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Technical Note

Assessment of Habitat Quality in Arid Regions Incorporating Remote Sensing Data and Field Experiments

by
Mingke Zhang
1,2,
Hao Zhang
1,*,
Wei Deng
1,3 and
Quanzhi Yuan
1
1
College of Geography and Resources Science, Sichuan Normal University, Chengdu 610101, China
2
College of Life Science, Sichuan Normal University, Chengdu 610101, China
3
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(19), 3648; https://doi.org/10.3390/rs16193648
Submission received: 19 August 2024 / Revised: 21 September 2024 / Accepted: 26 September 2024 / Published: 29 September 2024
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Abstract

:
China’s arid regions are particularly vulnerable to the adverse effects of climate change and human activities, which pose threats to habitat quality. Consequently, evaluations of these effects are vital for devising ecological strategies and initiating regional remediation efforts. However, environmental variations in arid areas can cause habitat quality fluctuations, which complicates precise assessments. This study introduces a refined methodology that integrates remote sensing data and field survey biomass data to modify the habitat quality estimates obtained from the InVEST model in the Altai region over three decades. A comparative analysis of the unmodified, normalized difference vegetation index (NDVI)-modified and biomass-modified habitat quality estimates was conducted. The results revealed an improvement in the correlation between habitat quality and field observations, with a significant increase in the R2 value from 0.129 to 0.603. The unmodified model exhibits subtle variations in habitat quality in mountainous areas, with a slight decline in the plains. However, the modified model shows an increasing trend in mountainous areas. This finding contrasts with the reductions in mountains typically reported by other studies. The refined approach accurately expresses the variations in habitat quality across different habitat types, with declines in forested areas and improvements in shrubland and grassland regions. This model is suitable for arid regions and accommodates urban and agricultural ecosystems affected by human activities, offering empirical data for biodiversity and habitat management.

1. Introduction

Habitat quality is a crucial component in the evaluation of ecosystem health and the capacity to sustain biodiversity and other ecosystem services [1,2]. A decline in habitat quality can markedly reduce local ecosystem services, including biodiversity, climate regulation, and water conservation [3]. The ecological environment constitutes the fundamental support for economic development, and sustainable development hinges on ensuring ecosystem quality [4,5]. Accurately assessing habitat quality and refining the management of regional ecological systems will bolster the ecosystem’s resilience and capacity to withstand risks [6]. Over the past few decades, the environment in arid regions has suffered from a series of ecological problems due to the growing scale of human activities, including the expansion of urban areas, agricultural lands, and transportation infrastructures [7,8,9]. To prevent continuous habitat degradation and biodiversity decline, investigating habitat quality across various previous and expected contexts and evaluating the accuracy of various models incorporating remote sensing or onsite data are urgently needed.
Habitat quality is the ecosystem’s capacity to furnish suitable conditions for the enduring development and subsistence of individuals and populations [10,11,12]. Current methods for assessing habitat quality are generally divided into two principal types. One approach involves ecological modeling, including the application of models such as the Habitat Suitability Index (HSI) [13,14,15,16], the Social Values for Ecosystem Services (SolVES) [13,15,17], and the Integrated Valuation of Environmental Services and Trade-offs (InVEST) [18,19]. These models are frequently applied to perform comprehensive quantitative evaluations of habitat quality across extensive areas. The other approach involves field investigations, including the direct examination of biodiversity and biomass within the study area, providing essential data for habitat evaluations [20,21,22]. Ecological models frequently overlook the interactions among field elements. Field studies are inadequate for long-term habitat monitoring and are only suitable for small or specific environments.
The InVEST model’s habitat quality module serves as an influential instrument for evaluating habitat quality, offering nuanced insights into biodiversity by integrating habitat suitability with anthropogenic pressures [2,23,24]. The use of the InVEST model to evaluate habitat quality has become a hot research topic in academia due to its few time constraints and excellent data availability [25]. It has the advantages of simple operation and high efficiency, such as less input data volume, a large amount of output data, and spatially continuous results [26,27]. Although they are easier to implement, consume less human cost and natural resources, and are more efficient than methods based on field surveys, the assessment results obtained using this approach are not always highly accurate. Previous studies have aimed to diminish this uncertainty by refining the collection of parameters [28]. However, the majority of these advancements have concentrated on the detrimental impacts of threats, which is attributable to the absence of comprehensive ecosystem field studies, and the assessment’s precision and implementation may be compromised [29,30]. It may not suffice to accurately detect habitat quality changes, especially within the areas with the same land cover type, solely based on the land cover classification data.
In order to accurately evaluate the status of habitat quality, some scholars have incorporated vegetation information into the evaluation system [31,32]. Vegetation affects the habitat selection for many species to a great extent, which can provide a basis for analyzing the suitability of the current habitat [33,34]. The normalized difference vegetation index (NDVI) is widely recognized as a standard metric for vegetation monitoring [35,36,37], providing spatial and temporal data on vegetation distribution and biomass. It is strongly correlated with aboveground biomass, serving as a reliable indicator of habitat quality in various ecosystems [38,39]. Arid and semiarid regions are characterized by heterogeneity at multiple scales due to variations in composition, productivity, and diversity [39,40]. Such spatial variability can impact the reliability of large-scale ecological assessments in these areas [41,42]. Although the NDVI is widely applied for estimating biomass and habitat quality across various ecosystems, its accuracy in arid and semiarid regions is increasingly being questioned [37,43,44]. Consequently, the precision of the NDVI must be confirmed through field-based data analysis, considering its extensive application. Beyond NDVI changes, a comprehensive assessment of habitat quality necessitates the integration of various environmental variables and ecosystem attributes, such as the climate and terrain conditions, structure and the number of species, and the profound impact of human activities [32].
This research utilized a modified habitat quality assessment model that integrates a comprehensive environmental index—encompassing the NDVI, a DEM, and snowmelt data from remote sensing data and biomass data collected through field sampling—with the InVEST-based habitat quality index. The aims of this study are (i) to explore the spatial and temporal patterns of habitat quality in the Altai region from 1990 to 2022 and (ii) to contrast the differences between the initial and corrected InVEST models and verify the accuracy and applicability of biomass data.

2. Materials and Methods

2.1. Study Area

The Altai region is located in Xinjiang, China, bordering Russia, Kazakhstan, and Mongolia, and is spread over 45°00′00″–49°00′45″N and 85°31′36–91°04′23″E (Figure 1). The region is situated in the hinterland of the Eurasian continent, far from the sea, and has a typical cold temperate continental climate. The Altai region features significant variations in precipitation and temperature. The Altai Mountains block moisture from the Atlantic monsoon, resulting in lower temperatures and greater precipitation in the northern mountainous areas. In contrast, the southern plains, influenced by the Gurbantunggut Desert, have limited precipitation and a dry climate. It has complicated and diverse landforms, which are mainly categorized into mountains, hills, plains, and deserts. Owing to the varying elevations of the Altai Mountains from west to east, the elevations of the hilly area and plains progressively increase from west to east. In the Altai region, the Irtysh River Basin dominates the central areas, whereas the southern area encompasses the hilly area and plains along the northern periphery of the Junggar Basin. The Irtysh River, the Ulungur River, and many minor rivers in Jimunai County are the three main sources of surface runoff here, and this area is one of the key regions of international concern for transboundary water security issues. The Altai region has a total extent of 118,000 square kilometers and is governed as one municipality with six districts. As of February 2024, the population of the Altai region was recorded as 668,587 individuals.

2.2. Data Sources

The land cover, NDVI, climate, and DEM data were sourced from publicly accessible datasets. The land cover data, encompassing 44 classifications, were sourced from the National Earth System Science Data Center (HJ-1A/1B, ChinaCover, 30 m resolution, http://gre.geodata.cn (accessed on 25 September 2024)). Road data were specifically derived from this land cover dataset, which utilized a 30 m resolution. The NDVI obtained from the United States National Aeronautics and Space Administration (MOD13Q1 NDVI, 250 m resolution, https://www.earthdata.nasa.gov/eosdis (accessed on 25 September 2024)) was calculated using the maximum value composite technique. The snowmelt dataset was provided by the National Cryosphere Desert Data Center (Monthly snowmelt dataset in China, 1000 m resolution, http://www.ncdc.ac.cn (accessed on 25 September 2024)). Additionally, digital elevation model (DEM) data from the Shuttle Radar Topography Mission with a 90 m × 90 m resolution were used. All the data were resampled to a 1000 m spatial resolution. The average value was used for resampling snowmelt data and DEM data. The maximum was used for resampling NDVI data. The unmodified habitat quality index calculated using land use data and road data (30 m-m resolution) was resampled to 1000 m resolution by the mean method.
To capture the period with the most pronounced seasonal changes in ground vegetation, biomass samples were collected from the main research locations during the spring (May) and summer (June to September) months from 2022 to 2024. The predominant vegetation types in the study area are desert meadows and grasslands, alongside broad-leaved forests, shrubs, and herbaceous swamps located in the river valleys. Our collection strategy focused on the principal habitats, primarily meadow and forest ecosystems (Figure 2). In the forest area, 83 sampling sites were placed in three of the most representative river valley forests (15 sites in the Ulungur River, 61 in the Irtysh River, and 7 in the Jimunai River). For the meadow area, 51 sampling sites were selected (12 sites in the Ulungur River and 39 along the primary flow path of the Irtysh River).

2.3. Methods

The habitat quality in the Altai region was compared across three models in this study: (a) the unmodified habitat quality was calculated using the habitat quality module of the InVEST model, which allows for the overlay of land cover habitat suitability and degradation maps generated by the threat factors; (b) the habitat quality modified by the NDVI was coupled with degradation maps and the suitability obtained from the NDVI [45]; (c) the habitat quality modified by biomass was calculated by combining the comprehensive index and biomass (Figure 3).

2.3.1. Biomass Estimation

In the field survey experiments, we established meadow sites, each measuring 100 × 100 m; within these sites, three randomly located 1 × 1 m plots were designated for sampling (Table 1). The aboveground biomass was harvested by mowing the vegetation to the ground level. For the forest ecosystem, the sampling area spans 30 × 30 m and includes investigations of the canopy, shrub, and herbaceous layers. Within the canopy layer, we measured trees exceeding 1 m in height and with a diameter at breast height greater than 30 cm, recording species, number, height, and crown spread. The leaf area index (LAI) was also recorded for a detailed vegetation analysis. Within the shrub layer, three 5 × 5 m plots were randomly selected to document coverage of the shrubs and saplings. A 30 × 30 cm section of each shrub was collected as biomass using gardening shears in each plot. For the herbaceous layer assessments, three 1 × 1 m plots were established for measurement, and biomass was collected by cutting the vegetation. The total biomass was calculated by gathering individual samples, and the fresh weights of shrubs and grass were immediately measured after sampling. The samples were dried in the laboratory for the measurement of dry weight. Allometric equations were applied to estimate the tree biomass at the sample sites. The standard form of the allometric equation is given by:
Y = aXb
where Y represents biomass or volume, X denotes DBH (diameter at breast height) or tree height, and a and b are empirical constants that are determined through fitting actual measurement data. The equation is derived from prior research [46], selecting values appropriate for the tree species in the study region.

2.3.2. Habitat Quality Assessment

The habitat quality across the Altai region for the period spanning 1990 to 2022 was quantified via the InVEST model. The InVEST model is a widely used and mature model, and the result depends on the interaction between the factors of threat and habitat suitability [47,48,49,50,51]. The influence (itnm) of the threat index t in grid cell m on the habitat in grid cell n was estimated via the following equation:
i t n m = 1 − d n m d t max   if   linear ;
i t n m = exp − 2.99 d t max d n m   if   exponential ,
where dtmax is the maximum impact distance of the threat and dnm is the separation of grid units n from the threat. The degradation (Dxj) was calculated by the relative impacts of the threat r (wr), the effect of the threat over space (irxy), and the degree of sensitivity for each habitat segment to threat r (Sjr):
D n j = ∑ T t = 1 ∑ M r m = 1 w t ∑ T t = 1 w t t m i t n m b n s j t
The habitat quality index (Qnj) was then ascertained through Equation (5) via the suitability (Hj) of each habitat patch and the accumulated deterioration:
Q n j = H j 1 − D n j z D n j z + k z

2.3.3. Original Model Modification

Considering the distribution of the sampling sites across plains, the evaluation of habitat quality was conducted by categorizing the region into plains and mountainous areas. Human influence was lower in the mountainous regions; hence, the results derived from the enhanced methodologies previously applied served as the benchmark for these regions. The comprehensive index (CI) formulated from field data served as a corrective measure tailored for plains.
M Q n j = N D V I m × H j × 1 − D n j z D n j z + k z , in   mountains C I p × H j × 1 − D n j z D n j z + k z , in   plains
We employed a non-replacement random forest model, which is based on the decision tree algorithm, for the correction of habitat suitability. Within this model, anthropogenic and natural factors were weighted, followed by an internal algorithmic analysis to determine the target features for simulation. The mapping relationships between the biomass and environmental factors at each sampling point in the study area were established via a random forest model (Figure 4). Among all the sampling points, 60% were designated training samples, 20% served as testing samples, and the remaining 20% were used for the validation of the habitat quality results.

3. Results

3.1. Interannual Variation in Habitat Quality in Altai Region

Unmodified habitat quality values spanning 1990 to 2022 for the Altai region were compiled (Figure 5), revealing significant spatial heterogeneity. The northern Altai Mountains exhibit the highest habitat quality values, followed by the central areas, which feature moist valleys and seasonal wetlands. Conversely, the predominantly desert southern plains exhibit the lowest values. The habitat quality values for 1990, 2000, 2010, and 2022 were 0.609, 0.606, 0.603, and 0.599, respectively, indicating a modest decline (Figure 5a–d). The values modified by the NDVI increased from 0.414 in 1990 to 0.466 in 2022, reflecting a consistent increase in habitat quality over the study period (Figure 5e–h). In the model validated with biomass data, the values increased modestly from 0.366 in 1990 to 0.378 in 2022, indicating an increasing trend.

3.2. Trends in Habitat Quality by Region and Land Cover

3.2.1. Habitat Quality in Mountainous and Plain Areas

The results indicate that the habitat quality in mountainous areas is generally greater than that in plain regions. The unmodified model showed little variation in habitat quality values, whereas the modified models revealed distinct trends (Figure 6). According to the unmodified model, the habitat quality in mountainous regions remained high, with stable values from 1990 to 2022. In contrast, the habitat quality values in plain areas declined over time. The model incorporating the NDVI data clearly revealed an increasing trend in habitat quality for both the mountainous and plain regions. The biomass-modified model results revealed a modest increase in habitat quality in the mountainous regions and a downward trend in plain areas. Moreover, habitat quality modified by the NDVI in these mountainous areas increased more rapidly than that modified by biomass. In both the mountainous and plain areas, the habitat quality modified by biomass was lower than that of the other two models, indicating a discrepancy. These inconsistencies suggest that the results from the individual InVEST model may not accurately reflect habitat variations in the study area.

3.2.2. Habitat Quality of Each Land Cover Type

The results of the three habitat quality models show significant differences according to land use classification. The values from the unmodified model exhibit minimal variation over time, with forests, water, and meadows having the highest habitat quality, followed by shrubs and croplands, and unused land having the lowest. The model modified with the NDVI shows that the habitat quality values of forests, water, meadows, and shrubs are lower than those of the unmodified model, whereas those of croplands, urban areas, and unused land are greater (Figure 7a). In the model modified with biomass data, the values are lower than those of the unmodified model for all categories except for water (Figure 7b). Between 1990 and 2022, habitat quality modified by the NDVI and by biomass for forests declined, whereas that for shrubs and meadows increased. The habitat quality modified by the NDVI for cropland varied, peaking in 2010 before declining by 2022, but remained higher than both the unmodified habitat quality and the biomass-modified values for these lands. Urban areas showed a gradual increase in both NDVI-modified values and biomass-modified values, with the NDVI-modified values significantly exceeding the biomass-modified values. In water areas, the modified values are relatively high, but the NDVI-modified values are relatively low. Despite a notable decline in the NDVI-modified values and a clear increase in the biomass-modified values in 2022, these values remained below the unmodified values. For unused land, the NDVI-modified values exceed both unmodified values and biomass-modified values overall, with an obvious increase in 2022.

3.3. Validation

In the unmodified results, biomass correlated linearly with habitat quality but failed to reach statistical significance (R2 = 0.129, p > 0.05) (Figure 8a). The model adjusted with the NDVI exhibited a markedly better significant correlation between biomass and habitat quality, reaching significance at the 0.001 level (R2 = 0.499, p < 0.005) (Figure 8b). The model modified with biomass data showed a notably improved fit between the habitat quality and the observed data (R2 = 0.608, p < 0.005) (Figure 8c). These findings demonstrate that the NDVI-modified and biomass-modified methods have greater accuracy in assessing habitat quality within arid and semiarid areas.

4. Discussion

4.1. Dynamics of Habitat Quality

We constructed models to estimate the habitat quality in the Altai region, employing biomass measurements gathered through field investigations. The findings reveal that actual variations in habitat quality differ greatly from the results of the unmodified model (Figure 5). The habitat quality of the Altai region is highly correlated with water availability [50,52]. However, the original model overly relies on land use changes and generally fails to detect changes in habitat quality [45], especially in mountainous areas with weak land use changes (Figure 6). The habitat quality in mountainous regions has gradually improved over time, a result that aligns with the research conducted by Zhang and Fan [53,54]. A primary driver of this occurrence in mountainous areas is the increased water supply from snowmelt, which subsequently impacts the conditions of mountain ecosystems [55]. Additionally, the Altai Mountains, recognized as a key forest-steppe ecological function conservation area outlined by the Ministry of Environmental Protection, have experienced effective ecological preservation efforts in recent years [56]. The gradual decline in habitat quality in plain areas over time can be attributed to the combined effects of human and natural factors. The continuous expansion of the scope of human activities has led to a significant increase in construction land and cropland [57,58]. Several factors characteristic of deserts include the lack of stable water sources, high temperatures, elevated evaporation rates, sparse vegetation, and slow growth patterns of vegetation [59,60].
Between 1990 and 2018, there was a decline in the biodiversity and biomass of forests [61]. Forest ecosystems are often intertwined with grazing lands. The summer grazing activities and harvesting in autumn may negatively influence the growth of juvenile trees within these forests, thereby causing deterioration in the state of forested regions [62]. Saplings in forest ecosystems are unable to grow to maturity, mature trees are prone to death, and tree regeneration is hindered, leading to a gradual decline in the quality of forest habitats (Figure 3). In recent years, the ecological water consumption in the Altai region has shown an increasing trend, with an increase in river water volume during summer. In times of elevated water levels during the flood season, vegetation may experience waterlogging—where roots are left submerged—or complete submergence, rendering the entire plant underwater. The effects of oxygen depletion are swift; the roots of some species begin to perish within hours of encountering anoxic conditions [63,64]. After the flood season, the increase in temperature can further contribute to tree mortality [65]. In recent decades, vegetation coverage in the arid and semiarid regions of northern China has significantly improved. Overall, the habitat quality of grasslands and shrubs has improved. In the Altai region, forage species exhibit rapid growth and renewal [66,67]. With the influence of water conservancy facilities and land use and protection techniques, cropland habitat quality continues to increase [68,69]. Research indicates that the use of more specialized planting techniques and soil conservation strategies can increase the quality of arable land habitats, leading to a gradual improvement in the quality of farmland habitats [70]. The condition of urban green areas serves as an essential metric for assessing enhancements to urban living conditions and fostering high-caliber sustainable urban growth [71]. The urban areas of the Altai region emphasize the development of green spaces and increase habitat quality. Authorities in the Altai region implemented water monitoring and protection measures, significantly reducing pollution and eutrophication and increasing biodiversity. Consequently, the quality of aquatic habitats has increased following a period of gradual decline between 1990 and 2010 [27,72,73].

4.2. Applicability of the Model

The ecosystems in arid regions are increasingly under pressure from habitat degradation. As a result, quantifying and assessing changes in habitat quality is an issue that warrants attention. Previous research has shown that the InVEST model is effective for evaluating habitat quality in regions with limited species monitoring information. Its strength lies in the spatial identification of biodiversity within regions. Nevertheless, the model’s variables are contingent upon experienced judgment, including the identification of ecological stressors and the refinement of pertinent variables [74,75]. The findings concentrate on evaluating the potential for habitat decline caused by human disturbance, neglecting the impact of natural conditions in the actual area on habitat quality. Therefore, it cannot accurately evaluate habitat quality from a spatiotemporal perspective [30,76]. This is a common limitation in the majority of habitat quality assessment studies. Following the implementation of LULC modeling, the models fail to incorporate modifications based on the intrinsic factors of the habitat. By integrating habitat degradation with the NDVI, the results can effectively reflect the variations in habitat quality caused by vegetation recovery and the threats posed by human activities. However, different habitat types may respond differently to the NDVI, and neglecting the objective sensitivity of habitats to the NDVI can lead to errors in estimating habitat quality. Therefore, as our results indicate, this method may overestimate or underestimate the habitat quality in areas with high or low vegetation cover.
In this study, we leveraged biomass data derived from onsite surveys to verify the habitat quality and its variations in the Altai region from 1900 to 2022. While this methodology facilitates a more diverse and dynamic assessment of habitat quality, this approach comes with its own set of constraints. The analysis at the regional scale can serve only as a reference for the spatiotemporal relationship of habitat quality in arid regions. Furthermore, the landscape index at the type level indicates that the area index of biomass has different levels.

5. Conclusions

The combination of field experiment data and remote sensing data was applied to modify the InVEST model to quantify habitat quality changes in the Altai region over the last two decades, and a research framework was provided for more accurate habitat quality assessment at a large scale. The results indicated that snowmelt, DEM, NDVI, and human activity data (mainly reflected by changes in croplands and ditches) reflect impacts on the habitats in the Altai region. The results of the InVEST model were unable to accurately measure the changes in habitat quality, and the habitat quality modified by the NDVI could not reflect the values of specific habitats. The results modified with biomass data showed that the habitat quality significantly improved in the shrubland and grassland and declined in the forest. In addition, the habitat qualities of cropland, urban areas, and water bodies were determined. These findings can be used to assess the quality of different ecosystems and develop optimal adaptive ecological protection strategies in arid areas.

Author Contributions

M.Z., H.Z. and W.D. conceived of the main idea and designed and performed the experiments. H.Z. made contributions to the pan-sharpening model. W.D. and H.Z. made contributions to the comprehensive index models. M.Z. and Q.Y. organized and conducted field experiments The manuscript was written by M.Z. and H.Z. and was improved by the contributions of all of the co-authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The Third Xinjiang Scientific Expedition [grant numbers 2021xjkk070204].

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, W.; Gu, T.; Xiang, J.; Luo, T.; Zeng, J.; Yuan, Y. Ecological restoration zoning of territorial space in China: An ecosystem health perspective. J. Environ. Manag. 2024, 364, 121371. [Google Scholar] [CrossRef] [PubMed]
  2. Xie, Z.-X.; Zhang, B.; Shi, Y.-T.; Zhang, X.-Y.; Sun, Z.-X. Changes and protections of urban habitat quality in Shanghai of China. Sci. Rep. 2023, 13, 10976. [Google Scholar] [CrossRef]
  3. Tang, L.; Ke, X.; Chen, Y.; Wang, L.; Zhou, Q.; Zheng, W.; Xiao, B.J. Which impacts more seriously on natural habitat loss and degradation? Cropland expansion or urban expansion? Land Degrad. Dev. 2021, 32, 946–964. [Google Scholar] [CrossRef]
  4. Shao, Q.J. Assessing the sustainability of socio-economic boundaries in China: A downscaled “safe and just space” framework. NPJ Clim. Action 2023, 2, 28. [Google Scholar] [CrossRef]
  5. Wang, Z.; Liu, S.; Su, Y. Spatiotemporal evolution of habitat quality and its response to landscape patterns in karst mountainous cities: A case study of Guiyang City in China. Environ. Sci. Pollut. Res. 2023, 30, 114391–114405. [Google Scholar] [CrossRef] [PubMed]
  6. Kovac, M.; Gasparini, P.; Notarangelo, M.; Rizzo, M.; Cañellas, I.; Fernández-de-Uña, L.; Alberdi, I. Towards a set of national forest inventory indicators to be used for assessing the conservation status of the habitats directive forest habitat types. J. Nat. Conserv. 2020, 53, 125747. [Google Scholar] [CrossRef]
  7. Bai, L.; Xiu, C.; Feng, X.; Liu, D. Influence of urbanization on regional habitat quality: A case study of Changchun City. Habitat Int. 2019, 93, 102042. [Google Scholar] [CrossRef]
  8. Liu, X.; Liang, X.; Li, X.; Xu, X.; Ou, J.; Chen, Y.; Li, S.; Wang, S.; Pei, F. A future land cover simulation model (FLUS) for simulating multiple land cover scenarios by coupling human and natural effects. Landsc. Urban Plan. 2017, 168, 94–116. [Google Scholar] [CrossRef]
  9. Xiao, R.; Yin, H.; Liu, R.; Zhang, Z.; Chinzorig, S.; Qin, K.; Tan, W.; Wan, Y.; Gao, Z.; Xu, C.; et al. Exploring the relationship between land cover change patterns and variation in environmental factors within urban agglomeration. Sustain. Cities Soc. 2024, 108, 105447. [Google Scholar] [CrossRef]
  10. Li, X.; Zhang, X.; Feng, H.; Li, Y.; Yu, J.; Liu, Y.; Du, H. Dynamic evolution and simulation of habitat quality in arid regions: A case study of the Hexi region, China. Ecol. Model. 2024, 493, 110726. [Google Scholar] [CrossRef]
  11. Wu, J.; Luo, J.; Zhang, H.; Qin, S.; Yu, M. Projections of land cover change and habitat quality assessment by coupling climate change and development patterns. Sci. Total Environ. 2022, 847, 157491. [Google Scholar] [CrossRef] [PubMed]
  12. Zheng, L.; Wang, Y.; Li, J. Quantifying the spatial impact of landscape fragmentation on habitat quality: A multi-temporal dimensional comparison between the Yangtze River Economic Belt and Yellow River Basin of China. Land Cover Policy 2023, 125, 106463. [Google Scholar] [CrossRef]
  13. Guan, D.J.; Ran, B.W.; Zhou, L.L.; Jin, C.J.; Yu, X. Assessing and transferring social value of ecosystem services in wetland parks based on SolVES model. Ecol. Indic. 2023, 157, 111300. [Google Scholar] [CrossRef]
  14. Monnier-Corbel, A.; Robert, A.; Hingrat, Y.; Benito, B.M.; Monnet, A.C. Species Distribution Models predict abundance and its temporal variation in a steppe bird population. Glob. Ecol. Conserv. 2023, 43, e02442. [Google Scholar] [CrossRef]
  15. Wang, L.; Huang, L.; Cao, W.; Zhai, J.; Fan, J.W. Assessing grassland cultural ecosystem services supply and demand for promoting the sustainable realization of grassland cultural values. Sci. Total Environ. 2024, 912, 169255. [Google Scholar] [CrossRef] [PubMed]
  16. Yu, W.; Wen, J.; Chen, X.; Gong, Y.; Liu, B. Trans-Pacific multidecadal changes of habitat patterns of two squid species. Fish. Res. 2021, 233, 105762. [Google Scholar] [CrossRef]
  17. Zhang, J.; Fu, Y.C.; Peng, W.Q.; Zhao, J.Y.; Fu, G.S. Interactive influences of ecosystem services and socioeconomic factors on watershed eco-compensation standard “popularization” based on natural based solutions. Heliyon 2022, 8, e12503. [Google Scholar] [CrossRef] [PubMed]
  18. Li, H.D.; Shi, D.M. Spatio-temporal variation in soil erosion on sloping farmland based on the integrated valuation of ecosystem services and trade-offs model: A case study of Chongqing, southwest China. Catena 2024, 236, 107693. [Google Scholar] [CrossRef]
  19. Shi, N.A.; Yang, H.C.; Wang, Q.; Han, Y.; Yan, H.M.; Xiao, N.W. Spatiotemporal variation of habitat quality and its response to fractional vegetation cover change and human disturbance in the Loess Plateau. Front. Ecol. Evol. 2023, 11, 1302224. [Google Scholar] [CrossRef]
  20. Bowler, E.; Lefebvre, V.; Pfeifer, M.; Ewers, R.M. Optimising sampling designs for habitat fragmentation studies. Methods Ecol. Evol. 2022, 13, 217–229. [Google Scholar] [CrossRef]
  21. Feher, L.C.; Allain, L.K.; Osland, M.J.; Pigott, E.; Reid, C.; Latiolais, N. A comparison of plant communities in restored, old field, and remnant coastal prairies. Restor. Ecol. 2021, 29, 13325. [Google Scholar] [CrossRef]
  22. Vandever, M.W.; Elgersma, K.; Carter, S.K.; Wen, A.; Welty, J.L.; Arkle, R.S.; Assal, T.J.; Pilliod, D.S.; Mushet, D.M.; Iovanna, R. Persistence and quality of vegetation cover in expired Conservation Reserve Program fields. Ecosphere 2023, 14, e4359. [Google Scholar] [CrossRef]
  23. Mengist, W.; Soromessa, T.; Feyisa, G.L. Landscape change effects on habitat quality in a forest biosphere reserve: Implications for the conservation of native habitats. J. Clean. Prod. 2021, 329, 129778. [Google Scholar] [CrossRef]
  24. Terrado, M.; Sabater, S.; Chaplin-Kramer, B.; Mandle, L.; Ziv, G.; Acuña, V. Model development for the assessment of terrestrial and aquatic habitat quality in conservation planning. Sci. Total Environ. 2016, 540, 63–70. [Google Scholar] [CrossRef] [PubMed]
  25. Adelisardou, F.; Zhao, W.; Chow, R.; Mederly, P.; Minkina, T.; Schou, J.S. Spatiotemporal change detection of carbon storage and sequestration in an arid ecosystem by integrating Google Earth Engine and InVEST (the Jiroft plain, Iran). Int. J. Environ. Sci. Technol. 2022, 19, 5929–5944. [Google Scholar] [CrossRef]
  26. Liu, Y.; Wang, Y.; Lin, Y.; Ma, X.; Guo, S.; Ouyang, Q.; Sun, C. Habitat Quality Assessment and Driving Factors Analysis of Guangdong Province, China. Sustainability 2023, 15, 11615. [Google Scholar] [CrossRef]
  27. Wang, B.; Cheng, W. Effects of land use/cover on regional habitat quality under different geomorphic types based on InVEST model. Remote Sens. 2022, 14, 1279. [Google Scholar] [CrossRef]
  28. Fernández, M.; Vitiello, A.; Erb, M.; Poelman, E.H. Plant defense strategies against attack by multiple herbivores. Trends Plant Sci. 2022, 27, 528–535. [Google Scholar] [CrossRef]
  29. Chen, X.; Yu, L.; Cao, Y.; Xu, Y.; Zhao, Z.; Zhuang, Y.; Liu, X.; Du, Z.; Liu, T.; Yang, B.; et al. Habitat quality dynamics in China’s first group of national parks in recent four decades: Evidence from land cover and land cover changes. J. Environ. Manag. 2023, 325, 116505. [Google Scholar] [CrossRef] [PubMed]
  30. Wu, J.; Li, X.; Luo, Y.; Zhang, D. Spatiotemporal effects of urban sprawl on habitat quality in the Pearl River Delta from 1990 to 2018. Sci. Rep. 2021, 11, 13981. [Google Scholar] [CrossRef]
  31. Zhu, C.; Zhang, X.; Zhou, M.; He, S.; Gan, M.; Yang, L.; Wang, K. Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecol. Indic. 2020, 117, 106654. [Google Scholar] [CrossRef]
  32. Tang, Z.; Zhou, Z.; Wang, D.; Luo, F.; Bai, J.; Fu, Y. Impact of vegetation restoration on ecosystem services in the Loess plateau, a case study in the Jinghe Watershed, China. Ecol. Indic. 2022, 142, 109183. [Google Scholar] [CrossRef]
  33. Wang, Y.; Quan, D.; Zhu, W.; Lin, Z.; Jin, R. Habitat Quality Assessment under the Change of Vegetation Coverage in the Tumen River Cross-Border Basin. Sustainability 2023, 15, 9269. [Google Scholar] [CrossRef]
  34. Tobisch, C.; Rojas-Botero, S.; Uhler, J.; Kollmann, J.; Müller, J.; Moning, C.; Redlich, S.; Steffan-Dewenter, I.; Benjamin, C.; Englmeier, J.; et al. Conservation-relevant plant species indicate arthropod richness across trophic levels: Habitat quality is more important than habitat amount. Ecol. Indic. 2023, 148, 110039. [Google Scholar] [CrossRef]
  35. Fenetahun, Y.; Yuan, Y.; Xu, X.-W.; Wang, Y.-D. Borana rangeland of southern Ethiopia: Estimating biomass production and carrying capacity using field and remote sensing data. Plant Divers. 2022, 44, 598–606. [Google Scholar] [CrossRef]
  36. Huang, S.; Tang, L.N.; Hupy, J.P.; Wang, Y.; Shao, G.F. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 2021, 32, 1–6. [Google Scholar] [CrossRef]
  37. Jimenez, R.B.; Lane, K.J.; Hutyra, L.R.; Fabian, M.P. Spatial resolution of Normalized Difference Vegetation Index and greenness exposure misclassification in an urban cohort. J. Expo. Sci. Environ. Epidemiol. 2022, 32, 213–222. [Google Scholar] [CrossRef]
  38. Jin, H.; Chen, X.; Wang, Y.; Zhong, R.; Zhao, T.; Liu, Z.; Tu, X. Spatio-temporal distribution of NDVI and its influencing factors in China. J. Hydrol. 2021, 603, 127129. [Google Scholar] [CrossRef]
  39. Martinez, A.d.l.I.; Labib, S.M. Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening. Environ. Res. 2023, 220, 115155. [Google Scholar] [CrossRef]
  40. Xue, J.; Ge, Y.; Ren, H. Spatial upscaling of green aboveground biomass derived from MODIS-based NDVI in arid and semiarid grasslands. Adv. Space Res. 2017, 60, 2001–2008. [Google Scholar] [CrossRef]
  41. Donovan, G.H.; Gatziolis, D.; Derrien, M.; Michael, Y.L.; Prestemon, J.P.; Douwes, J. Shortcomings of the normalized difference vegetation index as an exposure metric. Nat. Plants 2022, 8, 617–622. [Google Scholar] [CrossRef]
  42. Li, C.; Fu, B.; Wang, S.; Stringer, L.C.; Wang, Y.; Li, Z.; Liu, Y.; Zhou, W. Drivers and impacts of changes in China’s drylands. Nat. Rev. Earth Environ. 2021, 2, 858–873. [Google Scholar] [CrossRef]
  43. Li, H.; Cao, Y.; Xiao, J.; Yuan, Z.; Hao, Z.; Bai, X.; Wu, Y.; Liu, Y. A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China. Sci. Data 2024, 11, 527. [Google Scholar] [CrossRef] [PubMed]
  44. Zhang, J.; Deng, M.; Yang, T.; Pang, M.; Wang, Z. Spatiotemporal pattern of reference crop evapotranspiration and its response to meteorological factors in northwest China over years 2000–2019. Environ. Sci. Pollut. Res. 2022, 29, 69831–69848. [Google Scholar] [CrossRef]
  45. Ma, R.; Lü, Y.; Fu, B.; Lü, D.; Wu, X.; Sun, S.; Zhang, Y. A modified habitat quality model to incorporate the effects of ecological restoration. Environ. Res. Lett. 2022, 17, 104029. [Google Scholar] [CrossRef]
  46. Pati, P.K.; Kaushik, P.; Khan, M.L.; Khare, P.K. Allometric equations for biomass and carbon stock estimation of small diameter woody species from tropical dry deciduous forests: Support to REDD+. Trees For. People 2022, 9, 100289. [Google Scholar] [CrossRef]
  47. Nematollahi, S.; Fakheran, S.; Kienast, F.; Jafari, A. Application of InVEST habitat quality module in spatially vulnerability assessment of natural habitats (case study: Chaharmahal and Bakhtiari province, Iran). Environ. Monit. Assess. 2020, 192, 487. [Google Scholar] [CrossRef] [PubMed]
  48. Lu, Y.; Zhao, J.; Qi, J.; Rong, T.; Wang, Z.; Yang, Z.; Han, F. Monitoring the Spatiotemporal Dynamics of Habitat Quality and Its Driving Factors Based on the Coupled NDVI-InVEST Model: A Case Study from the Tianshan Mountains in Xinjiang, China. Land 2022, 11, 1805. [Google Scholar] [CrossRef]
  49. Liu, F.-T.; Xu, E.-Q. Comparison of spatial-temporal evolution of habitat quality between Xinjiang Corps and Non-corps Region based on land cover. J. Appl. Ecol. 2020, 31, 2341–2351. [Google Scholar]
  50. Wang, B.; Cheng, W.; Lan, S. Impact of land cover changes on habitat quality in Altai region. J. Resour. Ecol. 2021, 12, 715–728. [Google Scholar]
  51. Cao, C.; Luo, Y.; Xu, L.; Xi, Y.; Zhou, Y. Construction of ecological security pattern based on InVEST-Conefor-MCRM: A case study of Xinjiang, China. Ecol. Indic. 2024, 159, 111647. [Google Scholar] [CrossRef]
  52. Yi, S.; Wang, H.; Xie, L.; Wang, C.; Huang, X. Spatio-Temporal Dynamics and Drivers of Ecosystem Service Bundles in the Altai region: Implications for Sustainable Land Management. Land 2024, 13, 805. [Google Scholar] [CrossRef]
  53. Fan, Y.; Wang, H.; Yang, S.; Liu, Q.; Heng, J.; Gao, Y.B. Identification of ecological protection crucial areas in Altay Prefecture based on habitat quality and ecological security pattern. Acta Ecol. Sin. 2021, 41, 7614–7626. [Google Scholar]
  54. Zhang, D.; Feng, Z. Holocene climate variations in the Altai Mountains and the surrounding areas: A synthesis of pollen records. Earth-Sci. Rev. 2018, 185, 847–869. [Google Scholar] [CrossRef]
  55. Qin, S.; Xiao, P.; Zhang, X. How do snow cover fraction change and respond to climate in Altai Mountains of China? Int. J. Climatol. 2022, 42, 7213–7227. [Google Scholar] [CrossRef]
  56. Rudaya, N.; Krivonogov, S.; Słowiński, M.; Cao, X.; Zhilich, S. Postglacial history of the Steppe Altai: Climate, fire and plant diversity. Quat. Sci. Rev. 2020, 249, 106616. [Google Scholar] [CrossRef]
  57. Cai, E.; Zhang, S.; Chen, W.; Li, L. Spatio–temporal dynamics and human–land synergistic relationship of urban expansion in Chinese megacities. Heliyon 2023, 9, e19872. [Google Scholar] [CrossRef]
  58. Wang, C.; Liu, S.; Zhou, S.; Zhou, J.; Jiang, S.; Zhang, Y.; Feng, T.; Zhang, H.; Zhao, Y.; Lai, Z.; et al. Spatial-temporal patterns of urban expansion by land cover/land cover transfer in China. Ecol. Indic. 2023, 155, 111009. [Google Scholar] [CrossRef]
  59. He, G.; Zhang, C.; Dong, Z. Survival in desert: Extreme water adaptations and bioinspired structural designs. iScience 2023, 26, 105819. [Google Scholar] [CrossRef]
  60. Wang, X.; Geng, X.; Liu, B.; Cai, D.; Li, D.; Xiao, F.; Zhu, B.; Hua, T.; Lu, R.; Liu, F. Desert ecosystems in China: Past, present, and future. Earth-Sci. Rev. 2022, 234, 104206. [Google Scholar] [CrossRef]
  61. Zhou, S.; Long, H.; Xing, H.; Zhang, K.; Wang, R.; Zhang, E. Human activities facilitated the decline of forest ecosystem in East Asia after 5000 a BP. Earth-Sci. Rev. 2023, 245, 104552. [Google Scholar] [CrossRef]
  62. Li, X.B.; Lyu, X.; Dou, H.S.; Dang, D.L.; Li, S.K.; Li, X.; Li, M.Y.; Xuan, X.J. Strengthening grazing pressure management to improve grassland ecosystem services. Glob. Ecol. Conserv. 2021, 31, e01782. [Google Scholar] [CrossRef]
  63. Deng, X.; Greet, J.; Jones, C.S. Soil moisture influences the root characteristics of a herbaceous riparian plant along a regulated river. Plant Ecol. 2024, 225, 25–36. [Google Scholar] [CrossRef]
  64. Manghwar, H.; Hussain, A.; Alam, I.; Khoso, M.A.; Ali, Q.; Liu, F. Waterlogging stress in plants: Unraveling the mechanisms and impacts on growth, development, and productivity. Environ. Exp. Bot. 2024, 224, 105824. [Google Scholar] [CrossRef]
  65. Tembata, K.; Yamamoto, Y.; Yamamoto, M.; Matsumoto, K. Don’t rely too much on trees: Evidence from flood mitigation in China. Sci. Total Environ. 2020, 732, 138410. [Google Scholar] [CrossRef]
  66. Feng, Y.; Shi, Y.; Zhao, M.; Shen, H.; Xu, L.; Luo, Y.; Liu, Y.; Xing, A.; Kang, J.; Jing, H.; et al. Yield and quality properties of alfalfa (Medicago sativa L.) and their influencing factors in China. Eur. J. Agron. 2022, 141, 126637. [Google Scholar] [CrossRef]
  67. Lv, C.; Schlecht, E.; Goenster-Jordan, S.; Buerkert, A.; Zhang, X.; Wesche, K. Vegetation Responses to Fixed Stocking Densities in Highly Variable Montane Pastures in the Chinese Altay. Rangel. Ecol. Manag. 2019, 72, 812–821. [Google Scholar] [CrossRef]
  68. Zhang, S.; Jia, W.; Zhu, H.; You, Y.; Zhao, C.; Gu, X.; Liu, M. Vegetation growth enhancement modulated by urban development status. Sci. Total Environ. 2023, 883, 163626. [Google Scholar] [CrossRef]
  69. Zhao, C.; Zhu, H.; Zhang, S.; Jin, Z.; Zhang, Y.; Wang, Y.; Shi, Y.; Jiang, J.; Chen, X.; Liu, M. Long—term trends in surface thermal environment and its potential drivers along the urban development gradients in rapidly urbanizing regions of China. Sustain. Cities Soc. 2024, 105, 105324. [Google Scholar] [CrossRef]
  70. Qiao, L.; Wang, X.; Smith, P.; Fan, J.; Lu, Y.; Emmett, B.; Li, R.; Dorling, S.; Chen, H.; Liu, S.; et al. Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Chang. 2022, 12, 574–580. [Google Scholar] [CrossRef]
  71. Yang, Z.; Fang, C.; Mu, X.; Li, G.; Xu, G. Urban green space quality in China: Quality measurement, spatial heterogeneity pattern and influencing factor. Urban For. Urban Green. 2021, 66, 127381. [Google Scholar] [CrossRef]
  72. Fan, J.; Abudumanan, A.; Wang, L.; Zhou, D.; Wang, Z.; Liu, H. Dynamic Assessment and Sustainability Strategies of Ecological Security in the Irtysh River Basin of Xinjiang, China. Chin. Geogr. Sci. 2023, 33, 393–409. [Google Scholar] [CrossRef] [PubMed]
  73. Qin, Y.; Aldyarhan, K.; Zhang, Z.; Liu, P.; Liu, H.; Li, Y. The Main Problems of the Water Ecological Environment and Protective Countermeasures in the River Basin of the Altai region, Xinjiang. J. Resour. Ecol. 2023, 14, 383–390. [Google Scholar] [CrossRef]
  74. Ji, X.; Sun, Y.; Guo, W.; Zhao, C.; Li, K. Land cover and habitat quality change in the Yellow River Basin: A perspective with different CMIP6-based scenarios and multiple scales. J. Environ. Manag. 2023, 345, 118729. [Google Scholar] [CrossRef] [PubMed]
  75. Wang, Q.; Wang, H.; Chang, R.; Zeng, H.; Bai, X. Dynamic simulation patterns and spatiotemporal analysis of land-use/land-cover changes in the Wuhan metropolitan area, China. Ecol. Model. 2022, 464, 109850. [Google Scholar] [CrossRef]
  76. Zheng, H.; Li, H. Spatial–temporal evolution characteristics of land cover and habitat quality in Shandong Province, China. Sci. Rep. 2022, 12, 15422. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Study area.
Figure 1. Study area.
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Figure 2. Forest and grassland ecosystems (a) and tree saplings under an old tree (b) in the Altai region.
Figure 2. Forest and grassland ecosystems (a) and tree saplings under an old tree (b) in the Altai region.
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Figure 3. Framework and flowchart of the methodology.
Figure 3. Framework and flowchart of the methodology.
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Figure 4. Environmental elements that affect habitat quality: (a) DEM, (b) snow, (c) NDVI, (d) farmland density, (e) river chain density.
Figure 4. Environmental elements that affect habitat quality: (a) DEM, (b) snow, (c) NDVI, (d) farmland density, (e) river chain density.
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Figure 5. Habitat quality in the Altai region from 1990 to 2022. Unmodified habitat quality (a–d), habitat quality modified by the NDVI (e–h), and habitat quality modified by biomass (i–l).
Figure 5. Habitat quality in the Altai region from 1990 to 2022. Unmodified habitat quality (a–d), habitat quality modified by the NDVI (e–h), and habitat quality modified by biomass (i–l).
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Figure 6. Comparison of the unmodified habitat quality, NDVI-modified habitat quality, and biomass-modified habitat quality across mountainous and plain regions.
Figure 6. Comparison of the unmodified habitat quality, NDVI-modified habitat quality, and biomass-modified habitat quality across mountainous and plain regions.
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Figure 7. Comparison of NDVI-modified habitat quality (a) and biomass-modified (b) habitat quality across different land use types from 1990 to 2022.
Figure 7. Comparison of NDVI-modified habitat quality (a) and biomass-modified (b) habitat quality across different land use types from 1990 to 2022.
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Figure 8. Linear and quadratic fit relationships between actual biomass data and the InVEST habitat quality model (a), the NDVI-modified model (b), and the biomass-modified model (c).
Figure 8. Linear and quadratic fit relationships between actual biomass data and the InVEST habitat quality model (a), the NDVI-modified model (b), and the biomass-modified model (c).
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Table 1. Sampling point setting and measurement items.
Table 1. Sampling point setting and measurement items.
TypeForest Layer
(30 m × 30 m)		Shrub Layer
(5 m × 5 m)		Herbaceous Layer
(1 m × 1 m)
Forest sites8334 × 378 × 3
Shrub sites-20 × 35 × 3
Meadow sites
(100 m × 100 m)		--51 × 3
Measure itemThe leaf area index, plant coverage (used to verify the accuracy of biomass results)
Species, number, height, and crown spreadBiomass (a 30 × 30 cm section)The total biomass of individual samples
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Zhang, M.; Zhang, H.; Deng, W.; Yuan, Q. Assessment of Habitat Quality in Arid Regions Incorporating Remote Sensing Data and Field Experiments. Remote Sens. 2024, 16, 3648. https://doi.org/10.3390/rs16193648

AMA Style

Zhang M, Zhang H, Deng W, Yuan Q. Assessment of Habitat Quality in Arid Regions Incorporating Remote Sensing Data and Field Experiments. Remote Sensing. 2024; 16(19):3648. https://doi.org/10.3390/rs16193648

Chicago/Turabian Style

Zhang, Mingke, Hao Zhang, Wei Deng, and Quanzhi Yuan. 2024. "Assessment of Habitat Quality in Arid Regions Incorporating Remote Sensing Data and Field Experiments" Remote Sensing 16, no. 19: 3648. https://doi.org/10.3390/rs16193648

APA Style

Zhang, M., Zhang, H., Deng, W., & Yuan, Q. (2024). Assessment of Habitat Quality in Arid Regions Incorporating Remote Sensing Data and Field Experiments. Remote Sensing, 16(19), 3648. https://doi.org/10.3390/rs16193648

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