Remote Sensing Application in Ecological Restoration Monitoring: A Systematic Review

Abstract

In the context of the continuous degradation of the global environment, ecological restoration has become a primary task in global environmental governance. In this process, remote sensing technology, as an advanced monitoring and analysis tool, plays a key role in monitoring ecological restoration. This article reviews the application of remote sensing technology in ecological restoration monitoring. Based on a comprehensive analysis of the literature in the field of ecological remote sensing, it systematically summarizes the major in-orbit spaceborne and airborne sensors and their related products. This article further proposes a series of evaluation indicators for ecological restoration from four aspects: forests, soil, water, and the atmosphere, and elaborates on the calculation methods for these indicators. In addition, this paper also summarizes the methods for evaluating the effectiveness of ecological restoration, including subjective evaluation, objective evaluation, and comprehensive evaluation methods. Finally, we analyze the challenges faced by remote sensing technology in evaluating ecological restoration effectiveness, such as issues with the precision of indicators extraction, the limitations of spatial resolution, and the diversity of evaluation methods. This review also looks forward to future ecological restoration technologies, such as the potential applications of integrated aerospace and terrestrial remote sensing, multi-data fusion, and machine learning technologies. This study reveals the effectiveness of remote sensing technology in ecological restoration monitoring, aiming to provide efficient tools and innovative strategies for future remote sensing monitoring and assessment of ecological restoration.

1. Introduction

With the continuous growth of the global population and rapid industrialization, human activities have exerted unprecedented impacts on the natural environment. Issues such as deforestation, wetland degradation, soil erosion, and climate change are increasingly severe, posing significant threats to ecosystem stability and biodiversity. In response to this trend, at the international level, the United Nations General Assembly has declared the “United Nations Decade on Ecosystem Restoration (2021–2030)” resolution [1]. Furthermore, the United Nations has recognized ten global “World Ecological Restoration Flagship Projects” worldwide, including China’s “Beautiful China: Watershed Management Project”; the “Atlantic Forest Three Countries Agreement” aiming to restore forest ecosystems in Brazil, Paraguay, and Argentina; the “Altyn Dala Conservation Initiative” aimed at reversing the decline of extensive grasslands in Central Asia; and the “Indonesia Natural Building Project” aimed at creating conditions for the natural revival of mangroves. This marks ecological restoration as a top global priority.

The concept of ecological restoration was initially proposed by Aldo Leopold and others in the United States in 1935. His early work in the 20th century, especially at the “Aldo Leopold Reserve” in Sauk County, Wisconsin, demonstrated the practice of restoring ecosystems. Since 1950, countries abroad have started using remote sensing technology to monitor the ecological environment of mining areas. From the 1970s to the 1990s, remote sensing technology rapidly developed, and the application of satellite remote sensing in ecological restoration monitoring became more widespread. Some scholars have proposed various vegetation indices for the remote sensing monitoring of ecological restoration, such as NDVI, TVI, SAVI, and RVI [2,3]. Muller et al. reviewed the various types of satellite remote sensing data available and their uses in studying river systems [4]; Veitch et al. discussed the application of geographic information systems and remote sensing data in wasteland conservation [5]. With the widespread application of high-resolution satellite imagery abroad since the beginning of the 21st century, remote sensing monitoring technology in the field of ecological restoration has reached a mature stage. Morales et al. developed a new image analysis method to select and delineate areas with higher canopy coverage for focused ecological restoration and conservation efforts [6]. Zerger et al. developed an assessment of ground layer nutrient status based on spectral indices, combining time-series data with rainfall and soil moisture data [7]. Cabello et al. utilized remote sensing technology to quantify ecosystem functions, thereby supporting ecological restoration planning and monitoring [8]. In the field of ecological restoration remote sensing monitoring abroad, indicator systems have also been constructed to assess the progress and effectiveness of ecological restoration projects. Das et al. used the pressure–state–response model, selecting nine ecosystem indicators to study the health of the wetland ecosystem in the Murshidabad area [9]; Del et al. assessed the ecosystem services of various ecological restoration interventions in a conservation area in South Africa based on measured data, Sentinel-2 data, and soil terrain information [10].

China has experienced a transformation from its initial stages to modernization in the field of ecological restoration monitoring using remote sensing. Initially, before the 1980s, monitoring primarily relied on field measurements, which were not only labor-intensive and costly but also unsuitable for large-scale monitoring [11]. With the advent of geographic information systems (GIS) and remote sensing technologies in the 1980s, China began to explore the use of these technologies for regional-scale environmental quality monitoring. At the beginning of the 21st century, scholars began to emphasize the importance of monitoring and assessing issues such as grassland restoration, land degradation, air pollution, and water pollution. Zhuo et al. developed a climate–vegetation growth baseline response model that effectively evaluated ecological restoration projects in the Xilingol Grassland [12], while Liu et al., based on the conceptual framework of the United Nations Millennium Ecosystem Assessment (MA), proposed an assessment indicator system for the grassland ecosystem in the Sanjiangyuan area [13]. With the continuous advancement of Chinese satellite technology, remote sensing has made significant progress in ecological restoration monitoring and has been increasingly applied in ecological restoration projects [14,15,16,17,18]. Currently, research on ecological restoration evaluation is increasingly leaning towards establishing a systematic set of indicators. For instance, Wu et al. used remote sensing technology and landscape indices to develop a wetland health indicator system, conducting an in-depth study of the Hongze Lake wetlands [19]. Cheng et al. constructed an assessment indicator system that integrates physiological, ecological, and environmental factors to evaluate the health of desert ecosystems [20]. Recently, the Chinese government has initiated a series of major projects for ecological protection and construction that have significantly improved vegetation and forest coverage. The focus of China’s ecological restoration has shifted from the restoration of individual elements to the integrated management of mountains, rivers, forests, fields, lakes, and grasslands, showcasing the comprehensiveness and depth of ecological restoration efforts.

The evaluation of ecological restoration benefits is a multidimensional and complex process involving the application and combination of various evaluation methods. In order to comprehensively evaluate the benefits of ecological restoration projects, this paper provides an overview of evaluation methods classified into subjective evaluation methods, objective evaluation methods, and comprehensive evaluation methods. Firstly, subjective evaluation methods rely on expert opinions and experiential judgments, including the Delphi method, fuzzy Delphi method (FDM), and analytic hierarchy process (AHP), which play a crucial role by incorporating expert knowledge into assessments. Secondly, objective evaluation methods such as the entropy method, principal component analysis (PCA), and machine learning methods are driven by data, deeply exploring the inherent evaluation indicators and structural relationships within the data through mathematical models. Comprehensive evaluation methods integrate the advantages of both subjective and objective approaches, such as fuzzy comprehensive evaluation (FCE) and the multi-level and stepwise weighted (MLSW) method, which provide a relatively comprehensive and balanced assessment perspective by combining multiple technical approaches.

Based on the summary and refinement of the ecological remote sensing literature, this paper first provides an overview of commonly used remote sensing sensors and the remote sensing products of different indicators. It then proposes ecological restoration indicators from four aspects: forests, soil, water bodies, and the atmosphere. This article summarizes the calculation methods for indicators and reviews the evaluation methods for ecological restoration effectiveness from subjective, objective, and comprehensive perspectives. Finally, we conclude by summarizing the challenges and future prospects in areas such as high-precision indices; spatial resolution; evaluation methods; the integration of space, air, and ground systems; and machine learning.

2. Meta-Analysis of the Literature

This review is based on the literature retrieved by searching the Web of Science. The search strategy employed the following formula: TS = (“ecological”) AND TS = (“restoration”) AND TS = (“assessment”) AND TS = (“remote sensing”). After conducting the search, articles that did not meet the research criteria were excluded by reviewing the title, abstract, and keywords of each paper. A total of 141 papers met the inclusion criteria.

2.1. Annual Statistics of Literature Quantity

Currently, the field of ecological restoration research based on remote sensing technology exhibits an active research posture. For example, the data shown in Figure 1 illustrate the number of publications on ecological restoration using remote sensing in recent years, reflecting significant growth and an ongoing development trend in this area (including only a portion of the publications from 2024). The graph indicates that although there are fluctuations in the annual number of publications, there is an overall upward trend, particularly notable in the number of publications from 2023. This growth trend demonstrates the increasingly widespread application of remote sensing technology in ecological restoration.

2.2. Keyword Network Diagram of the Literature

Figure 2 displays a keyword association network diagram created using VOSviewer1.6.20 software, where keywords are divided into four main categories: a yellow area centered around “remote sensing”, a green area centered around “ecological restoration”, a red area centered around “index”, and a blue area centered around “vegetation.” The classification of these keywords reveals various research directions and focal points. The hot topics in remote sensing monitoring for ecological restoration include different categories of ecological restoration such as forests, soil, river basins, and climate; systemic keywords like ecological assessment, ecosystem services, indexes, and indicators; and remote sensing monitoring aspects such as MODIS, Landsat, imagery, and monitoring.

2.3. Keyword Network Diagram of the Literature

Following a detailed review of the literature retrieved, we primarily examined the titles, keywords, abstracts, research areas, and content of the documents and organized the related indices. We tabulated the number of publications related to the ecological restoration of forests, soil, water bodies, and the atmosphere, as shown in Figure 3. It is important to note that a single publication could cover different geographical areas and multiple types of ecological restoration. The results show that the number of publications related to forests, soil, and water bodies is higher, mainly due to the high level of attention these areas receive, the convenience of data acquisition, and the strong demand for practical applications. Although the number of publications involving atmospheric ecological restoration is relatively fewer, in the context of climate change and air pollution, research on atmospheric ecological restoration is also critically important and warrants increased focus and further investigation.

3. Sensors and Products for Monitoring Ecological Restoration

Remote sensing sensors play a key role in the assessment and monitoring of ecological restoration projects. These high-tech devices are capable of capturing images and data of the Earth’s surface from the air or space, providing scientists and environmental managers with valuable information about the status and changes of ecosystems. Whether it is analyzing changes in vegetation cover, monitoring the recovery progress of damaged areas, or assessing the long-term impacts of ecological engineering, remote sensing technology offers an efficient, precise, and non-invasive approach. Utilizing spectral data across various wavelengths, from visible light to infrared and thermal infrared, remote sensing sensors can identify and quantify various ecological indicators, such as leaf area index, vegetation health status, soil moisture, and land use changes. Moreover, the accumulation of remote sensing data over time can assist researchers in tracking the dynamic changes in the ecological restoration process, thereby providing a scientific basis for the planning and management of ecological restoration.

Observation sensors can be categorized into two main types based on their platforms: satellite sensors and drone sensors. Satellite sensors are mounted on satellites and are capable of monitoring large areas of the Earth or other planets. Drone sensors, on the other hand, are installed on unmanned aerial vehicles (UAVs) and are suited to smaller-scale, higher-resolution observations [21].

3.1. Spaceborne Sensors

Satellite sensors operate in higher orbits, and spaceborne sensors are capable of covering vast areas, making them extremely useful for global or large regional environmental and climate monitoring. They can provide data on various aspects, including topography, climate change, agriculture, and forest cover.

Table 1. Major orbital spaceborne sensors.

Category	Satellite Sensor	Spectrum or Frequency Range	Bands	Spatial Resolution (m)	Revisit Period
Multispectral Sensor	GF-1	0.45–0.90	5	2; 8	4 days
	GF-2	0.45–0.90	5	1; 4	5 days
	GF-7	0.45–0.90	5	0.8; 3.2	5 days
	WorldView-2	0.45–0.92	9	0.46; 1.84	1.1 days
	WorldView-3	0.45–2.36	29	0.31; 1.24; 3.70; 30	97 min
	SPOT-6	0.45–0.89	5	1.5	Daily
	SPOT-7	0.45–0.89	5	1.5	Daily
	Planet Scope	0.455–0.86	8	3; 3.5–4	Daily
	ZY-3	0.45–0.9	5	2.1; 2.5; 5.8	3–5 days
	Landsat-7 (ETM+)	0.45–2.35	7	15; 30	16 days
	Landsat 8 (OLI)	0.43–2.29	9	15; 30	16 days
	Landsat 9 (OLI-2)	0.43–2.29	9	15; 30	16 days
	Sentinel-2	0.44–2.19	13	10; 20; 60	5 days
	GF-4	0.45–4.10	6	50; 400	20 s
	GF-6	0.40–0.90	13	2; 8; 16	2 days, 4 days
	ASTER	0.52–2.43	10	15; 30	15 days
	HJ-1B	0.43–3.90	7	30; 150	4 days
	MODIS	0.405–2.155	19	250; 500; 1000	Daily
	AVHRR	0.55–3.93	3	1100	Twice daily
	VIIRS	0.41–4.00	18	375–750	Twice daily
	GOCI	0.40–0.86	8	250	8 days
Hyperspectral Sensor	GF-5	0.4–12.5	342	20; 30; 40	5 days
	PRISMA	0.40–2.50	239	30	——
	CHRIS (PROBA-1)	0.40–1.05	62	17	2 days
	EnMAP	0.43–2.45	228	30	27 days
Microwave Sensor	TerraSAR-X/TanDEM-X	X-band	1	0.6; 1.2; 1.7–3.3	11 days
	Cosmo-SkyMed	X-band	1	1; 3; 15; 30; 100	16 days
	Sentinel-1	C-band	1	5	6 days
	KOMPSAT-5	X-band	1	1; 3; 20	28 days
	HJ-1C	S-band	1	5; 20	——
	ALOS-2	L-band	1	1; 3; 6; 10	14 days
	RADARSAT-2	C-band	1	1–100	24 days
	GF-3	C-band	1	1–500	1.5 days, 3 days
	SAOCOM-1A	L-band	1	10–100	16 days
	SMAP	L-Band	1	Passive: 40,000 Active: 1000–3000	8 days
	SMOS	L-Band	1	30,000–50,000	3–7 days
Thermal Infrared Sensor	Landsat 7 (ETM+)	10.4–12.5	1	60	16 days
	ECOSTRESS	8.5–12.5	5	70	——
	ASTER	8.13–11.66	15	90	15 days
	Landsat 8 (TIRS)	10.6–12.51	2	100	16 days
	Landsat 9 (TIRS-2)	10.6–12.51	2	100	16 days
	HJ-1B	10.50–12.50	1	300	4 days
	VIIRS	8.00–12.00	4	375–750	——
	AVHRR	10.50–12.50	2	1100	Twice daily
	MODIS	1.36–14.38	17	1000	Daily
Laser Sensor	ICESat-2	0.532	——	14	91 days
	GEDI	1.064	1	25	A few days to a few weeks

summarizes the major spaceborne sensors currently in orbit. Satellite sensors offer important tools for assessing and guiding ecological restoration work. By providing comprehensive and temporally continuous environmental data, satellite remote sensing technology helps ensure the effectiveness and sustainability of ecological restoration efforts.

3.2. Unmanned Aerial Vehicle (UAV) Sensors

Unmanned aerial vehicle (UAV) sensors are devices used to collect specific types of data, mounted on drones to perform various monitoring and measurement tasks. Due to the high flexibility of drones and their ability to access remote areas, the sensors they carry play a significant role in fields such as agriculture, environmental monitoring, ecological assessment, and security surveillance.

In the field of ecological restoration, UAV sensors are especially significant,

Table 2. Major orbital UAV sensors.

Category	Satellite Sensor	Spectrum or Frequency Range	Bands	Spatial Resolution (m)
Optical Sensor	ADS40	0.4–1	5	0.3–1
	ADS80		5	0.15–0.5
	Hyspex ODIN-1024	0.40–2.50	1024	0.5 m at 2000 m Altitude
	AVIRIS	0.40–2.50	224	17
	HYDICE	0.40–2.50	210	0.8–4
	Hymap	0.40–2.50	126	3–10
	CASI-1500	0.40–1.00	15–288	0.5–3
	Daedalus	0.42–14.00	12	25
Microwave Sensor	E-SAR	0.3–12 ghz	4	≤10 m
	F-SAR	0.3–110 ghz	7	≤10 m
	Orbisar	0.3–12 ghz	2	1
	RAMSES			≤10 m
	SETHI			≤10 m
Laser Sensor	Optech ALTM	1.064	1	A Few Centimeters to Several Meters
	Leica ALS	1.064	1	Centimeter-Level
	RIEGL Airborne Laser Scanner	1.064	1	Centimeter-Level

summarizes common onboard UAV sensors. They offer a detailed view of damaged ecosystems, encompassing key indicators such as vegetation cover, water body conditions, and soil quality. For example, multispectral and hyperspectral sensors can analyze plant growth and health status. Thermal infrared sensors are useful for assessing water distribution and soil moisture. Additionally, light detection and ranging (LiDAR) technology provides precise data on terrain and vegetation structure. This information is crucial for monitoring the ecological restoration process, evaluating the effectiveness of restoration efforts, and guiding future restoration measures.

In the application of remote sensing monitoring for ecological restoration, different remote sensing technologies exhibit unique performance characteristics. Optical remote sensing relies on the reflection and scattering of sunlight to capture surface images, making it suitable for vegetation monitoring, the assessment of water body conditions, and the monitoring of surface temperature and the environment. Thermal infrared remote sensing detects thermal radiation emitted from the surface, which can be used to monitor soil moisture, vegetation water conditions, and surface temperatures. Microwave remote sensing can penetrate cloud cover to observe, making it suitable for assessing soil characteristics, moisture conditions, and structural features of vegetation. As an advanced form of microwave remote sensing, synthetic aperture radar (SAR) provides high-resolution images that can monitor surface changes, and its ability to penetrate vegetation makes it an ideal choice for monitoring forests and other densely vegetated areas. Airborne LiDAR (light detection and ranging) sends laser pulses to the ground and measures the reflected signals to obtain high-precision terrain and surface feature data, which can accurately measure the height, density, and structure of vegetation, providing important data support for ecological restoration.

3.3. Remote Sensing Products

In the field of remote sensing products for ecological restoration indices, various types of products are available for different applications.

Table 3. Remote sensing indicator products.

Indicator	Product		Sensor	Spatial Scale	Spatial Resolution	Temporal Resolution
Vegetation Cover Indicator	MODIS	MOD13 Series	MODIS	Global	500 m	16 days
		MYD13 Series	MODIS		1 km	16 days
		MOD13Q1	MODIS		1 km	Monthly
	MuSyQ High-Resolution 16 m/10-day Vegetation Coverage Product		GF1	China	16 m	10 days
	GEOV	GEOV1, GEOV2, GEOV3	SPOT VGT	Global	GEOV1, GEOV2: 1 km; GEOV3: 300 m	10 days
	GLASS	GLASS MODIS	MODIS	Global	500 m	8 days
		GLASS AVHRR	AVHRR	Global	5 km	8 days
	EUMETSAT/LSA SAF		SEVIRI	Europe, South America, Africa	3 km	Daily
Leaf Area Index (LAI)	Musyq High-Resolution 16 m Leaf Area Index Product with 10-Day Synthesis		GF1	China	16 km	10 days
	MISR		MISR	Global	1.1 km	1 day
	PROBA-V		PROBA-V	Global	300 m	10 days
	MODIS		MODIS	Global	500 m	4 days
	VIIRS		SNPP/VIIRS	Global	500 m	8 days
	GEOV2		SPOT/VEGETATION, MODIS	Global	1 km	10 days
	GLASS		SPOT/VEGETATION, MODIS	Global	1 km	8 days
Net Primary Productivity	MOD17A3		MODIS	Global	1 km	Yearly
	MOD17A3HGF		MODIS	Global	500 m	Yearly
Forest Growing Stock	ATL08		ICESat-2 (ATLAS)	Global	Laser Footprint Diameter of 17 m, with One Footprint Every 0.7 m Along the Track	91 days
Soil Moisture	SMOS		SMOS MIRAS	Global	43 km	Daily
	ERS/MetOp		Radar Altimeters on ERS-1 and ERS-2 Satellites/ASCAT	Global	25 km	Daily
	SMAP	SMAP L3	SMAP	Global	9 km	Daily
		SMAP L4	SMAP	Global	9 km	3 h
		SMAP/Sentinel-1 L2 Radiometer	SMAP/Sentinel-1	Global	1 km	Each Orbital Pass
	AMSR2-JAXA		GCOM-W1 AMSR	Global	0.1°	Twice Daily
	AMSR2-LPRM		GCOM-W1 AMSR	Global	0.1°	Twice Daily
Land Surface Temperature	LST_AVHRR		AVHRR/NOAA	Global	1.1 km	Twice Daily (Day and Night)
	MODIS	MxD11_L2	MODIS	Global	1 km	Twice Daily
		MxD21_L2	MODIS	Global	1 km	Twice Daily
	TM		Landsat 4–5	Global	30 m	16 days
	ETM+		Landsat 7	Global	30 m	16 days
	TIRS		Landsat 8	Global	30 m	16 days
	ECO2LSTE		ECOSTRESS/International Space Station	Global	70 m
Atmosphere	GEOS-FP		Multiple satellites	Global	0.25° × 0.3125°	Hourly/3 h
	MERRA-2		Multiple Satellites	Global	0.5° × 0.625°	Hourly/3 h/6 h/Daily
	ECMWF ERA5		Multiple Satellites	Global	31 km	Hourly

summarizes the main remote sensing ecological monitoring products. These remote sensing products provide key data for monitoring and understanding environmental changes, making them indispensable tools in ecological restoration research and practice. By analyzing the data provided by these remote sensing products, researchers and environmental managers can better formulate and adjust ecological restoration strategies. This enables the effective protection and restoration of ecosystems, contributing to the achievement of sustainable development goals.

4. The Indicators for Monitoring Ecological Restoration

4.1. Major Indicator Selection in Ecological Restoration Monitoring

For ecological restoration evaluation in different regions, selecting appropriate evaluation indicators is key, taking into account the commonalities and characteristics of different ecological zones. Different natural geographic areas have varying degrees of vegetation conditions, soil types, hydrological conditions, and climatic environments, leading to distinct regional differences in ecological damage and restoration needs. This article proposes targeted ecological restoration indicators from four aspects: forests, soil, water bodies, and air.

Centering on the main content of ecological restoration, forests, soil, water bodies, and air can be considered primary indicators. Further, it is important to select key indicators from each primary indicator that can be measured through remote sensing technology, ensuring that these indicators can construct a comprehensive ecological zone ecological restoration evaluation system. This ensures the effectiveness and influence of these indicators during the ecological restoration monitoring process.

4.2. The Indicators for Monitoring Forest Ecological Restoration

When monitoring the ecological restoration effects of an ecosystem, forest elements are very important. Through a literature review on ecological restoration monitoring, key indicators such as fractional vegetation cover (FVC), leaf area index (LAI), net primary productivity (NPP), forest stock volume, and water conservation function were identified. (Table 4).

Fractional vegetation coverage is a key parameter depicting surface vegetation cover and plays an important role in global change research, surface process simulations, and hydro-ecological models [22]. It is an intuitive indicator for assessing forest recovery and health status. LAI measures the total area of leaves per unit area, which is crucial for photosynthesis and energy flow, making it an important variable in vegetation structure [23]. NPP refers to the net carbon gain of plants in natural and agricultural ecosystems [24] and is an important indicator for evaluating the function of forest ecosystems. Canopy density describes the coverage and density of the tree canopy, impacting the ecosystem’s light and water cycles. Forest stock volume is one of the most significant indicators representing the quantity of forests [25]. The water conservation function of forests illustrates their contribution to the hydrological cycle, including regulating runoff, intercepting precipitation, and purifying water quality [26]. These indicators collectively present the multifunctional aspects of forest ecosystems and are crucial for ecological restoration and sustainable management.

In the context of calculating indicators for monitoring forest ecological restoration, apart from specific mechanistic models, widely used empirical statistical models include regression modeling and vegetation index methods. Additionally, in the realm of machine learning, commonly utilized techniques encompass neural network algorithms, support vector machines, lookup table (LUT) methods, and Bayesian network algorithms, among others. These methods provide effective tools for the accurate assessment and monitoring of forest ecological restoration processes.

Table 4. The indicators for monitoring forest ecological restoration and calculation methods.

Indicators	Formula or Method	Literature
FVC	Regression Model	[27,28]
	Mixed Pixel Decomposition	[29]
	Machine Learning	[30,31,32]
	Spectral Gradient Method	[33]
	FCD Grading	[34]
LAI	Empirical Statistical Model	[35,36,37]
	Machine Learning	[38,39,40]
	Canopy Reflectance Model	[41]
NPP	Empirical Statistical Model	[42]
	Carnegie–Ames–Stanford Approach (CASA)	[43]
	Vegetation Photosynthesis Model (VPM)	[44]
	Physiological Principles for Predicting Growth (3-P)	[45]
Forest Growing Stock	Empirical Statistical Model	[46]
	Levenberg–Marquardt backpropagation (LM–BP)	[47]
	Machine Learning	[48]
Water Conservation Function	Equivalent Method	[49]
	Water Balance Method	[50]
	Comprehensive Water Storage Capacity Method	[51]
	Precipitation Storage Quantity Method	[52]
	Multifactor Regression Method	[53]

Table 4. The indicators for monitoring forest ecological restoration and calculation methods.

Indicators	Formula or Method	Literature
FVC	Regression Model	[27,28]
	Mixed Pixel Decomposition	[29]
	Machine Learning	[30,31,32]
	Spectral Gradient Method	[33]
	FCD Grading	[34]
LAI	Empirical Statistical Model	[35,36,37]
	Machine Learning	[38,39,40]
	Canopy Reflectance Model	[41]
NPP	Empirical Statistical Model	[42]
	Carnegie–Ames–Stanford Approach (CASA)	[43]
	Vegetation Photosynthesis Model (VPM)	[44]
	Physiological Principles for Predicting Growth (3-P)	[45]
Forest Growing Stock	Empirical Statistical Model	[46]
	Levenberg–Marquardt backpropagation (LM–BP)	[47]
	Machine Learning	[48]
Water Conservation Function	Equivalent Method	[49]
	Water Balance Method	[50]
	Comprehensive Water Storage Capacity Method	[51]
	Precipitation Storage Quantity Method	[52]
	Multifactor Regression Method	[53]

In the remote sensing monitoring of forest ecological restoration, many empirical statistical methods are based on vegetation and soil indices. Common indices include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI), vegetation dryness index (VDI), optimized soil adjusted vegetation index (OSAVI), and soil brightness index (SBI). (Table 5). These vegetation and soil indices are extremely important in the remote sensing monitoring of forest ecological restoration. They effectively reflect changes in vegetation cover and health, as well as soil characteristics, providing a scientific basis for assessing restoration effects and formulating future management strategies. Additionally, the application of vegetation indices not only helps in quickly monitoring and predicting changes in the forest ecosystem but also plays a significant practical role in guiding forest restoration and protection efforts. Soil indices are equally crucial as they assist researchers in assessing soil conditions and monitoring erosion and bare land, thus enabling more precise decision-making in the ecological restoration process [54].

Table 5. Common vegetation indeces in remote sensing monitoring of ecological restoration.

Index	Formula	Literature
NDVI	$\frac{NIR-red}{NIR+red}$	[2]
RVI	$\frac{NIR}{red}$	[3]
TVI	$\sqrt{NDVI+0.5}$	[2]
TVDI	$\frac{LS{T}_i-(a_1+b_1\times NDVI)}{(a_2+b_2\times NDVI)-(a_1+b_1\times NDVI)}$	[55]
SAVI	$\frac{NIR-red}{NIR+red+L}$	[56]
OSAVI	$\frac{1.16(NIR-red)}{NIR+red+0.16}$	[57]
SBI	$-0.283MSS4-0.66MSS5+0.577MSS6+0.388MSS7$	[58]

NIR: near-infrared band reflectance; red: shortwave infrared reflectance; LST: surface temperature of any image element; a₁, b₁, a₂, b₂: dry edge and wet edge fitting coefficients; L: adjustment parameter, used to reduce the impact of soil background; MSS4~MSS7: four channels of the Landsat-MSS sensor.

Table 5. Common vegetation indeces in remote sensing monitoring of ecological restoration.

Index	Formula	Literature
NDVI	$\frac{NIR-red}{NIR+red}$	[2]
RVI	$\frac{NIR}{red}$	[3]
TVI	$\sqrt{NDVI+0.5}$	[2]
TVDI	$\frac{LS{T}_i-(a_1+b_1\times NDVI)}{(a_2+b_2\times NDVI)-(a_1+b_1\times NDVI)}$	[55]
SAVI	$\frac{NIR-red}{NIR+red+L}$	[56]
OSAVI	$\frac{1.16(NIR-red)}{NIR+red+0.16}$	[57]
SBI	$-0.283MSS4-0.66MSS5+0.577MSS6+0.388MSS7$	[58]

NIR: near-infrared band reflectance; red: shortwave infrared reflectance; LST: surface temperature of any image element; a₁, b₁, a₂, b₂: dry edge and wet edge fitting coefficients; L: adjustment parameter, used to reduce the impact of soil background; MSS4~MSS7: four channels of the Landsat-MSS sensor.

4.3. The Indicator for Monitoring Soil Ecological Restoration

When monitoring the ecological restoration effects of an ecosystem, soil quality plays a very important role. Through literature review and summarization, this study selected indicators such as soil moisture, soil heavy metal content, soil pH, soil organic matter, soil salinization, soil conservation (soil and water loss), land surface temperature (LST), and topographic relief to assess the effectiveness of soil restoration. (Table 6).

Soil moisture is a key variable in the study of surface systems, including regional water cycles, agricultural irrigation management, climate change, and environmental monitoring [59]. Remote sensing technology can monitor moisture changes, helping to determine which areas require irrigation or drainage improvements to take appropriate measures in dry or overly wet conditions. Monitoring soil heavy metal content and soil pH is crucial for assessing soil pollution and acid–base balance [60,61,62]. Remote sensing technology allows for the rapid identification of polluted areas over a larger range and the targeted implementation of remediation measures. LST is an important parameter reflecting land–atmosphere interactions [63], essential for understanding the microclimate and hydrological conditions of ecological areas. Topographic relief is a key indicator for quantitatively describing landform morphology, and its extent significantly influences the potential erosion intensity of surface materials, playing an important role in soil and water conservation management and ecological reconstruction in mining areas [64]. Monitoring topographic relief helps in selecting vegetation types suited to the local environment and in planning vegetation restoration and land use. Soil organic matter and soil salinity are key indicators for assessing soil fertility and the degree of soil salinization [65,66,67]. Remote sensing monitoring effectively tracks these indicators, guiding fertilization and improvement measures. Through these soil quality indicators, soil ecological restoration can be more effectively carried out, while remote sensing technology strengthens monitoring and assessment.

In the calculation of indicators for soil ecological restoration monitoring, in addition to specific mechanistic models and methods based on optical and thermal infrared technologies for estimating soil moisture and surface temperature, empirical statistical models are also widely applied. These include various regression models, primarily stepwise multiple linear regression and diagnostic indices. Additionally, in the realm of machine learning, commonly used techniques include artificial neural networks, random forests, support vector machines, and gradient boosting machines, providing effective tools for the precise assessment and monitoring of soil ecological restoration processes.

Table 6. The indicators for soil ecological restoration and calculation methods.

Indicators	Formula or Method	Literature
Soil Conservation	Revised Universal Soil Loss Equation (RUSLE)
Soil Moisture	Empirical Statistical Model	[55]
	Thermal Inertia Method	[68]
	Triangle Method	[69]
	Extended Kalman Filter	[70]
	Machine Learning	[71]
Soil Heavy Metal Content	Empirical Statistical Model	[72]
	Machine Learning	[73]
Soil pH	Empirical Statistical Model	[74]
	Machine Learning	[75]
LST	Single-Channel (SC) Algorithm	[76]
	Split-Window/Double-Window (SW/DW) Algorithm	[77]
	Temperature and Emissivity Separation (TES) Algorithm	[78]
	Day/Night (D/N) Algorithm	[79]
	Machine Learning	[80]
Topographic Relief	Mean Change Point Analysis Method	[81]
Soil Organic Matter	Empirical Statistical Model	[82]
	Ground-Based Non-Imaging Spectrometer Estimation	[83]
	Machine Learning	[84]
Soil Salinity	Empirical Statistical Model	[85]
	Machine Learning	[86]

Table 6. The indicators for soil ecological restoration and calculation methods.

Indicators	Formula or Method	Literature
Soil Conservation	Revised Universal Soil Loss Equation (RUSLE)
Soil Moisture	Empirical Statistical Model	[55]
	Thermal Inertia Method	[68]
	Triangle Method	[69]
	Extended Kalman Filter	[70]
	Machine Learning	[71]
Soil Heavy Metal Content	Empirical Statistical Model	[72]
	Machine Learning	[73]
Soil pH	Empirical Statistical Model	[74]
	Machine Learning	[75]
LST	Single-Channel (SC) Algorithm	[76]
	Split-Window/Double-Window (SW/DW) Algorithm	[77]
	Temperature and Emissivity Separation (TES) Algorithm	[78]
	Day/Night (D/N) Algorithm	[79]
	Machine Learning	[80]
Topographic Relief	Mean Change Point Analysis Method	[81]
Soil Organic Matter	Empirical Statistical Model	[82]
	Ground-Based Non-Imaging Spectrometer Estimation	[83]
	Machine Learning	[84]
Soil Salinity	Empirical Statistical Model	[85]
	Machine Learning	[86]

4.4. The Indicators for Monitoring Water Ecological Restoration

When monitoring the ecological restoration effects of an ecosystem, water bodies play a crucial role. Monitoring water quality is important for environmental protection. After reviewing and summarizing the literature, this study selected the following indicators for assessing the effectiveness of water body restoration: the water body eutrophication indicator, total suspended matter (TSM), chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), dissolved oxygen (DO), total phosphorus (TP) and total nitrogen (TN), water transparency, black and odorous water bodies, and river sinuosity. (Table 7). These indicators help in assessing various aspects of water quality and the ecological health of water bodies, which are essential for ecological restoration efforts.

The water body eutrophication index reflects the enrichment of nutrients in the water, which is a primary cause of excessive algae growth and water quality deterioration. Eutrophication in lakes is a major ecological and environmental issue faced by lakes in China and globally [87]. TSM concentration is one of the key factors in the primary water quality components [88], directly affecting water transparency and photosynthesis. Chl-a indicates the distribution of plankton biomass and is a basic indicator of water body primary productivity and eutrophication [88], often used to assess algal biomass and the level of water body eutrophication. CDOM is complex in composition and significantly influences water color and chemical processes [88], reflecting the content and quality of organic matter in the water, while COD is an important indicator for measuring the degree of water pollution. TP and TN are key nutrients whose excessive presence can lead to water body eutrophication. Water transparency is an intuitive indicator of water quality. DO is crucial for aquatic life, and its content is key to assessing water body health. Black and odorous water bodies are typical manifestations of urban water pollution. River sinuosity reflects the morphological characteristics and ecological health of rivers. By comprehensively using these indicators, the effectiveness of water body restoration projects can be thoroughly evaluated, ensuring the effectiveness and sustainability of ecological restoration.

In the calculation methods for water ecological restoration monitoring indicators, in addition to specific mechanistic models based on water color remote sensing, various empirical statistical methods are commonly used. These include single-band models, band ratio models, first-order differential models, and multiple regression models, among others. Moreover, advanced techniques in the field of machine learning, such as artificial neural networks, genetic algorithms, and support vector machines, are also widely applied in the precise assessment and monitoring of aquatic ecological restoration. The combined use of these methods significantly enhances the efficiency and accuracy of aquatic ecological restoration monitoring.

Table 7. The indicators for water ecological restoration and calculation methods.

Indicators	Formula or Method	Literature
Eutrophication of Water Body	Trophic Status Index (TSI)	[89,90]
	Comprehensive Trophic Level Index (TLI)	[91]
TSM, Chl-a, CDOM	Empirical Statistical Model	[92,93,94,95,96]
	Machine Learning	[97,98]
	Quantitative Assessment of Absorption (QAA)	[99,100]
	Case 2 Regional Coast Colour (C2RCC)	[101,102]
COD	Empirical Statistical Model	[103]
	Backpropagation Neural Network (BPMN) Model	[104]
TP and TN	Empirical Statistical Model	[105]
	Machine Learning	[105,106,107]
Water Transparency	Semi-Analytical Algorithm	[108]
	Transparency Inversion Algorithm Based on FUI Water Color Index and Hue Angle	[109]
DO	Empirical Statistical Model	[110]
	Machine Learning	[111]
Black and Smelly Water Bodies	Optical Threshold Method	[112]
	Recognition Method Based on Typical Remote Sensing Water Quality Indicators	[113]
	Colorimetric Method	[114]
River Sinuosity	$S=Lr/Lv$ S: Sinuosity; Lr: River Centerline, which refers to the actual length of the river segment being measured; Lv: Centerline of the River Basin, which is the straight-line distance between two points upstream and downstream of the river segment being measured	[115]

Table 7. The indicators for water ecological restoration and calculation methods.

Indicators	Formula or Method	Literature
Eutrophication of Water Body	Trophic Status Index (TSI)	[89,90]
	Comprehensive Trophic Level Index (TLI)	[91]
TSM, Chl-a, CDOM	Empirical Statistical Model	[92,93,94,95,96]
	Machine Learning	[97,98]
	Quantitative Assessment of Absorption (QAA)	[99,100]
	Case 2 Regional Coast Colour (C2RCC)	[101,102]
COD	Empirical Statistical Model	[103]
	Backpropagation Neural Network (BPMN) Model	[104]
TP and TN	Empirical Statistical Model	[105]
	Machine Learning	[105,106,107]
Water Transparency	Semi-Analytical Algorithm	[108]
	Transparency Inversion Algorithm Based on FUI Water Color Index and Hue Angle	[109]
DO	Empirical Statistical Model	[110]
	Machine Learning	[111]
Black and Smelly Water Bodies	Optical Threshold Method	[112]
	Recognition Method Based on Typical Remote Sensing Water Quality Indicators	[113]
	Colorimetric Method	[114]
River Sinuosity	$S=Lr/Lv$ S: Sinuosity; Lr: River Centerline, which refers to the actual length of the river segment being measured; Lv: Centerline of the River Basin, which is the straight-line distance between two points upstream and downstream of the river segment being measured	[115]

4.5. The Indicators for Monitoring Atmosphere Ecological Restoration

When monitoring the ecological restoration effects of an ecosystem, atmospheric factors are crucial. After collecting and researching the literature, this study selected aerosol optical depth (AOD) and PM2.5 as indicators for atmospheric ecological restoration evaluation. Atmospheric aerosols [116] refer to solid or liquid particles suspended in the atmosphere and are an important indicator for measuring the concentration of suspended particulate matter in the air. PM2.5 [117] refers to particulate matter with a diameter of 2.5 μm or less. In the evaluation of ecological area restoration, AOD can be used to measure changes in air quality, especially in monitoring vegetation recovery and ecosystem health. By monitoring changes in PM2.5, the impact of air quality on ecosystem restoration can be assessed. (

Table 8. The indicators for atmosphere ecological restoration and calculation methods.

Indicators	Formula or Method	Literature
AOD	Ground-Based Remote Sensing Inversion	[118]
	Multi-Angle Remote Sensing	[119]
	Twin-Satellite Cooperative	[120]
	Improved Dark Pixel	[121]
	Cloud-Top AOD	[122]
PM2.5	Spatio-Temporal Geographically Weighted Method	[123]
	Machine Learning	[124]
	Empirical Statistical Model	[125]

). These indicators are essential for understanding and managing the atmospheric component of ecological restoration efforts.

In the realm of calculation methods for atmospheric ecological restoration monitoring indicators, various mechanistic models are utilized, including ground-based and satellite remote sensing inversion techniques. Empirical statistical models include multiple linear regression models and linear mixed-effects models, among others. Furthermore, in the field of machine learning, techniques such as backpropagation (BP) neural networks, random forests, and stochastic gradient descent are widely applied, enhancing the precision and efficiency in the assessment and monitoring of atmospheric ecological restoration.

5. The Method for Evaluating Ecological Restoration Effectiveness

For ecological restoration monitoring, choosing appropriate evaluation methods is key to ensuring that the results are scientifically accurate. In research both domestically and internationally, the evaluation methods used can generally be categorized into three types: subjective evaluation methods, objective evaluation methods, and comprehensive evaluation methods (

Table 9. Methods for evaluating ecological restoration effectiveness.

Category	Evaluation Method	Description	Advantages	Disadvantages
Subjective Evaluation Method	Delphi [126]	Through the extensive solicitation of expert opinions and multiple feedback revisions, the experts’ opinions on the evaluation object gradually converge. Finally, combined with the comprehensive opinions of experts, a quantitative and qualitative method is used to evaluate the evaluation object.	Able to effectively bring together and integrate the knowledge and opinions of experts in different fields.	Time-consuming and dependent on expert choices, sometimes leading to biased outcomes.
	FDM [127]	Fuzzy set theory is integrated on the basis of traditional Delphi to deal with the ambiguity of thoughts and expressions in decision-making and achieve the selection of important indicators.	Able to more accurately handle and interpret uncertainty and ambiguity in expert opinions and provide more refined evaluation results.	The lack of unbiased expert selection contributes to the significant application challenges.
	AHP [128]	By building a hierarchical model, complex decision problems are broken down into more manageable parts; then, the relative importance of these parts is evaluated through quantitative and qualitative analysis and, finally, these evaluations are aggregated to determine the best decision overall.	Clear ideas and simple methods when analyzing problems with multiple goals, factors, and criteria.	A certain degree of subjectivity exists.
Objective Evaluation Methods	Entropy [129]	Uses the characteristic that entropy is an uncertain measurement to judge the effectiveness and value of existing indicators.	Avoids possible biases caused by human factors on indicator weight results.	Lack of horizontal comparison between indicators.
	Entropy Weighting	This is an extension of the entropy method. It not only calculates the information entropy of each indicator but also further determines the weight of each indicator based on the information entropy.	Makes the evaluation results more reasonable; suitable for various types of evaluation and decision analysis.	In actual operation, the calculation process is relatively complex.
	PCA [130]	Calculates eigenvalues and eigenvectors, obtains principal components through cumulative contribution rate calculation, and, finally, performs comprehensive analysis.	The original indicators can be recombined into new comprehensive indicators to ensure the authenticity of the original indicator information and strong objectivity.	Shortage of information loss is present.
	Machine Learning [131]	Automatically extracts valuable information from data to support decision-making and predict future ecological changes.	Reduces the impact of subjective weights on evaluation results to a great extent; uses artificial intelligence and other means to invert parameters and improve evaluation accuracy.	The accuracy of the evaluation is highly dependent on the quality and quantity of data.
	Factor Analysis [132,133]	This is a technique for simplifying multiple variables. Assuming that multicollinearity is removed between variables, factor analysis classifies highly correlated variables into one category.	Ability to simplify complex data into key factors for easy analysis and interpretation.	Not suitable for small samples or situations with a lot of missing data.
	Mean–Variance Comprehensive Analysis [131]	Evaluation is performed by calculating the mean (average level) and variance (fluctuation or stability) of a set of data or indicators. The mean reflects the overall level of the evaluation object and the variance reflects its stability or consistency.	Easy to understand and calculate	Only considers the mean and variance, other important distribution characteristics of the data may be ignored.
	Grey Relational Analysis [134]	Based on the similarity of the geometric shapes of sequence curves, it can be judged whether the connection between different sequences is close, and the degree of similarity or dissimilarity of development trends between various factors can be quantitatively described, which is suitable for dynamic process analysis.	Handles data with incomplete information or high uncertainty; it is suitable for situations where the amount of data is small or the quality is low.	Evaluation results differ depending on the selected model and parameters, different choices will lead to different outcomes.
Comprehensive Evaluation Methods	FCE [135]	By establishing a fuzzy relationship matrix and combining the evaluation indicator set and the evaluation grade set, multiple attributes or performances of the object can be comprehensively evaluated.	Able to handle incomplete or ambiguous information; able to conduct comprehensive evaluations.	May involve subjective judgment; evaluation process is relatively complex.
	Matter Element Analysis [136]	Uses the rules and methods for studying matter elements and their changes to solve contradictory problems.	Multiple evaluation indicators can be comprehensively considered; suitable for handling complex evaluation problems.	The definition of uncertainty, classical domain, and spectral domain evaluation criteria is problematic.
	Composite Indicator [136]	Comprehensively observes the extent and direction of the influence on a certain phenomenon or outcome when multiple indicators change simultaneously.	Strong comprehensiveness, logic, and systematicity are evident.	Extensive content may obscure certain factors with significant impact, leading to biases in the evaluation results.
	Set Pair Analysis	Effectively depicts the corresponding unified relationship of certain uncertain systems, which is in line with the dialectics of nature and the way of human thinking.	Simple calculation and easy method.	A lack of clarity exists in determining the coefficient value.
	MLSW	The composite weight method used for multi-indicator decision analysis groups multiple indicators into different levels or categories and determines the weight of each indicator and each group of indicators step by step.	Handles complex indicator systems at multiple levels and categories; the evaluation becomes more comprehensive and systematic.	Establishing and implementing a multi-level and progressive weighting system can be relatively complex.

). These include methods such as the AHP, entropy method, PCA, and fuzzy comprehensive evaluation method. This categorization aids a more comprehensive understanding and application of various evaluation tools to enhance the effectiveness and accuracy of ecological restoration evaluations. The use of these methods also makes the evaluation of ecological restoration projects more comprehensive and multi-dimensional.

Subjective evaluation methods focus on expert opinions and experiential judgments, typically relying on experts’ intuitive assessment of an ecosystem’s condition. Although this method has a certain degree of subjectivity, it is very effective in situations where data are scarce or uncertainties are high. Objective evaluation methods are based on concrete data and statistical analysis, such as the entropy method and principal component analysis, which assess the effectiveness of ecological restoration by quantifying indicators, reducing the interference of subjective judgments. Comprehensive evaluation methods, like the fuzzy comprehensive evaluation method, combine the advantages of both subjective and objective methods, offering a more comprehensive and balanced evaluation result by integrating different types of data and expert opinions.

6. Challenges and Future Prospects

6.1. Challenges

6.1.1. Ecological Indicators Present Challenges in Meeting the Demand for High-Precision Extraction

In the current remote sensing monitoring of ecological restoration effectiveness, we face significant challenges in improving the extraction accuracy of various ecological indicators. Despite using remote sensing technologies, such as combining hyperspectral remote sensing data, satellite, and drone observations, we have been able to enhance the accuracy of detail recognition and classification to some extent. However, there are still many areas that require improvement. For instance, in processing large-scale ecological indicators like the assessment of net primary productivity and forest stock volume, existing ecological models and time series analysis methods have not yet been able to precisely capture seasonal and interannual ecological changes. In the calculation of soil moisture, although we try to improve monitoring accuracy by integrating precipitation data and artificial irrigation information, the effectiveness of these methods is still limited under different soil types and environmental conditions. As for the measurement of water body indices, high temporal resolution remote sensing data and existing algorithms still struggle to meet the needs for high-precision extraction in some cases. These issues highlight that in the accurate extraction and evaluation of ecological indicators, further research and technological innovation are still needed.

6.1.2. Spatial Resolution Affects the Effectiveness of Remote Sensing Data in Extracting Ecological Indicators

Spatial resolution directly impacts the effectiveness of remote sensing data in extracting specific ecological indicators. For example, high-spatial-resolution data are more suitable for extracting small-scale ecological features, such as soil moisture or soil heavy metal content. However, high-resolution data may not be applicable for large-scale ecological assessments, such as net primary productivity or forest stock volume. Although remote sensing technologies provide extensive coverage both spatially and temporally, their resolution may limit the precise assessment of small-scale or detailed features.

6.1.3. The Accuracy of Remote Sensing Data Requires Validation through Ground Observations

The accuracy of remote sensing data needs to be validated through ground observations, but there are many challenges in conducting ground validation, especially in vast agricultural lands or natural environments, where collecting comprehensive and representative ground data can be difficult. Areas with complex terrain in remote geographical locations or harsh environments are particularly challenging for extensive field surveys. Moreover, remote sensing data and ground observations need to be collected at the same or close time points to ensure the timeliness of their comparison. However, it is often difficult to achieve this in practice, especially in rapidly changing environments. Conducting extensive ground validations over large areas requires a significant amount of time, manpower, and financial resources, making it challenging to obtain sufficient ground validation data.

6.1.4. Acquiring High-Quality and Multi-Temporal Remote Sensing Data Faces Challenges

Ecological restoration, as a long-term process, relies on continuously collected time-series data. However, obtaining multi-temporal data faces multiple challenges. Firstly, cost factors significantly impact the feasibility of long-term data collection, especially when high-resolution remote sensing data are required. Secondly, satellite orbital cycles and data-sharing policies may limit access to continuous and consistent data. In addition, the quality of remote sensing data is often affected by natural environmental factors, particularly cloud cover. Atmospheric conditions, such as fog and smoke, can also interfere with remote sensing data, potentially leading to data loss or decreased quality. These issues pose serious challenges to the accuracy and reliability of the data, thereby affecting the accurate assessment of ecological changes. Therefore, relying on remote sensing data for ecological restoration assessment presents numerous challenges. These challenges need to be overcome through technological innovation and policy adjustments to ensure the ongoing collection and high quality of data.

6.1.5. Universality of Algorithms and Models

When developing a remote sensing restoration indicator system, creating algorithms and models with broad applicability presents significant challenges. Different types of remote sensing indicators, such as forests, soil, water bodies, and the atmosphere, require different remote sensing calculation methods. Additionally, the composition and structure of forests and soils vary across different regions, and variations in light and humidity due to different climates can affect the accuracy and applicability of calculation methods. Therefore, algorithms and models need to adapt to various types of remote sensing indicators and the changes in indicators in different regions to accurately extract reliable information from remote sensing data. Future research on the remote sensing monitoring of ecological restoration should focus on developing intelligent and flexible processing algorithms and models that can adapt to different environmental changes and application requirements.

6.1.6. A Unified Method for Evaluating Ecological Restoration Effectiveness Is Lacking

Currently, there is no unified method for evaluating the effectiveness of ecological restoration in China. The evaluation methods used in previous research are based on specific assumptions and simplifications, and some may overlook the complex interactions within ecosystems or fail to fully consider the uncertainty of environmental changes. Additionally, subjective, objective, and comprehensive evaluation methods all have their limitations to some extent. For example, subjective evaluations are limited by biases in expert selection, objective evaluation methods heavily depend on the chosen models and parameters, and the models and methods used in comprehensive evaluations can be quite complex. Therefore, it is crucial to conduct research on the methods for evaluating the effectiveness of ecological restoration.

6.2. Future Prospects

The future development of remote sensing monitoring for ecological restoration should move towards integration and intelligence, requiring the comprehensive application of a range of advanced technologies, for instance, integrating multi-sensor and multi-data fusion technologies with high-resolution imaging to achieve intelligent monitoring across space, air, and land; utilizing the Internet of Things (IoT) and blockchain technologies to enhance data integration and security; automating monitoring through machine learning and artificial intelligence; and applying digital twin technology to provide scientific decision support. The use of these technologies will drive smarter and more effective restoration strategies, promoting sustainable ecological management and protection.

6.2.1. The Fusion of Multiple Sensors and Multiple Data

As remote sensing technology continues to advance, future methods for evaluating ecological restoration are expected to rely more on the fusion of data from multiple sensors. This integrated approach will leverage the unique advantages of different sensors to provide more comprehensive and accurate ecosystem monitoring data. For example, LiDAR technology excels in acquiring precise three-dimensional structural information, such as tree height and forest density. Combining this with traditional optical remote sensing data (like multispectral and hyperspectral imaging) can lead to a deeper understanding of changes in vegetation cover and biomass. Integrating data from sensors with high temporal resolution (like MODIS or VIIRS) can better track seasonal and short-term changes during the ecological restoration process, providing dynamic assessments. Additionally, data fusion, combining data from different sensors and platforms, along with ground observation data, and utilizing big data analytics techniques, helps in comprehensively analyzing the status and changes of ecosystems. Incorporating real-time data streams, such as from near-real-time satellite and UAV observations, allows for the timely detection of and response to ecological changes and disaster events. The fusion of multi-source data will greatly enhance the comprehensive capability of ecological restoration monitoring, promoting more efficient and accurate ecological management and decision-making.

6.2.2. High-Precision Sensor Technology and Satellite and Aircraft Technology Improvements

With the continuous development of sensing technology, future sensors will possess higher precision and sensitivity, allowing for more accurate and detailed monitoring of various ecosystem parameters, including soil quality, water quality, and vegetation condition. The application of new-generation satellite and unmanned aerial vehicle (UAV) remote sensing technologies will provide data with high spatial and temporal resolution, enabling more detailed regional segmentation and significantly increasing monitoring frequency, thus capturing the minute details and dynamic processes of ecosystem changes. In the future, these advancements will provide more scientific and precise assessment tools for ecological restoration, propelling ecological research and practice towards a new stage of greater efficiency and refinement.

6.2.3. Establish an Intelligent Ecological Restoration Monitoring System That Integrates Air, Space, and Ground

The integrated ecological restoration monitoring system for space, air, and ground is a system that combines sensors and detection equipment from different layers, such as space (satellites), air (UAV), and ground (sensor networks and ground stations), along with data processing technologies. Its purpose is to monitor, analyze, and implement ecological restoration measures. This system, utilizing various remote sensing technologies and ground monitoring data, achieves comprehensive monitoring and analysis of ecosystems. Through data fusion and intelligent analysis, the integrated aerospace, terrestrial, and subterranean system can provide in-depth insights into the state, trends, and recovery processes of ecosystems. This is of significant importance for the planning, execution, and evaluation of ecological restoration projects, enabling scientists and decision-makers to more effectively understand and manage ecosystems.

6.2.4. Application of Advanced Technologies in Remote Sensing Monitoring for Ecological Restoration

In the field of ecological restoration, contemporary research should focus on integrating advanced technologies such as the IoT, blockchain, artificial intelligence (AI), and unmanned aerial vehicles (UAVs) with traditional remote sensing techniques. This integration offers a more comprehensive and detailed monitoring and assessment framework for ecological restoration. The application of IoT technology in the real-time capture and transmission of environmental data significantly enhances the efficiency and responsiveness of monitoring. Innovations in data storage, sharing, and security brought by blockchain technology ensure the reliability and transparency of environmental monitoring data. Additionally, the incorporation of artificial intelligence, especially in image recognition and pattern analysis, greatly improves the ability to process and interpret data. Furthermore, the use of UAV technology for acquiring high-resolution images allows for more detailed observations of surface changes, particularly effective in monitoring ecological changes in specific areas. The combined application of these advanced technologies can not only boost the efficiency and accuracy of the entire monitoring system but also provide all-around data support for ecological restoration, promoting the sustainable management and effective recovery of ecosystems.

6.2.5. The Widespread Use of Artificial Intelligence Technologies Such as Machine Learning

Machine learning and other artificial intelligence technologies are already widely used in the calculation of ecological restoration indicators. Machine learning algorithms can process large amounts of complex data, making the calculation of ecological restoration indicators more precise and accurate and less prone to human error. In the future monitoring of ecological restoration, machine learning and other AI technologies can be more comprehensively applied to the calculation of indicators and the establishment of evaluation systems. Machine learning can effectively integrate information from multiple sensors and data sources, including remote sensing data, geographic information, meteorological data, and soil data, to establish more comprehensive ecological restoration indicators. Moreover, machine learning models can provide predictions and early warnings of future ecosystem changes, helping to address potential ecological issues proactively and take timely actions.

6.2.6. Support of Digital Twin Technology for the Remote Sensing Monitoring of Ecological Restoration

Digital twin technology involves creating virtual models of physical objects, processes, or systems. In ecological restoration, researchers can use digital twin models of ecosystems to simulate the impacts of various restoration measures without actual intervention in the natural environment, predicting both long-term and short-term effects of restoration projects. Additionally, digital twin technology can receive updated remote sensing data in real time, thereby reflecting the current ecological status and allowing for immediate adjustments to restoration strategies. The detailed visualizations and predictive data provided by digital twin models are powerful tools for policy-making and resource allocation. Governments and institutions can use these data to support the decision-making process for their ecological restoration projects, enhancing the scientific basis and effectiveness of their policies.

7. Conclusions

In this article, we reviewed the application of remote sensing technology in the field of ecological restoration monitoring. Focusing on remote sensing technology, the article comprehensively outlined the major in-orbit spaceborne and airborne sensors, as well as associated remote sensing data products. Moreover, we generalized a range of ecological restoration monitoring indicators, calculation methods, and evaluation methods from the critical perspectives of forests, soil, water, and atmosphere. Nonetheless, current research in ecological restoration monitoring still confronts several challenges, including limitations in extracting high-accuracy indicators; the impacts of spatial resolution; the necessity for ground verification; challenges in acquiring high-quality, multi-temporal data; issues with the applicability of algorithms and models; and a lack of unified methods for evaluating ecological restoration effectiveness. Looking ahead, with the ongoing advancement of remote sensing technology and the emergence of new technologies, the research focus for ecological restoration monitoring should shift towards the integration of various sensors and data, the enhancement of high-precision sensing technologies, and the optimization of satellite and UAV technologies. Additionally, the convergence of cutting-edge technologies like integrated space–air–ground intelligent monitoring systems, the Internet of Things, blockchain, and artificial intelligence with remote sensing will provide more efficient and comprehensive solutions for ecological restoration monitoring, thereby offering stronger support for protecting and restoring our natural environment.