The conservation of species at risk of extinction requires data to support decisions at landscape to regional scales. There is a need for information that can assist with locating suitable habitats in fragmented and degraded landscapes to aid the reintroduction of at-risk plant species. In addition, desiccation and water stress can be significant barriers to the success of at-risk plant reintroduction programs. We examine how airborne light detection and ranging (LiDAR) data can be used to model microtopographic features that reduce water stress and increase resource availability, providing information for landscape planning that can increase the success of reintroduction efforts for a dryland landscape in Hawaii. We developed a topographic habitat-suitability model (HSM) from LiDAR data that identifies topographic depressions that are protected from prevailing winds (high-suitability sites) and contrasts them with ridges and other exposed areas (low-suitability sites). We tested in the field whether high-suitability sites had microclimatic conditions that indicated better-quality habitat compared to low-suitability sites, whether plant-response traits indicated better growing conditions in high-suitability sites, whether the locations of individuals of existing at-risk plant species corresponded with our habitat-suitability classes, and whether the survival of planted individuals of a common native species was greater in high-suitability, compared to low-suitability, planting sites. Mean wind speed in a high-suitability field site was over five times lower than in a low-suitability site, and soil moisture and leaf wetness were greater, indicating less stress and greater resource availability in high-suitability areas. Plant height and leaf nutrient content were greater in high-suitability areas. Six at-risk species showed associations with high-suitability areas. The survival of planted individuals was less variable among high-suitability plots. These results suggest that plant establishment and survival is associated with the habitat conditions identified by our model. The HSM can improve the survival of planted individuals, reduce the cost of restoration and reintroduction programs through targeted management activities in high-suitability areas, and expand the ability of managers to make landscape-scale decisions regarding land-use, land acquisition, and species recovery.

The use of remote sensing in ecosystem management has transformed how land managers, practitioners, and policymakers evaluate ecosystem loss, gain, and change at multiple spatial and temporal scales. Less developed is the use of these spatial tools for planning, implementing, and evaluating ecosystem restoration projects and especially so in multifunctional landscapes. We use a case study approach in a multistakeholder tropical dryland restoration project to highlight the potential of remotely sensed products to quantitatively and economically guide often conflicting restoration priorities with stakeholder objectives. High‐resolution digital elevation models derived from an airborne remote sensing platform informed land managers tasked with endangered species restoration by guiding their efforts to highly suitable areas of the landscape where plant growth, performance, and survival should be greater. In turn, satellite‐based monitoring offered a temporal approach to broadly quantify vegetation fire risk in order to restrict fire promoting activities in dry landscapes most modified by fire promoting invasive grasses. Together, the delineation of high suitability areas for plant‐based restoration and low suitability areas for wildfire management ultimately releases moderate suitability land for alternative land uses deemed important in multistakeholder landscapes. We review the benefits of using remotely sensed data for restoration planning, and highlight the costs and benefits of various data sources.