Conservation easements (CEs) play an important role in the provision of ecological services. This paper aims to use the open-access Sentinel-2 satellites to advance existing conservation management capacity to a new level of near-real-time monitoring and assessment for the conservation easements in Nebraska. This research uses machine learning and Google Earth Engine to classify inundation status using Sentinel-2 imagery during 2018–2021 for all CE sites in Nebraska, USA. The proposed machine learning approach helps monitor the CE sites at the landscape scale in an efficient and low-cost manner. The results confirmed effective inundation performance in these floodplain or wetland-related CE sites. The CE sites under the Emergency Watershed Protection-Floodplain Easement (EWPP-FPE) had the highest inundated area rate of 18.72%, indicating active hydrological inundation in the floodplain areas. The CE sites under the Wetlands Reserve Program (WRP) reached a mean annual surface water cover rate area of 8.07%, indicating the core wetland areas were inundated periodically or regularly. Other types of CEs serving upland conservation purposes had a lower level of inundation while these uplands conservation provided critical needs in soil erosion control. The mean annual surface water cover rate is 0.96% for the CE sites under the Grassland Reserve Program (GRP). The conservation of the CEs on uplands is an important component to reduce soil erosion and improve downstream wetland hydrological inundation performance. The findings support that the sites with higher inundation frequencies can be considered for future wetland-related conservation practices. The four typical wetland-based CE sites suggested that conservation performance can be improved by implementing hydrological restoration and soil erosion reduction at the watershed scale. The findings provided robust evidence to discover the surface water inundation information on conservation assessment to achieve the long-term goals of conservation easements.

Recent developments in Unmanned Aircraft Vehicles (UAVs), thermal imaging, and Auto-machine learning (AutoML) have shown high potential for precise wildlife surveys but have rarely been studied for habitat assessment. Here, we propose a framework that leverages these advanced techniques to achieve cost-effective habitat quality assessment from the perspective of actual wildlife community usage. The framework exploits vision intelligence hidden in the UAV thermal images and AutoML methods to achieve cost-effective wildlife distribution mapping, and then derives wildlife use indicators to imply habitat quality variance. We conducted UAV-based thermal wildlife surveys at three wetlands in the Rainwater Basin, Nebraska. Experiments were set to examine the optimal protocols, including various flight designs (61 and 122 m), feature types, and AutoML. The results showed that UAV images collected at 61 m with a spatial resolution of 7.5 cm, combined with Faster R-CNN, returned the optimal wildlife mapping (more than 90% accuracy). Results also indicated that the vision intelligence exploited can effectively transfer the redundant AutoML adaptation cycles into a fully automatic process (with around 33 times efficiency improvement for data labeling), facilitating cost-effective AutoML adaptation. Eventually, the derived ecological indicators can explain the wildlife use status well, reflecting potential within- and between-habitat quality variance.

Indigenous vegetation such as Alhagi sparsifolia Shap. (Fabaceae) has been severely damaged in recent years because of the growing population and increasing land use on the southern margin of the Taklamakan Desert. Alhagi sparsifolia plays an important role in supporting the fragile ecosystem in the oasis foreland as it has multiple ecological and economic functions. Cele Oasis, located on the southern rim of the Taklamakan Desert, was used to investigate the impact of human disturbance on regeneration of A. sparsifolia in the oasis–desert ecotone. Observations of A. sparsifolia in response to cutting and burning were conducted in 2010 and 2011. The results showed that burning in spring significantly decreased height and biomass in comparison to cutting in the fall. Moreover, biomass was decreased by spring burning more than by spring cutting. Burning in spring is no advantage for the growth and survival of A. sparsifolia. Cutting in fall appears to be a useful treatment for increasing the production from and survival of A. sparsifolia that could facilitate the sustainable development of the Cele Oasis.

Traditional field-based ecological surveys are inefficient in mountainous regions with steep slopes and deep valleys, highlighting the need for new quantitative remote sensing–based approaches. To account for complex terrain, four representative topographic factors (slope, relief, dissection, curvature) were selected via Digital Elevation Model (DEM) analysis to develop a Terrain Complexity Index (TCI), replacing the dryness component in the Remote Sensing Ecological Index (RSEI). Combined with greenness, wetness, and heat factors from Landsat 8, TCI was integrated using principal component analysis to form a Terrain-Adjusted RSEI (TARSEI), extending ecological assessment from two to three dimensions. In a mountainous case study in Huzhou City, Zhejiang, China, TARSEI showed a marked 34.2-percentage-point improvement over the original RSEI. Its high-value areas captured 82.3% of ecotourism points of interest, versus 48.1% for RSEI, demonstrating its enhanced accuracy for terrain-specific analysis. TARSEI further identified 28 new ecotourism resource clusters totaling 520.1 km² (8.9% of the city area), with a 98.5% overlap with high TARSEI zones. These results confirmed TARSEI’s effectiveness and provided robust scientific support for sustainable ecotourism development and spatial planning. With its high accuracy, stability, and universality, TARSEI is a promising and transferable tool for ecotourism resource assessment and spatial planning and management in complex terrain regions.