, asphalt, and vehicles. |
14 | Cropland/Natural Vegetation Mosaics | Mosaics of small-scale cultivation 40–60% with natural tree, shrub, or herbaceous vegetation. |
15 | Permanent Snow and Ice | At least 60% of area is covered by snow and ice for at least 10 months of the year. |
16 | Barren | At least 60% of area is non-vegetated barren (sand, rock, soil) areas with less than 10% vegetation. |
17 | Water Bodies | At least 60% of area is covered by permanent water bodies. |
Category | Coding inMCD12Q1 | Recoded Classification in Study |
---|---|---|
Cropland | 12, 14 | 1 |
Forest | 1, 2, 3, 4, 5, 8, 9 | 2 |
SGO | 6, 7, 10, 11 | 3 |
Urban | 13 | 4 |
Bare area | 16 | 5 |
WIS | 15, 17 | 6 |
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Natural Factors | Data Source | Resolution | Unit |
---|---|---|---|
EL | Geospatial Information Authority of Japan (https://globalmaps.github.io/el.html, accessed on 20 August 2025) | 1 km | m |
ET | MODIS/Terra Evapotranspiration Product (https://doi.org/10.5067/MODIS/MOD16A3GF.061) | 500 m | kg/m−2·a−1 |
MAP | National Tibetan Plateau Data Center (https://doi.org/10.5281/zenodo.3185722) | 1 km | mm |
TEMP | National Tibetan Plateau Data Center (https://doi.org/10.5281/zenodo.3185722) | 1 km | °C |
SMC | National Tibetan Plateau Data Center (https://doi.org/10.11888/Terre.tpdc.272415) | 1 km | mm |
Criterion for Discrimination | Type of Interaction | Number |
---|---|---|
q (X1 ∩ X2) < Min [q (X1), q (X2)] | Nonlinear Weakening | I |
Min [q (X1), q (X2)] < q (X1 ∩ X2) < Max [q (X1), q (X2)] | Single-Factor Nonlinear Weakening | II |
q (X1 ∩ X2) > Max [q (X1), q (X2)] | Two-Factor Enhancement | III |
q (X1 ∩ X2) = q (X1) + q (X2) | Independent | IV |
q (X1 ∩ X2) > q (X1) + q (X2) | Nonlinear Enhancement | V |
Land Type | Cropland | Forest | SGO | Urban | WIS | SC | CF | SF | CFC |
---|---|---|---|---|---|---|---|---|---|
Area (km2) | 158,696 | 201,677 | 13,830 | 4305 | 2915 | 16,417 | 13,641 | 3786 | 3019 |
Percentage (%) | 35.08 | 44.59 | 3.06 | 0.95 | 0.64 | 3.63 | 3.02 | 0.83 | 0.67 |
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Li, B.; Jiang, Q.; Zhao, Y.; Wang, Z.; Tao, M.; Qin, Y. The Impact of Natural Factors on Net Primary Productivity in Heilongjiang Province Under Different Land Use and Land Cover Changes. Agronomy 2025, 15, 2304. https://doi.org/10.3390/agronomy15102304
Li B, Jiang Q, Zhao Y, Wang Z, Tao M, Qin Y. The Impact of Natural Factors on Net Primary Productivity in Heilongjiang Province Under Different Land Use and Land Cover Changes. Agronomy. 2025; 15(10):2304. https://doi.org/10.3390/agronomy15102304
Chicago/Turabian StyleLi, Baohan, Qiuxiang Jiang, Youzhu Zhao, Zilong Wang, Meiyun Tao, and Yu Qin. 2025. "The Impact of Natural Factors on Net Primary Productivity in Heilongjiang Province Under Different Land Use and Land Cover Changes" Agronomy 15, no. 10: 2304. https://doi.org/10.3390/agronomy15102304
APA StyleLi, B., Jiang, Q., Zhao, Y., Wang, Z., Tao, M., & Qin, Y. (2025). The Impact of Natural Factors on Net Primary Productivity in Heilongjiang Province Under Different Land Use and Land Cover Changes. Agronomy, 15(10), 2304. https://doi.org/10.3390/agronomy15102304